Search

Multi-relational learning on knowledge graphs infers high-order relations among the entities across the graphs. This learning task can be solved by label propagation on the tensor product of the knowledge graphs to learn the high-order relations as a tensor. In this paper, we generalize a widely used label propagation model to the normalized tensor product graph, and propose an optimization formulation and the scalable Low-rank Tensor-based Label Propagation algorithm (LowrankTLP) to infer multi-relations for two learning tasks, hyperlink prediction and multiple graph alignment. The optimization formulation minimizes the upper bound of the noisy-tensor estimating error for multiple graph alignment, by learning with a subset of the eigen-pairs in the spectrum of the normalized tensor product graph. We also provide a data-dependent transductive Rademacher bound for binary hyperlink prediction. We accelerate LowrankTLP with parallel tensor computation which enables label propagation on a tensor product of 100 graphs each of size 1000 in less than half hour in the simulation. LowrankTLP was also applied to predicting the author-paper-venue hyperlinks in publication records, alignment of segmented regions across up to 26 CT-scan images and alignment of protein-protein interaction networks across multiple species. The experiments demonstrate that LowrankTLP indeed well approximates the original label propagation with better scalability and accuracy. [ABSTRACT FROM AUTHOR]

Bipartite graphs widely exist in real-world scenarios and model binary relations like host-website, author-paper, and user-product. In bipartite graphs, a butterfly (i.e., $2\times 2$ 2 × 2 bi-clique) is the smallest non-trivial cohesive structure and plays an important role in applications such as anomaly detection. Considerable efforts focus on counting butterflies in static bipartite graphs. However, they suffer from high time and space complexity when the bipartite graph of interest is given as a stream of edges. Although there are methods for approximately counting butterflies from bipartite graph streams, they suffer from either low accuracy or high time complexity. Therefore, it is still a challenge to accurately estimate butterfly counts from bipartite graph streams in a short time. To address this issue, we develop novel algorithms by exploiting the bipartite nature, which subtly integrates sampling and sketching techniques. We provide accurate estimators for butterfly counts and derive simple yet exact formulas for bounding their errors. We also conduct extensive experiments on a variety of real-world large bipartite graphs. Experimental results demonstrate that our algorithms are up to 20.0 times more accurate and up to 286.3 times faster than state-of-the-art methods under the same memory usage. [ABSTRACT FROM AUTHOR]

Subgraph queries are routinely used to search for entities in richly attributed graphs e.g., social networks and knowledge graphs. With little knowledge of underlying data, users often need to rewrite queries multiple times to reach desirable answers. Why-questions are studied to clarify missing or unexpected query results. This paper makes a first step to answer Why-questions for entity search in attributed graphs. We consider three common types of Why-questions: Why-not, Why, and Why-rank, which suggest query manipulations that are responsible for user-specified missing, unexpected, and undesirably ranked entities, respectively. (1) We approach a general query rewriting paradigm that suggests to identify desired entities that are specified by Why-questions. We introduce measures that characterize good query rewrites by incorporating both query editing cost and answer closeness. (2) While computing optimal query rewrites is intractable, we develop feasible algorithms, from approximation to fast heuristics, and provide query rewrites with (near) optimality guarantees whenever possible, for Why, Why-not and Why-rank questions. We further show that our results remain intact for Why questions that (1) request a single query rewrite to clarify multiple types of entities, and (2) variants such as Why-empty and Why-so-many, by providing the matching algorithms. Using real-world graphs, we experimentally verify that our algorithms are effective and feasible for large graphs. Our case study also verifies their application in e.g., knowledge exploration. [ABSTRACT FROM AUTHOR]

Driven by real-life applications in geo-social networks, we study the problem of computing radius-bounded $k$ k -cores (RB- $k$ k -cores) that aims to find communities satisfying both social and spatial constraints. In particular, the model $k$ k -core (i.e., the subgraph where each vertex has at least $k$ k neighbors) is used to ensure the social cohesiveness, and a radius-bounded circle is used to restrict the locations of users in an RB- $k$ k -core. We explore several algorithmic paradigms to compute RB- $k$ k -cores, including a triple-vertex-based paradigm, a binary-vertex-based paradigm, and a paradigm utilizing the concept of rotating circles. The rotating-circle-based paradigm is further enhanced by several pruning techniques to achieve better efficiency. In addition, to find representative RB- $k$ k -cores, we study the diversified radius-bounded $k$ k -core search problem, which finds $t$ t RB- $k$ k -cores to cover the most number of vertices. We first propose a baseline algorithm that identifies the distinctive RB- $k$ k -cores after finding all the RB- $k$ k -cores. Beyond this, we design algorithms that can efficiently maintain the top- $t$ t candidate RB- $k$ k -cores and also achieve a guaranteed approximation ratio. Experimental studies on both real and synthetic datasets demonstrate that our proposed techniques can efficiently compute (diversified) RB- $k$ k -cores. Moreover, our techniques can be used to compute the minimum-circle-bounded $k$ k -core and significantly outperform the existing techniques. [ABSTRACT FROM AUTHOR]

Social-media platforms have created new ways for citizens to stay informed and participate in public debates. However, to enable a healthy environment for information sharing, social deliberation, and opinion formation, citizens need to be exposed to sufficiently diverse viewpoints that challenge their assumptions, instead of being trapped inside filter bubbles. In this paper, we take a step in this direction and propose a novel approach to maximize the diversity of exposure in a social network. We formulate the problem in the context of information propagation, as a task of recommending a small number of news articles to selected users. In the proposed setting, we take into account content and user leanings, and the probability of further sharing an article. Our model allows to capture the balance between maximizing the spread of information and ensuring the exposure of users to diverse viewpoints. The resulting problem can be cast as maximizing a monotone and submodular function, subject to a matroid constraint on the allocation of articles to users. It is a challenging generalization of the influence-maximization problem. Yet, we are able to devise scalable approximation algorithms by introducing a novel extension to the notion of random reverse-reachable sets. We experimentally demonstrate the efficiency and scalability of our algorithm on several real-world datasets. [ABSTRACT FROM AUTHOR]

Cliques refer to subgraphs in an undirected graph such that vertices in each subgraph are pairwise adjacent. The maximum clique problem, to find the clique with most vertices in a given graph, has been extensively studied. Besides its theoretical value as an NP-hard problem, the maximum clique problem is known to have direct applications in various fields, such as community search in social networks and social media, team formation in expert networks, gene expression and motif discovery in bioinformatics and anomaly detection in complex networks, revealing the structure and function of networks. However, algorithms designed for the maximum clique problem are expensive to deal with real-world networks. In this paper, we first devise a randomized algorithm for the maximum clique problem. Different from previous algorithms that search from each vertex one after another, our approach RMC, for the randomized maximum clique problem, employs a binary search while maintaining a lower bound $\underline{\omega _c}$ ω c ̲ and an upper bound $\overline{\omega _c}$ ω c ¯ of $\omega (G)$ ω (G) . In each iteration, RMC attempts to find a $\omega _t$ ω t -clique where $\omega _t=\lfloor (\underline{\omega _c}+\overline{\omega _c})/2\rfloor$ ω t = ⌊ (ω c ̲ + ω c ¯) / 2 ⌋ . As finding $\omega _t$ ω t in each iteration is NP-complete, we extract a seed set $S$ S such that the problem of finding a $\omega _t$ ω t -clique in $G$ G is equivalent to finding a $\omega _t$ ω t -clique in $S$ S with probability guarantees ($\geq$ ≥ $ 1-n^{-c}$ 1 - n - c ). We propose a novel iterative algorithm to determine the maximum clique by searching a $k$ k -clique in $S$ S starting from $k=\underline{\omega _c}+1$ k = ω c ̲ + 1 until $S$ S becomes $\lbrace \rbrace$ { } , when more iterations benefit marginally. Due to the potential inconsistency of maximum clique algorithms, we study the problem of maximum clique enumeration and propose an efficient algorithm RMCE to enumerate all maximum cliques in a given graph. As confirmed by the experiments, both RMC and RMCE are much more efficient and robust than previous solutions, RMC can always find the exact maximum clique, and RMCE can always enumerate all maximum cliques in a given graph. [ABSTRACT FROM AUTHOR]

