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Event logs that are recorded by information systems provide a valuable starting point for the analysis of processes in various domains, reaching from healthcare, through logistics, to e-commerce. Specifically, behavioral patterns discovered from an event log enable operational insights, even in scenarios where process execution is rather unstructured and shows a large degree of variability. While such behavioral patterns capture frequently recurring episodes of a process’ behavior, they are not limited to sequential behavior but include notions of concurrency and exclusive choices. Existing algorithms to discover behavioral patterns are context-agnostic, though. They neglect the context in which patterns are observed, which severely limits the granularity at which behavioral regularities are identified. In this paper, we therefore present an approach to discover contextual behavioral patterns. Contextual patterns may be frequent solely in a certain partition of the event log, which enables fine-granular insights into the aspects that influence the conduct of a process. Moreover, we show how to analyze the discovered contextual behavioral patterns in terms of causal relations between context information and the patterns, as well as correlations between the patterns themselves. A complete analysis methodology leveraging all the tools presented in the paper and supplemented by interpretations guidelines is also provided. Finally, experiments with real-world event logs demonstrate the effectiveness of our techniques in obtaining fine-granular process insights. [ABSTRACT FROM AUTHOR]

While recent developments of neural network (NN) models have led to a series of record-breaking achievements in many applications, the lack of sufficiently good datasets remains a problem for some applications. For such a problem, we can however exploit a large number of unstructured text corpora as an external knowledge to complement the training data, and most prevailing neural network solutions employ word embedding methods for such purposes. In this paper, we propose LDA-Reg, a novel knowledge driven regularization framework based on Latent Dirichlet Allocation (LDA) as an alternative to the word embedding methods to adaptively utilize abundant external knowledge and to interpret the NN model. For the joint learning of the parameters, we propose EM-SGD, an effective update method which incorporates Expectation Maximization (EM) and Stochastic Gradient Descent (SGD) to update parameters iteratively. Moreover, we also devise a lazy update and sparse update method for the high-dimensional inputs and sparse inputs respectively. We validate the effectiveness of our regularization framework through an extensive experimental study over real world and standard benchmark datasets. The results show that our proposed framework not only achieves significant improvement over state-of-the-art word embedding methods but also learns interpretable and significant topics for various tasks. [ABSTRACT FROM AUTHOR]

Heterogeneous graph embedding aims at learning low-dimensional representations from a graph featuring nodes and edges of diverse natures, and meanwhile preserving the underlying topology. Existing approaches along this line have largely relied on meta-paths, which are by nature hand-crafted and pre-defined transition rules, so as to explore the semantics of a graph. Despite the promising results, defining meta-paths requires domain knowledge, and thus when the test distribution deviates from the priors, such methods are prone to errors. In this paper, we propose a self-learning scheme for heterogeneous graph embedding, termed as self-guided walk (SILK), that bypasses meta-paths and learns adaptive attentions for node walking. SILK assumes no prior knowledge or annotation is provided, and conducts a customized random walk to encode the contexts of the heterogeneous graph of interest. Specifically, this is achieved via maintaining a dynamically-updated guidance matrix that records the node-conditioned transition potentials. Experimental results on four real-world datasets demonstrate that SILK significantly outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Competition between Points of Interest (POIs) refers to the situation in which two POIs directly or indirectly provide similar services to secure businesses. A large portion of prior studies on competition analysis focuses on mining textual data, e.g., news articles and social comments. However, the increasing availability of human mobility and mobile query data enables a new paradigm for analyzing the competitive relationships among POIs, which remains largely unexplored. To this end, in this paper, we attempt to mine large-scale online map search query data for better understanding POI competitive relationships. Based on a co-query POI graph built from the map search query data, we develop a novel neural graphlet-based prediction framework to predict the competitive relationships among POIs. A unique perspective of our model is to infer latent POI competitive relationships by integrating multiple distinct factors, e.g., graphlet structure, geographical distance, and regional features, reflected in map search query data and POI data. Finally, we conduct extensive experiments on real-world datasets to demonstrate the effectiveness of the proposed framework, and show that our framework outperforms all baselines with a significant margin in all evaluation metrics. [ABSTRACT FROM AUTHOR]