Reorganizing bus frequencies to cater for the actual travel demands can significantly save the cost of the public transport system. Many, if not all, previous studies formulate this as a bus frequency optimization problem that tries to minimize passengers’ average waiting time. On the other hand, many investigations have confirmed that the user satisfaction drops faster as the waiting time increases. Consequently, this paper studies the bus frequency optimization problem considering the user satisfaction. Specifically, for the first time to our best knowledge, we study how to schedule the buses such that the total number of passengers who could receive their bus services within the waiting time threshold can be maximized. We propose two variants of the problem, FAST and FASTCO, to cater for different application needs and prove that both are NP-hard. To solve FAST effectively and efficiently, we first present an index-based $(1-1/e)$ (1 - 1 / e) -approximation algorithm. By exploiting the locality property of routes in a bus network, we further propose a partition-based greedy method that achieves a $(1-\rho)(1-1/e)$ (1 - ρ) (1 - 1 / e) approximation ratio. Then we propose a progressive partition-based greedy method to further boost the efficiency while achieving a $(1-\rho)(1-1/e-\varepsilon)$ (1 - ρ) (1 - 1 / e - ɛ) approximation ratio. For the FASTCO problem, two greedy-based heuristic methods are proposed. Experiments on a real city-wide bus dataset in Singapore have been conducted to verify the efficiency, effectiveness, and scalability of our methods in addressing FAST and FASTCO respectively. [ABSTRACT FROM AUTHOR]

Many time series data mining problems can be solved with repeated use of distance measure. Examples of such tasks include similarity search, clustering, classification, anomaly detection and segmentation. For over two decades it has been known that the Dynamic Time Warping (DTW) distance measure is the best measure to use for most tasks, in most domains. Because the classic DTW algorithm has quadratic time complexity, many ideas have been introduced to reduce its amortized time, or to quickly approximate it. One of the most cited approximate approaches is FastDTW. The FastDTW algorithm has well over a thousand citations and has been explicitly used in several hundred research efforts. In this work, we make a surprising claim. In any realistic data mining application, the approximate FastDTW is much slower than the exact DTW. This fact clearly has implications for the community that uses this algorithm: allowing it to address much larger datasets, get exact results, and do so in less time. [ABSTRACT FROM AUTHOR]

Since inaccuracies commonly exist in many applications, data uncertainty has become an important problem in database systems. To deal with data uncertainty, probabilistic databases can be used to store uncertain data, and querying facilities are provided to yield answers with confidence. However, the results from a query or mining process may not be reliable when the uncertainty propagates in the systems. In this paper, we leverage the power of crowdsourcing by designing a set of Human Intelligence Tasks, or HITs in short, to ask a crowd to improve the quality of uncertain data. In particular, we consider crowds consists of workers with diverse accuracy rates when answering the HITs. We design solutions to maximize the data quality with minimal number of HITs. There are two obstacles for this non-trivial optimization, which lead to very high computational cost for selecting the optimal set of HITs. First, members of a crowd may return incorrect answers with different probabilities. Second, the HITs decomposed from uncertain data are often correlated. We have addressed these challenges in this paper by designing an effective approximation algorithm and an efficient heuristic solution, especially for crowds with diverse individual accuracy rates. To further improve the efficiency, we derive tight lower and upper bounds for effective filtering and estimation. Extensive experiments on both a simulated crowd and a real crowdsourcing platform are conducted to evaluate our solutions. [ABSTRACT FROM AUTHOR]

Existing research on finding social groups mostly focuses on dense subgraphs in social networks. However, finding socially tenuous groups also has many important applications. In this article, we introduce the notion of $k$ k -triangles to measure the tenuity of a group. We then formulate a new research problem, Minimum $k$ k -Triangle Disconnected Group with No-Pair Constraint (MkTG), to find a socially tenuous group from the online social network. We prove that MkTG is NP-hard and inapproximable within any ratio. Two algorithms, namely TERA and TERA-ADV, are designed for solving MkTG effectively and efficiently. Further, we examine the MkTG problem on tree-based social networks, due to their structural resemblance with corporate social networks built upon the supervision relation. Accordingly, we devise an efficient algorithm, namely Tenuity Maximization for Trees (TMT), to obtain the optimal solution in polynomial time. In addition, we study a more general version of MkTG, named Generalized Minimum $k$ k -Triangle Disconnected Group without No-Pair Constraint (MkTG-G). We formulate MkTG-G, analyze its inapproximability, and propose a randomized approximation algorithm, named Randomized Ranking with Limited Neighborhood Participation (RLNP). Experimental results on real datasets manifest that the proposed algorithms outperform the baselines in terms of both efficiency and solution quality. [ABSTRACT FROM AUTHOR]

Static and dynamic clustering algorithms are a fundamental tool in any machine learning library. Most of the efforts in developing dynamic machine learning and data mining algorithms have been focusing on the sliding window model or more simplistic models. However, in many real-world applications one might need to deal with arbitrary deletions and insertions. For example, one might need to remove data items that are not necessarily the oldest ones, because they have been flagged as containing inappropriate content or due to privacy concerns. Clustering trajectory data might also require to deal with more general update operations. We develop a $(2+\epsilon)$ (2 + ε) -approximation algorithm for the $k$ k -center clustering problem with “small” amortized cost under the fully dynamic adversarial model. In such a model, points can be added or removed arbitrarily, provided that the adversary does not have access to the random choices of our algorithm. The amortized cost of our algorithm is poly-logarithmic when the ratio between the maximum and minimum distance between any two points in input is bounded by a polynomial, while $k$ k and $\epsilon$ ε are constant. Furthermore, we significantly improve the memory requirement of our fully dynamic algorithm, although at the cost of a worse approximation ratio of $4 +\epsilon$ 4 + ε . Our theoretical results are complemented with an extensive experimental evaluation on dynamic data from Twitter, Flickr, as well as trajectory data, demonstrating the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

We present new algorithms for $k$ k -means clustering on a data stream with a focus on providing fast responses to clustering queries. Compared to the state-of-the-art, our algorithms provide substantial improvements in the query time for cluster-center queries while retaining the desirable properties of provably small approximation error and low space usage. Our proposed clustering algorithms systematically reuse the “coresets” (summaries of data) computed for recent queries in answering the current clustering query, a novel technique which we refer to as coreset caching. We also present an algorithm called OnlineCC that integrates the coreset caching idea with a simple sequential streaming $k$ k -means algorithm. In practice, OnlineCC algorithm can provide constant query time. We present both theoretical analysis and detailed experiments demonstrating the correctness, accuracy, and efficiency of all our proposed clustering algorithms. [ABSTRACT FROM AUTHOR]

Online social networks have become major battlegrounds for political campaigns, viral marketing, and the dissemination of news. As a consequence, “bad actors” are increasingly exploiting these platforms, which is a key challenge for their administrators, businesses and society in general. The spread of fake news is a classical example of the abuse of social networks by these bad actors. While some have advocated for stricter policies to control the spread of misinformation in social networks, this often happens in detriment of their democratic and organic structure. In this paper, we aim to limit the influence of a target group in a social network via the removal of a few users/links. We formulate the influence limitation problem in a data-driven fashion, by taking into account past propagation traces. More specifically, our algorithms find critical edges to be removed in order to decrease the influence of a target group based on past data. The idea is to control the diffusion processes while minimizing the amount of disturbance in the network structure. Moreover, we consider two types of constraints over edge removals, a budget constraint and also a, more general, set of matroid constraints. These problems lead to interesting challenges in terms of algorithm design. For instance, we are able to show that influence limitation is APX-hard and propose deterministic and probabilistic approximation algorithms for the budgeted and the matroid version of the problem, respectively. Experiments show that the proposed approaches outperform several baselines. [ABSTRACT FROM AUTHOR]

Problems in scientific computing, such as distributing large sparse matrix operations, have analogous formulations as hypergraph partitioning problems. A hypergraph is a generalization of a traditional graph wherein “hyperedges” may connect any number of nodes. As a result, hypergraph partitioning is an NP-Hard problem to both solve or approximate. State-of-the-art algorithms that solve this problem follow the multilevel paradigm, which begins by iteratively “coarsening” the input hypergraph to smaller problem instances that share key structural features. Once identifying an approximate problem that is small enough to be solved directly, that solution can be interpolated and refined to the original problem. While this strategy represents an excellent trade off between quality and running time, it is sensitive to coarsening strategy. In this work we propose using graph embeddings of the initial hypergraph in order to ensure that coarsened problem instances retrain key structural features. Our approach prioritizes coarsening within self-similar regions within the input graph, and leads to significantly improved solution quality across a range of considered hypergraphs. Reproducibility: All source code, plots and experimental data are available at https://sybrandt.com/2019/partition. [ABSTRACT FROM AUTHOR]

Influence maximization (IM) aims at maximizing the spread of influence by offering discounts to influential users (called seeding). In many applications, due to user’s privacy concern, overwhelming network scale etc., it is hard to target any user in the network as one wishes. Instead, only a small subset of users is initially accessible. Such access limitation would significantly impair the influence spread, since IM often relies on seeding high degree users, which are particularly rare in such a small subset due to the power-law structure of social networks. In this paper, we attempt to solve the limited IM in real-world scenarios by the adaptive approach with seeding and diffusion uncertainty considered. Specifically, we consider fine-grained discounts and assume users accept the discount probabilistically. The diffusion process is depicted by the independent cascade model. To overcome the access limitation, we prove the set-wise friendship paradox (FP) phenomenon that neighbors have higher degree in expectation, and propose a two-stage seeding model with the FP embedded, where neighbors are seeded. On this basis, for comparison we formulate the non-adaptive case and adaptive case, both proven to be NP-hard. In the non-adaptive case, discounts are allocated to users all at once. We show the monotonicity of influence spread w.r.t. discount allocation and design a two-stage coordinate descent framework to decide the discount allocation. In the adaptive case, users are sequentially seeded based on observations of existing seeding and diffusion results. We prove the adaptive submodularity and submodularity of the influence spread function in two stages. Then, a series of adaptive greedy algorithms are proposed with constant approximation ratio. Extensive experiments on real-world datasets show that our adaptive algorithms achieve larger influence spread than non-adaptive and other adaptive algorithms (up to a maximum of 116 percent). [ABSTRACT FROM AUTHOR]

Graph matching, the problem of aligning a pair of graphs so as to minimize their edge disagreements, has received widespread attention owing to its broad spectrum of applications in data science. As the problem is NP–hard in the worst-case, a variety of approximation algorithms have been proposed for obtaining high quality, suboptimal solutions. In this article, we approach the task of designing an efficient polynomial-time approximation algorithm for graph matching from a previously unconsidered perspective. Our key result is that graph matching can be formulated as maximizing a monotone, supermodular set function subject to matroid intersection constraints. We leverage this fact to apply a discrete optimization variant of the minorization-maximization algorithm which exploits supermodularity of the objective function to iteratively construct and maximize a sequence of global lower bounds on the objective. At each step, we solve a maximum weight matching problem in a bipartite graph. Differing from prior approaches, the algorithm exploits the combinatorial structure inherent in the problem to generate a sequence of iterates featuring monotonically non-decreasing objective value while always adhering to the combinatorial matching constraints. Experiments on real-world data demonstrate the empirical effectiveness of the algorithm relative to the prevailing state-of-the-art. [ABSTRACT FROM AUTHOR]

Shortest path query in road network is pervasive in various location-based services nowadays. As the business expands, the scalability issue becomes severer and more servers are deployed to cope with it. Moreover, as the traffic condition keeps changing over time, the existing index-based approaches can hardly adapt to the real-life dynamic environment. Therefore, batch shortest path algorithms have been proposed recently to answer a set of queries together using shareable computation. Besides, they can also work in a highly dynamic environment as no index is needed. However, the existing batch algorithms either assume the batch queries are finely decomposed or just process them without differentiation, resulting in poor query efficiency. In this work, we assume the traffic condition is stable over a short period and treat the issued queries within that period as a stream of query sets. Specifically, we first propose three query set decomposition methods to cluster one query set into multiple query subsets: Zigzag that considers the 1-N shared computation; Co-Clustering that considers the source and target's spatial locality; and Search-Space-Aware that further incorporates search space estimation. After that, we propose two batch algorithms that take advantage of the previously decomposed query sets for efficient query answering: R2R that finds a set of approximate shortest paths from one region to another with bounded error; and Local Cache that improves the existing Global Cache with higher cache hit ratio. Finally, we design three efficient stream processing methods for intra-batch shared computation. The experiments on a large real-world query sets verify the effectiveness and efficiency of our decomposition methods compared with the state-of-the-art batch algorithms. [ABSTRACT FROM AUTHOR]

Despite being commonly used in big-data analytics; the outcome of dimensionality reduction remains a black-box to most of its users. Understanding the quality of a low-dimensional embedding is important as not only it enables trust in the transformed data, but it can also help to select the most appropriate dimensionality reduction algorithm in a given scenario. As existing research primarily focuses on the visual exploration of embeddings, there is still a need for enhancing interpretability of such algorithms. To bridge this gap, we propose two novel interactive explanation techniques for low-dimensional embeddings obtained from any dimensionality reduction algorithm. The first technique LAPS produces a local approximation of the neighborhood structure to generate interpretable explanations on the preserved locality for a single instance. The second method GAPS explains the retained global structure of a high-dimensional dataset in its embedding, by combining non-redundant local-approximations from a coarse discretization of the projection space. We demonstrate the applicability of the proposed techniques using 16 real-life tabular, text, image, and audio datasets. Our extensive experimental evaluation shows the utility of the proposed techniques in interpreting the quality of low-dimensional embeddings, as well as with selecting the most suitable dimensionality reduction algorithm for any given dataset. [ABSTRACT FROM AUTHOR]