Spatial co-location pattern mining (SCPM) aims to discover subsets of spatial features frequently located together in proximate areas. Previous studies of SCPM solely concern the inter-features association of a pattern, but neglect the interesting intra-feature behavior. In this paper, we propose the task of spatial co-location pattern mining with coupling relation consideration (SCPM-CR) to capture complex relations embedded in a co-location. Specifically, InterPCI measure is designed to capture the inter-features coupling by considering the comprehensive interaction of objects for the features in a pattern, and luckily it possesses the anti-monotone property. Another measure, IntraCAI, is proposed to capture the congregating behavior of intra-feature objects under the restriction of a co-location. A general framework is designed for SCPM-CR task and experiments show that a large fraction of computation time is devoted to identifying the participating objects of a candidate pattern. To tackle this calculation bottleneck, a novel candidate-and-search algorithm is suggested, CS-HBS, equipped with heuristic backtracking search. Extensive experiments are conducted on real and synthetic datasets to demonstrate the superiority of SCPM-CR compared with traditional SCPM methods, and also to validate the efficiency and scalability of CS-HBS. Experimental results show that CS-HBS outperforms the baselines by several times or even orders of magnitude. [ABSTRACT FROM AUTHOR]

Over two decades, context awareness has been incorporated into recommender systems in order to provide, not only the top-rated items to consumers but also the ones that are suitable to the user context. As a class of context-aware systems, context-aware service recommendation (CASR) aims to bind high-quality services to users, while taking into account their context requirements, including invocation time, location, social profiles, connectivity, and so on. However, current CASR approaches are not scalable with the huge amount of service data (QoS and context information, users reviews and feedbacks). In addition, they lack a rich representation of contextual information, as they adopt a simple matrix view. Moreover, current CASR approaches adopt the traditional user-service relation and they do not allow for multi-relational interactions between users and services in different contexts. To offer a scalable and context-sensitive service recommendation with great analysis and learning capabilities, we provide a rich and multi-relational representation of the CASR knowledge, based on the concept of knowledge graph. The constructed context-aware service knowledge graph (C-SKG) is, then, transformed into a low-dimensional vector space to facilitate its processing. For this purpose, we adopt Dilated Recurrent Neural Networks to propose a context-aware knowledge graph embedding, based on the principles of first-order and subgraph-aware proximity. Finally, a recommendation algorithm is defined to deliver the top-rated services according to the target user's context. Experiments have proved the accuracy and scalability of our solution, compared to state-of-the-art CASR approaches. [ABSTRACT FROM AUTHOR]

With the increasing accumulation of spatial-temporal trajectory data, location-based data mining has recently been extensively studied. A fundamental research topic in this field is learning the embedding vectors of locations through self-supervised pre-training. Pre-trained embedding vectors can utilize the highly available unlabeled trajectory data, and benefit downstream tasks in multiple aspects. However, most existing methods ignore the temporal information hidden in the visited time of locations in trajectories. Considering that human activities are highly regulated by specific periods of a day, temporal information can reflect some intrinsic characteristics of locations, so it is necessary to fuse them into location embedding vectors. In this paper, we propose a Time-Aware Location Embedding (TALE) pre-training method based on the CBOW framework, which is able to incorporate temporal information into the learned embedding vectors of locations. A novel temporal tree structure is designed to extract temporal information during the calculation of Hierarchical Softmax. In order to verify the effectiveness of TALE, we apply the learned embedding vectors into three downstream location-based prediction tasks, i.e., location classification, location visitor flow prediction and user next location prediction. Experiments are conducted on four real-world user trajectory datasets, and the experimental results demonstrate that our TALE model can obviously help downstream tasks gain better performance. [ABSTRACT FROM AUTHOR]

Process mining aims at discovering behavioral knowledge of business processes from their event logs, which has received an increasing attention in the era of cloud computing and big data. Surprisingly, to date, discovering structural errors (e.g., deadlocks and lack of synchronization) from event logs has not been considered in state-of-the-art process mining techniques. Moreover, existing process discovery approaches cannot be directly applied to event logs of processes with structural errors due to erroneous event occurrences caused by unsynchronized activities. To address this problem, we first preprocess the event log to obtain two separate event logs that are used to discover deadlocks and lack of synchronization, respectively. Erroneous event occurrences caused by unsynchronized activities are discarded in the two processed event logs, from which our error mining algorithms can discover all process fragments involving structural errors, without the need to obtain the overall process first. We implement our approach in a ProM plugin and evaluate it on event logs of real-life business processes, the results of which demonstrate that our approach can effectively and efficiently discover deadlocks and lack of synchronization if event logs contain sufficient event sequences. [ABSTRACT FROM AUTHOR]