Density Peak Clustering (DPC), a popular density-based clustering approach, has received considerable attention from the research community primarily due to its simplicity and fewer-parameter requirement. However, the resultant clusters obtained using DPC are influenced by the sensitive parameter $d_c$ d c , which depends on data distribution and requirements of different users. Besides, the original DPC algorithm requires visiting a large number of objects, making it slow. To this end, this paper investigates index-based solutions for DPC. Specifically, we propose two list-based index methods viz. (i) a simple List Index, and (ii) an advanced Cumulative Histogram Index. Efficient query algorithms are proposed for these indices which significantly avoids irrelevant comparisons at the cost of space. For memory-constrained systems, we further introduce an approximate solution to the above indices which allows substantial reduction in the space cost, provided that slight inaccuracies are admissible. Furthermore, owing to considerably lower memory requirements of existing tree-based index structures, we also present effective pruning techniques and efficient query algorithms to support DPC using the popular Quadtree Index and R-tree Index. Finally, we practically evaluate all the above indices and present the findings and results, obtained from a set of extensive experiments on six synthetic and real datasets. The experimental insights obtained can help to guide in selecting a befitting index. [ABSTRACT FROM AUTHOR]

The maximum clique problem (MCP) has various applications to reveal the structure and function of graphs. Graphs are constantly updated in the real life. However, no algorithm is specifically designed for dynamic graph. Although MCP in dynamic graphs can be solved by simply invoking a state-of-the-art static approach, such as PMC, when the graph is updated, such an approach of simply re-calculating from scratch is inefficient. The key issue with MCP algorithm is to find a large clique, namely a seed, as fast as possible. Thus, search space can be pruned based on the seed. Size of the seed greedily found by PMC cannot be guaranteed, as it fluctuates considerably. Moreover, the time required to find a seed under PMC is up to $O(| E| \cdot \Delta (G))$ O (| E | · Δ (G)) , where $\Delta (G)$ Δ (G) is the highest degree in G. In this article, we intend to find a sizable seed by updating the previous maximum clique with the incident vertices of the inserted/deleted edge. Size of the seed now is guaranteed to be no less than $\omega (G^{\prime})\; - \;1$ ω (G ') - 1 , where $\omega (G^{\prime})$ ω (G ') is the size of the maximum clique on the updated graph. Moreover, the seed can be found in a time complexity of $O(\Delta (G)^{2})$ O (Δ (G) 2) . Two other crucial issues related to the MCP in dynamic graphs are refreshing rate and refreshing overhead. After a tight upper bound is imposed on $\omega (G^{\prime})$ ω (G ') , the necessity of refreshing is evaluated by comparing the seed with its largest challenger, then unnecessary refreshing is wiped out effectively. The size of the largest challenger is judiciously estimated using a lazy growth strategy. Subsequently, the search space in refreshing is confined on a much smaller subgraph using a local refreshing strategy. Extensive experiments indicate that the proposed approach outperforms the baseline algorithm by approximately one order of magnitude. [ABSTRACT FROM AUTHOR]

The $K$ K -means algorithm is one of the most popular clustering methods. However, it is a well-known fact that its performance, in terms of quality of the obtained solution and computational load, highly depends upon its initialization phase. For this reason, different initialization techniques have been developed throughout the years to enable its fast convergence to competitive solutions. In this sense, it is common practice to re-start the $K$ K -means algorithm several times via one of these techniques and keep the solution with the lowest error. Unfortunately, such a choice is still likely to be a poor approximation of the optimal set of centroids. In this article, we introduce a cheap Split-Merge step that can be used to re-start the $K$ K -means algorithm after reaching a fixed point. Under some settings, one can show that this approach reduces the error of the given fixed point without requiring any further iteration of the $K$ K -means algorithm. Moreover, experimental results show that this strategy is able to generate approximations with an associated error that is hard to reach for different multi-start methods, such as multi-start Forgy $K$ K -means, $K$ K -means++ and Hartigan $K$ K -means, while also computing a lower amount of distances than the previous algorithms. [ABSTRACT FROM AUTHOR]

Many successful methods have been proposed for learning low-dimensional representations on large-scale networks, while almost all existing methods are designed in inseparable processes, learning embeddings for entire networks even when only a small proportion of nodes are of interest. This leads to great inconvenience, especially on large-scale or dynamic networks, where these methods become almost impossible to implement. In this paper, we formalize the problem of separated matrix factorization, based on which we elaborate a novel objective function that preserves both local and global information. We compare our SMF framework with approximate SVD algorithms and demonstrate SMF can capture more information when factorizing a given matrix. We further propose SepNE, a simple and flexible network embedding algorithm which independently learns representations for different subsets of nodes in separated processes. By implementing separability, our algorithm reduces the redundant efforts to embed irrelevant nodes, yielding scalability to large networks. To further incorporate complex information into SepNE, we discuss several methods that can be used to leverage high-order proximities in large networks. We demonstrate the effectiveness of SepNE on several real-world networks with different scales and subjects. With comparable accuracy, our approach significantly outperforms state-of-the-art baselines in running times on large networks. [ABSTRACT FROM AUTHOR]

Dual Coordinate Descent (DCD) is one of the most popular optimization methods. The parallelization of DCD is difficult, as DCD is sequential in nature. As such, simultaneously running multiple DCD threads on batches of data elements causes result inaccuracy and slow convergence, due to the concurrent updates of multiple coordinates. Some parallelization methods adopt separable approximate functions that depend on the degree of parallelism. Such dependencies result in both poor scalability and slow-convergence. To address these challenges, in this paper we present a new parallel primal-dual algorithm for DCD, called PPD. In PPD, the block data distribution is utilized to obtain a new approximate function that is independent from the parallelism. Moreover, PPD is designed with a novel primal-dual acceleration scheme to approach the optimal solution closely and quickly. We demonstrate the advantages of PPD in terms of scalability and efficiency through experiments. [ABSTRACT FROM AUTHOR]

Feature reduction is an important aspect of Big Data analytics on today’s ever-larger datasets. Rough sets are a classical method widely applied in attribute reduction. Most rough set algorithms use the priori domain knowledge of a dataset to process continuous attributes through using a membership function. Neighborhood rough sets (NRS) replace the membership function with the concept of neighborhoods, allowing NRS to handle scenarios where no a priori knowledge is available. However, the neighborhood radius of each object in NRS is fixed, and the optimization of the radius depends on grid searching. This diminishes both the efficiency and effectiveness, leading to a time complexity of not lower than $O(N^2)$ O (N 2) . To resolve these limitations, granular ball neighborhood rough sets (GBNRS), a novel NRS method with time complexity $O(N)$ O (N) , is proposed. GBNRS adaptively generates a different neighborhood for each object, resulting in greater generality and flexibility in comparison to standard NRS methods. GBNRS is compared with the current state-of-the-art NRS method, FARNeMF, and find that GBNRS obtains both higher performance and higher classification accuracy on public benchmark datasets. All code has been released in the open source GBNRS library at http://www.cquptshuyinxia.com/GBNRS.html. [ABSTRACT FROM AUTHOR]

Stego-images are often contaminated by interchannel noise or active noise attack when communicating on the Web. And it is challenging to restore embedded image from corrupted stego-image. This paper studies a kNN-bit approximation algorithm to remove noises in embedded image. The proposed algorithm distinguishes reliable bits from extracted bits, and estimates pixel values by keeping reliable bits unchanged and correcting unreliable bits. Specifically, the 8th (highest) unreliable bit of a pixel can be approximated with its nearest neighbor pixels. And then, if an unreliable bit locates at any one of the $5^{th}\sim 7^{th}$ 5 t h ∼ 7 t h bits of a pixel, it is adjusted with two nearest neighbors of the pixel, where the pixel is in-between these two nearest neighbors. Finally, for other unreliable bits, each one is approximated by the maximum and minimum possible values of nearest neighbors of its pixel. We conduct experiments for illustrating the efficiency, and demonstrate that the proposed algorithm can recover the embedded images with good visual quality from corrupted stego-images. [ABSTRACT FROM AUTHOR]

Besides the traditional cartographic data sources, spatial information can also be derived from location-based sources. However, even though different location-based sources refer to the same physical world, each one has only partial coverage of the spatial entities, describe them with different attributes, and sometimes provide contradicting information. Hence, we introduce the spatial entity linkage problem, which finds which pairs of spatial entities belong to the same physical spatial entity. Our proposed solution (QuadSky) starts with a time-efficient spatial blocking technique (QuadFlex), compares pairwise the spatial entities in the same block, ranks the pairs using Pareto optimality with the SkyRank algorithm, and finally, classifies the pairs with our novel SkyEx-* family of algorithms that yield 0.85 precision and 0.85 recall for a manually labeled dataset of 1,500 pairs and 0.87 precision and 0.6 recall for a semi-manually labeled dataset of 777,452 pairs. Moreover, we provide a theoretical guarantee and formalize the SkyEx-FES algorithm that explores only 27 percent of the skylines without any loss in F-measure. Furthermore, our fully unsupervised algorithm SkyEx-D approximates the optimal result with an F-measure loss of just 0.01. Finally, QuadSky provides the best trade-off between precision and recall, and the best F-measure compared to the existing baselines and clustering techniques, and approximates the results of supervised learning solutions. [ABSTRACT FROM AUTHOR]

Real graphs often contain edge and node weights, representing, for instance, penalty, distance or uncertainty. We study the problem of keyword search over weighted node-labeled graphs, in which a query consists of a set of keywords and an answer is a subgraph whose nodes contain the keywords. We evaluate answers using three ranking strategies: optimizing edge weights, optimizing node weights, and a bi-objective combination of both node and edge weights. We prove that optimizing node weights and the bi-objective function are NP-hard. We propose an algorithm that optimizes edge weights and has an approximation ratio of two for the unique node enumeration paradigm. To optimize node weights and the bi-objective function, we propose transformations that distribute node weights onto the edges. We then prove that our transformations allow our algorithm to also optimize node weights and the bi-objective function with the same approximation ratio of two. Notably, the proposed transformations are compatible with existing algorithms that only optimize edge weights. We empirically show that in many natural examples, incorporating node weights (both keyword holders and middle nodes) produces more relevant answers than ranking methods based only on edge weights. Extensive experiments over real-life datasets verify the effectiveness and efficiency of our solution. [ABSTRACT FROM AUTHOR]

With the rapid development of mobile technology, nowadays, people spend a large amount of time on mobile devices. The locations and contexts of users are easily accessed by mobile advertising brokers, and the brokers can send customers related location-based advertisements. In this paper, we consider an important location-based advertising problem, namely maximum utility advertisement assignment (MUAA) problem, with the estimation of the interests of customers and the contexts of the vendors, we want to maximize the overall utility of ads by determining the ads sent to each customer subject to the constraints of the capacities of customers, the distance ranges and the budgets of vendors. We prove that the MUAA problem is NP-hard and intractable. Thus, we propose one offline approach, namely the ${\sf reconciliation\ approach}$ reconciliation approach , which has an approximation ratio of $(1-\epsilon)\cdot \theta$ (1 - ε) · θ . In addition, we also address the online scenario, in which customers arrive in a streaming fashion, with one novel online algorithm, namely the ${\sf online\ adaptive\ factor-aware\ approach}$ online adaptive factor - aware approach , which has a competitive ratio (compared to the optimal solution of the offline scenario) of $\frac{\ln (g)+1}{\theta }$ ln (g) + 1 θ , $g>e$ g > e , where $e$ e is the base of the natural logarithm. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed approaches over both real and synthetic datasets. [ABSTRACT FROM AUTHOR]

Piecewise Linear Approximation (PLA) is one of the most widely used approaches for representing a time series with a set of approximated line segments. With this compressed form of representation, many large complicated time series can be efficiently stored, transmitted and analyzed. In this article, with the introduced concept of “semi-connection” that allowing two representation lines to be connected at a point between two consecutive time stamps, we propose a new optimal linear-time PLA algorithm SemiOptConnAlg for generating the least number of semi-connected line segments with guaranteed maximum error bound. With extended experimental tests, we demonstrate that the proposed algorithm is very efficient in execution time and achieves better performances than the state-of-art solutions. [ABSTRACT FROM AUTHOR]

The social presence theory in social psychology suggests that computer-mediated online interactions are inferior to face-to-face, in-person interactions. Thus, it's important to organize social activities for online social network users to meet in person. In this paper, we consider the scenarios of organizing in person friend-making social activities via online social networks (OSNs) and formulate a new research problem, namely, Hop-bounded Maximum Group Friending (HMGF), that takes into consideration both existing friendships and the likelihood of new friend making in organization of the targeted in person friend-making social activities. To find a set of attendees for such social activities, HMGF is unique and challenging due to the interplay of the group size, the constraint on existing friendships, and the objective of maximizing the likelihood of friend making. We prove that HMGF is NP-Hard, and there exists no approximation algorithm for it unless $P=NP$ P = N P . We also provide an Integer Linear Programming (ILP) formulation for the HMGF problem. The ILP formulation, which can be solved efficiently by a commercial solver to obtain the optimal solution for small HMGF instances, acts as a baseline approach for comparison in the evaluation of the proposed algorithm. We further propose an error-bounded approximation algorithm, MaxGF, to efficiently obtain the solutions very close to the optimal solutions. To boost the performance, we devise two graph-theoretical pruning strategies, namely Neighbor Pruning and Core Pruning, which can effectively avoid redundant graph explorations to improve the performance of HMGF. We also study HMGF on a class of special graphs, threshold graphs, which have properties very similar to many online social networks. We prove that MaxGF can obtain the optimal solution to HMGF on threshold graphs in polynomial time. We conduct a user study to validate our problem formulation and perform extensive experiments on real datasets to demonstrate the efficiency and effectiveness of our proposed algorithm. The experimental results manifest that our proposed algorithms outperform the baselines, including the ILP formulation. [ABSTRACT FROM AUTHOR]

We suggest a new optimization technique for minimizing the sum $\sum _{i=1}^n g_i(x)$ ∑ i = 1 n g i (x) of $n$ n non-convex real functions that satisfy a property that we call piecewise log-Lipschitz. This is by forging links between techniques in computational geometry, combinatorics and convex optimization. As an example application, we provide the first constant-factor approximation algorithms whose running-times are polynomial in $n$ n for the fundamental problem of Points-to-Lines alignment: Given $n$ n points $p_1,\ldots,p_n$ p 1 ,... , p n and $n$ n lines $\ell _1,\ldots,\ell _n$ ℓ 1 ,... , ℓ n on the plane and $z>0$ z > 0 , compute the matching $\pi :[n]\to [n]$ π : [ n ] → [ n ] and alignment (rotation matrix $R$ R and translation vector $t$ t ) that minimize the sum of euclidean distances $\sum _{i=1}^n \mathrm{dist}(Rp_i-t,\ell _{\pi (i)})^z$ ∑ i = 1 n dist (R p i - t , ℓ π (i)) z between each point to its corresponding line. This problem is non-trivial even if $z=1$ z = 1 and the matching $\pi$ π is given. If $\pi$ π is given, our algorithms run in $O(n^3)$ O (n 3) time, and even near-linear in $n$ n using core-sets that support: streaming, dynamic, and distributed parallel computations in poly-logarithmic update time. Generalizations for handling e.g., outliers or pseudo-distances such as $M$ M -estimators for the problem are also provided. Experimental results and open source code show that our algorithms improve existing heuristics also in practice. A companion demonstration video in the context of Augmented Reality shows how such algorithms may be used in real-time systems. [ABSTRACT FROM AUTHOR]

Hierarchical Dirichlet Process (HDP) has attracted much attention in the research community of natural language processing. Given a corpus, HDP is able to determine the number of topics automatically, possessing an important feature dubbed nonparametric that overcomes the challenging issue of manually specifying a suitable topic number in parametric topic models, such as Latent Dirichlet Allocation (LDA). Nevertheless, HDP requires a much higher computational cost than LDA for parameter estimation. By taking the advantage of multi-threading, a parallel Gibbs sampling algorithm is proposed to estimate parameters for HDP based on the equivalence between HDP and Gamma-Gamma Poisson Process (G2PP) in terms of the generative process. Unfortunately, the above parallel Gibbs sampling algorithm requires to apply the finite approximation on the number of topics manually (i.e., predefine the topic number), thus can not retain the nonparametric feature of HDP. Another drawback of the above models is the lack of capturing the semantic dependencies between words, because the topic assignment of words is independent with each other. Although some works have been done in phrase-based topic modelling, these existing methods are still limited by either enforcing the entire phrase to share a common topic or requiring much complex and time-consuming phrase mining methods. In this paper, we aim to develop a copula guided parallel Gibbs sampling algorithm for HDP which can adjust the number of topics dynamically and capture the latent semantic dependencies between words that compose a coherent segment. Extensive experiments on real-world datasets indicate that our method achieves low perplexities and high topic coherence scores with a small time cost. In addition, we validate the effectiveness of our method on the modelling of word semantic dependencies by comparing the extracted topical phrases with those learned by state-of-the-art phrase-based baselines. [ABSTRACT FROM AUTHOR]

Measuring similarity among data objects is important in data analysis and mining. SimRank is a popular link-based similarity measurement among nodes in a graph. To compute the all-pairs SimRank matrix accurately, iterative methods are usually used. For static graphs, current iterative solutions are not efficient enough, both in time and space, due to the unnecessary cost and storage by the nature of iterative updating. For dynamic graphs, all current incremental solutions for updating the SimRank matrix are based on an approximated SimRank definition, and thus have no accuracy guarantee. In this paper, we propose a novel local push based algorithm for computing and tracking all-pairs SimRank. Furthermore, we develop an iterative parallel two-step framework for local push to take advantage of modern hardwares with multicore CPUs. We show that our algorithms outperform the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Aggregate nearest neighbor (Ann) query in both the euclidean space and road networks has been extensively studied, and the flexible aggregate nearest neighbor (Fann) problem further generalizes Ann by introducing an extra flexibility parameter $\phi$ ϕ that ranges in $(0, 1]$ (0 , 1 ] . In this article, we focus on Fann on road networks, denoted as Fann $_\mathcal {R}$ R , and its keyword-aware variant, denoted as KFann $_\mathcal {R}$ R . To solve these problems, we propose a series of universal (i.e., suitable for both max and sum) algorithms, including a Dijkstra-based algorithm that enumerates $P$ P instead of $\phi |Q|$ ϕ | Q | -combinations of $Q$ Q , a queue-based approach that processes data points from-near-to-far, and a framework that combines incremental euclidean restriction (IER) and $k$ k NN. We also propose a specific exact solution to max-Fann $_\mathcal {R}$ R and a constant-factor ratio approximate solution to sum-Fann $_\mathcal {R}$ R . These specific algorithms are easy to implement and can achieve excellent performance in some scenarios. Besides, we further extend this problem to top- $k$ k and multiple Fann $_\mathcal {R}$ R (resp., KFann $_\mathcal {R}$ R ) queries. We conduct a comprehensive experimental evaluation for the proposed algorithms on real datasets to demonstrate their superior efficiency and high quality. [ABSTRACT FROM AUTHOR]

Given a geo-textual dataset $\mathbb {O}$ O , a set $\varphi$ φ of keywords, a query object $q \in \mathbb {O}$ q ∈ O , and an integer $k$ k , a reverse top- $k$ k keyword-based location query returns the influence zone $\mathcal {R}$ R of $q$ q such that $q$ q belongs to the result of a top- $k$ k spatial keyword query with query keywords $\varphi$ φ and any location in $\mathcal {R}$ R as arguments. For a query object $q$ q , the influence zone of $q$ q varies for different keywords $\varphi$ φ . Users may be interested in identifying the maximum influence zone of the query object. To this end, in this paper, we study the problem called MaxiZone that finds the keyword set maximizing the influence zone of a specified query object. The MaxiZone problem has many real-life applications, e.g., a business owner would like to identify the maximum influence zone so as to attract as many customers as possible. A straightforward way to tackle the MaxiZone problem is to compute the influence zone for every candidate keyword set. Obviously, this is infeasible if there are a large number of candidate keyword sets. We propose a more efficient index-centric algorithm together with a series of optimizations as well as a sampling-based algorithm, to facilitate the query processing. Moreover, we extend the proposed algorithms to address a variant of MaxiZone problem called $\tau$ τ -MaxiZone problem, which finds top- $\tau$ τ keyword sets having the maximum influence zones. Extensive empirical study using real-world datasets demonstrates the effectiveness and efficiency of our proposed algorithms. [ABSTRACT FROM AUTHOR]

Ranking on graphs is a centerpiece in many high-impact application domains, such as information retrieval, recommender systems, team management, neuroscience and many more. PageRank, along with many of its variants, is widely used across these application domains thanks to its mathematical elegance and the superior performance. Although PageRank and its variants are effective in ranking nodes on graphs, they often lack an efficient and effective way to audit the ranking results in terms of the input graph structure, e.g., which node or edge in the graph contributes most to the top-1 ranked node; which subgraph plays a crucial role in generating the overall ranking result? In this paper, we propose to audit graph ranking by finding the influential graph elements (e.g., edges, nodes, attributes, and subgraphs) regarding their impact on the ranking results. First, we formulate graph ranking auditing problem as quantifying the influence of graph elements on the ranking results. Second, we show that our formulation can be applied to a variety of graph structures. Third, we propose effective and efficient algorithms to find the top- $k$ k influential edges/nodes/subgraph. Finally, we perform extensive empirical evaluations on real-world datasets to demonstrate that the proposed methods (Aurora) provide intuitive auditing results with linear scalability. [ABSTRACT FROM AUTHOR]

With the increase of mobile devices, Mobile Crowdsensing (MCS) has become an efficient way to ubiquitously sense and collect environment data. Comparing to traditional sensor networks, MCS has a vital advantage that workers play an active role in collecting and sensing data. However, due to the openness of MCS, workers and sensors are of different qualities. Low quality sensors and workers may yield noisy data or even inaccurate data. Which gives the importance of inferring the quality of workers and sensors and seeking a valid task assignment with enough total qualities for MCS. To solve the problem, we adopt truth inference methods to iteratively infer the truth and qualities. Based on the quality inference, this paper proposes a task assignment problem called quality-bounded task assignment with redundancy constraint (QTAR). Different from traditional task assignment problem, redundancy constraint is added to satisfy the preliminaries of truth inference, which requires that each task should be assigned a certain or more amount of workers. We prove that QTAR is NP-complete and propose a $ (2+\epsilon)$ (2 + ε) - approximation algorithm for QTAR, called QTA. Finally, experiments are conducted on both synthesis data and real dataset. The results of the experiments prove the efficiency and effectiveness of our algorithms. [ABSTRACT FROM AUTHOR]

With the advent of big data and data lakes, data are often integrated from multiple sources. Such integrated data are often of poor quality, due to inconsistencies, errors, and so forth. One way to check the quality of data is to infer functional dependencies (fds). However, in many modern applications it might be necessary to extract properties and relationships that are not captured through fds, due to the necessity to admit exceptions, or to consider similarity rather than equality of data values. Relaxed fds (rfds) have been introduced to meet these needs, but their discovery from data adds further complexity to an already complex problem, also due to the necessity of specifying similarity and validity thresholds. We propose Domino, a new discovery algorithm for rfds that exploits the concept of dominance in order to derive similarity thresholds of attribute values while inferring rfds. An experimental evaluation on real datasets demonstrates the discovery performance and the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Coupled Matrix Tensor Factorization (CMTF) facilitates the integration and analysis of multiple data sources and helps discover meaningful information. Nonnegative CMTF (N-CMTF) has been employed in many applications for identifying latent patterns, prediction, and recommendation. However, due to the added complexity with coupling between tensor and matrix data, existing N-CMTF algorithms exhibit poor computation efficiency. In this paper, a computationally efficient N-CMTF factorization algorithm is presented based on the column-wise element selection, preventing frequent gradient updates. Theoretical and empirical analyses show that the proposed N-CMTF factorization algorithm is not only more accurate but also more computationally efficient than existing algorithms in approximating the tensor as well as in identifying the underlying nature of factors. [ABSTRACT FROM AUTHOR]

Because of strict response-time constraints, efficiency of top-k recommendation is crucial for real-world recommender systems. Locality sensitive hashing and index-based methods usually store both index data and item feature vectors in main memory, so they handle a limited number of items. Hashing-based recommendation methods enjoy low memory cost and fast retrieval of items, but suffer from large accuracy degradation. In this paper, we propose product Quantized Collaborative Filtering (pQCF) for better trade-off between efficiency and accuracy. pQCF decomposes a joint latent space of users and items into a Cartesian product of low-dimensional subspaces, and learns clustered representation within each subspace. A latent factor is then represented by a short code, which is composed of subspace cluster indexes. A user’s preference for an item can be efficiently calculated via table lookup. We then develop block coordinate descent for efficient optimization and reveal the learning of latent factors is seamlessly integrated with quantization. We further investigate an asymmetric pQCF, dubbed as QCF, where user latent factors are not quantized and shared across different subspaces. The extensive experiments with 6 real-world datasets show that pQCF significantly outperforms the state-of-the-art hashing-based CF and QCF increases recommendation accuracy compared to pQCF. [ABSTRACT FROM AUTHOR]

Approximate Nearest Neighbor Search (ANNS) is a fundamental problem in many areas of machine learning and data mining. During the past decade, numerous hashing algorithms are proposed to solve this problem. Every proposed algorithm claims to outperform Locality Sensitive Hashing (LSH), which is the most popular hashing method. However, the evaluation of these hashing article was not thorough enough, and the claim should be re-examined. If implemented correctly, almost all the hashing methods will have their performance improved as the code length increases. However, many existing hashing article only report the performance with the code length shorter than 128. In this article, we carefully revisit the problem of search-with-a-hash-index and analyze the pros and cons of two popular hash index search procedures. Then we proposed a simple but effective novel hash index search approach and made a thorough comparison of eleven popular hashing algorithms. Surprisingly, the random-projection-based Locality Sensitive Hashing ranked the first, which is in contradiction to the claims in all the other 10 hashing article. Despite the extreme simplicity of random-projection-based LSH, our results show that the capability of this algorithm has been far underestimated. For the sake of reproducibility, all the codes used in the article are released on GitHub, which can be used as a testing platform for a fair comparison between various hashing algorithms. [ABSTRACT FROM AUTHOR]

The discovery of time series motifs has emerged as one of the most useful primitives in time series data mining. Researchers have shown its utility for exploratory data mining, summarization, visualization, segmentation, classification, clustering, and rule discovery. Although there has been more than a decade of extensive research, there is still no technique to allow the discovery of time series motifs in the presence of missing data, despite the well-documented ubiquity of missing data in scientific, industrial, and medical datasets. In this work, we introduce a technique for motif discovery in the presence of missing data. We formally prove that our method is admissible, producing no false negatives. We also show that our method can “piggy-back” off the fastest known motif discovery method with a small constant factor time/space overhead. We will demonstrate our approach on diverse datasets with varying amounts of missing data. [ABSTRACT FROM AUTHOR]

According to the literature in psychology, the existence of small dense subgroups is closely related to many mental illnesses, such as depression, bullying, and psychotic disorders. Here, small dense subgroups refer to the small groups in the social network in which members are socially dense but have no or few links to other individuals outside the group. Therefore, in this article, we make the first attempt to address the issue of small dense subgroups with the concept of network intervention from Psychology. We first introduce the new notion of Δ -Subgroups (Δ -SGs) to quantify the small dense subgroups. Then, following the concept of network intervention, we formulate a new research problem, Small Subgroup Maximum Reduction Problem (SSMP), to reduce the number of small dense subgroups (i.e., Δ-SGs) in the social network. We prove that SSMP is NP-Hard and propose a linear-time algorithm, namely 3-SMMTG, to find the optimal solution for a special case of SSMP with Δ = 3 . We then devise a 1/2(1−1/e)-approximation algorithm, namely ESGR, for the general SSMP and enhance its efficiency with effective pruning methods. We conduct a 8-week evaluation study with 812 participants to validate the proposed SSMP and ESGR. The results show that the participants with the network intervention recommended by ESGR have significant improvements on Internet addiction and depression, as compared to those individuals without any intervention. We also perform experiments on 7 real datasets, and the experimental results manifest that the proposed algorithms outperform the other baselines in both efficiency and solution quality. [ABSTRACT FROM AUTHOR]

Eigenvalue decomposition is widely used in dimensionality reduction for knowledge engineering, in particular principal component analysis and other similar spectral methods. Traditional eigenvalue decomposition algorithms for decomposing a matrix of size n × n are usually of complexity O(n3), due to a bottleneck in using Householder/Givens transforms to convert a general matrix to a tri-diagonal one. It is proposed in this article a new algorithm that takes only O(n2.584963 log 1/ɛ) computational complexity to achieve accuracy ɛ of eigenvalue decomposition for any ɛ > 0 . The basic idea of our algorithm is to convert a matrix into a diagonal form in multi-scale divide and conquer scheme, and the conversion is to iteratively and recursively apply two phases of operations called diagonal attractions and diagonal absorptions respectively. In a diagonal attraction, it attracts the off-diagonal entries to make the entries nearer to the diagonal larger in magnitude than those farther away from the diagonal. In a diagonal absorption, it absorbs the near-to-diagonal nonzero entries into the diagonal. In such a scheme, no Householder or Givens transforms are involved. Moreover, diagonal attractions and diagonal absorptions can be implemented with fast matrix multiplications. The scheme’s divide and conquer pattern also allows our algorithm to be easily mapped to modern computer hardware. Our algorithm also complements well the family of randomized eigenvalue/SVD algorithms using sampling techinques, which are of complexity O(nα polylog (1/ɛ)) with small α but very large overheads in the polylog. Their strength in the small exponent α of n in complexity was easily cancelled by the exploding overheads in the polylog. Now, with their low-accuracy estimate refined by our algorithm for high accuracy, their strength can be boosted significantly. [ABSTRACT FROM AUTHOR]

This paper revisits the classical problem of multiple query optimization in federated RDF systems. We propose a heuristic query rewriting-based approach to optimize the evaluation of multiple queries. This approach can take advantage of SPARQL 1.1 to share the common computation of multiple queries while considering the cost of both query evaluation and data shipment. Although we prove that finding the optimal rewriting for multiple queries is NP-complete, we propose a heuristic rewriting algorithm with a bounded approximation ratio. Furthermore, we propose an efficient method to use the interconnection topology between RDF sources to filter out irrelevant sources, and utilize some characteristics of SPARQL 1.1 to optimize multiple joins of intermediate matches. The extensive experimental studies show that the proposed techniques are effective, efficient and scalable. [ABSTRACT FROM AUTHOR]

Ridge regression (RR) is an important machine learning technique which introduces a regularization hyperparameter α to ordinary multiple linear regression for analyzing data suffering from multicollinearity. In this paper, we present a quantum algorithm for RR, where the technique of parallel Hamiltonian simulation to simulate a number of Hermitian matrices in parallel is proposed and used to develop a quantum version of K-fold cross-validation approach, which can efficiently estimate the predictive performance of RR. Our algorithm consists of two phases: (1) using quantum K-fold cross-validation to efficiently determine a good α with which RR can achieve good predictive performance, and then (2) generating a quantum state encoding the optimal fitting parameters of RR with such α , which can be further utilized to predict new data. Since indefinite dense Hamiltonian simulation has been adopted as a key subroutine, our algorithm can efficiently handle non-sparse data matrices. It is shown that our algorithm can achieve exponential speedup over the classical counterpart for (low-rank) data matrices with low condition numbers. But when the condition numbers of data matrices are large to be amenable to full or approximately full ranks of data matrices, only polynomial speedup can be achieved. [ABSTRACT FROM AUTHOR]

With the rapid development of crowdsourcing platforms that aggregate the intelligence of Internet workers, crowdsourcing has been widely utilized to address problems that require human cognitive abilities. Considering great dynamics of worker arrival and departure, it is of vital importance to design a task assignment scheme to adaptively select the most beneficial tasks for the available workers. In this paper, in order to make the most efficient utilization of the worker labor and balance the accuracy of answers and the overall latency, we a) develop a parameter estimation model that assists in estimating worker expertise, question easiness, and answer confidence; b) propose a quality-assured synchronized task assignment scheme that executes in batches and maximizes the number of potentially completed questions (MCQ) within each batch. We prove that MCQ problem is NP-hard and present two greedy approximation solutions to address the problem. The effectiveness and efficiency of the approximation solutions are further evaluated through extensive experiments on synthetic and real datasets. The experimental results show that the accuracy and the overall latency of the MCQ approaches outperform the existing online task assignment algorithms in the synchronized task assignment scenario. [ABSTRACT FROM AUTHOR]

Data-dependent hashing methods have demonstrated good performance in various machine learning applications to learn a low-dimensional representation from the original data. However, they still suffer from several obstacles: First, most of existing hashing methods are trained in a batch mode, yielding inefficiency for training streaming data. Second, the computational cost and the memory consumption increase extraordinarily in the big data setting, which perplexes the training procedure. Third, the lack of labeled data hinders the improvement of the model performance. To address these difficulties, we utilize online sketching hashing (OSH) and present a FasteR Online Sketching Hashing (FROSH) algorithm to sketch the data in a more compact form via an independent transformation. We provide theoretical justification to guarantee that our proposed FROSH consumes less time and achieves a comparable sketching precision under the same memory cost of OSH. We also extend FROSH to its distributed implementation, namely DFROSH, to further reduce the training time cost of FROSH while deriving the theoretical bound of the sketching precision. Finally, we conduct extensive experiments on both synthetic and real datasets to demonstrate the attractive merits of FROSH and DFROSH. [ABSTRACT FROM AUTHOR]

The Earth Mover's Distance (EMD) is a robust similarity measure between two histograms (e.g., probability distributions). It has been extensively used in a wide range of applications, e.g., multimedia, data mining, computer vision, etc. As EMD is a computationally intensive operation, many efficient lower and upper bound functions of EMD have been developed. However, they provide no guarantee on the error. In this work, we study how to compute approximate EMD value with bounded error. First, we develop a parametric dual bound function for EMD, in order to offer sufficient trade-off points for optimization. After that, we propose an approximation framework that leverages on lower and upper bound functions to compute approximate EMD with error guarantee. Then, we present three solutions to solve our problem. Experimental results on real data demonstrate the efficiency and the effectiveness of our proposed solutions. [ABSTRACT FROM AUTHOR]

Clustering analysis has been widely used in statistics, machine learning, pattern recognition, image processing, and so on. It is a great challenge for most existing clustering algorithms to discover clusters with arbitrary shapes. Clustering algorithms based on Minimum spanning tree (MST) are able to discover clusters with arbitrary shapes, but they are time consuming and susceptible to noise points. In this paper, we employ local density peaks (LDP) to represent the whole data set and define a shared neighbors-based distance between local density peaks to better measure the dissimilarity between objects on manifold data. On the basis of local density peaks and the new distance, we propose a novel MST-based clustering algorithm called LDP-MST. It first uses local density peaks to construct MST and then repeatedly cuts the longest edge until a given number of clusters are found. The experimental results on synthetic data sets and real data sets show that our algorithm is competent with state-of-the-art methods when discovering clusters with complex structures. [ABSTRACT FROM AUTHOR]