Given a dataset $\mathcal S$ S composed of $g$ g subsets with each data item belonging to one of them, multiset membership lookup takes an item $e$ e as input and outputs a binary answer whether $e\in {\mathcal S}$ e ∈ S and, in case of yes, the ID of the subset to which $e$ e belongs. Overlaid upon while more sophisticated than the canonical membership lookup, multiset membership lookup emerges as a pivotal functionality in many computing and networking paradigms. The quest to achieve high-speed, high-accuracy lookup with limited memory cost makes lookup algorithm design a challenging task, particularly when the data items arrive as a stream. In this paper, we devise compact data structures and lookup algorithms that are amendable for hardware implementation, while guaranteeing high lookup accuracy and supporting interactive query processing. We first propose multi-hash color table, a variant of Bloom filter, to encode subset IDs compactly and map the ID of an item to its subset ID. We further construct a more balanced data structure called balanced multi-hash color table to improve the compactness by integrating the state-of-the-art load balancing technique. We complete our work by addressing the case of batch arrivals and design a batched recording algorithm optimizing the memory efficiency. We give both theoretical and empirical analysis to characterize and evaluate the performance of the proposed algorithms in terms of lookup accuracy, memory and access efficiency. [ABSTRACT FROM AUTHOR]

The KNN algorithm is one of the most popular data mining algorithms. It has been widely and successfully applied to data analysis applications across a variety of research topics in computer science. This paper illustrates that, despite its success, there remain many challenges in KNN classification, including K computation, nearest neighbor selection, nearest neighbor search and classification rules. Having established these issues, recent approaches to their resolution are examined in more detail, thereby providing a potential roadmap for ongoing KNN-related research, as well as some new classification rules regarding how to tackle the issue of training sample imbalance. To evaluate the proposed approaches, some experiments were conducted with 15 UCI benchmark datasets. [ABSTRACT FROM AUTHOR]

Learning low-dimensional topological representation of a network in dynamic environments is attracting much attention due to the time-evolving nature of many real-world networks. The main and common objective of Dynamic Network Embedding (DNE) is to efficiently update node embeddings while preserving network topology at each time step. The idea of most existing DNE methods is to capture the topological changes at or around the most affected nodes (instead of all nodes) and accordingly update node embeddings. Unfortunately, this kind of approximation, although can improve efficiency, cannot effectively preserve the global topology of a dynamic network at each time step, due to not considering the inactive sub-networks that receive accumulated topological changes propagated via the high-order proximity. To tackle this challenge, we propose a novel node selecting strategy to diversely select the representative nodes over a network, which is coordinated with a new incremental learning paradigm of Skip-Gram based embedding approach. The extensive experiments show GloDyNE, with a small fraction of nodes being selected, can already achieve the superior or comparable performance w.r.t. the state-of-the-art DNE methods in three typical downstream tasks. Particularly, GloDyNE significantly outperforms other methods in the graph reconstruction task, which demonstrates its ability of global topology preservation. [ABSTRACT FROM AUTHOR]

Data are invaluable. How can we assess the value of data objectively, systematically and quantitatively? Pricing data, or information goods in general, has been studied and practiced in dispersed areas and principles, such as economics, marketing, electronic commerce, data management, data mining and machine learning. In this article, we present a unified, interdisciplinary and comprehensive overview of this important direction. We examine various motivations behind data pricing, understand the economics of data pricing and review the development and evolution of pricing models according to a series of fundamental principles. We discuss both digital products and data products. We also consider a series of challenges and directions for future work. [ABSTRACT FROM AUTHOR]

Multi-view clustering has attracted increasing attention in multimedia, machine learning and data mining communities. As one kind of the essential multi-view clustering algorithm, multi-view subspace clustering (MVSC) becomes more and more popular due to its strong ability to reveal the intrinsic low dimensional clustering structure hidden across views. Despite superior clustering performance in various applications, we observe that existing MVSC methods directly fuse multi-view information in the similarity level by merging noisy affinity matrices; and isolate the processes of affinity learning, multi-view information fusion and clustering. Both factors may cause insufficient utilization of multi-view information, leading to unsatisfying clustering performance. This paper proposes a novel consensus one-step multi-view subspace clustering (COMVSC) method to address these issues. Instead of directly fusing multiple affinity matrices, COMVSC optimally integrates discriminative partition-level information, which is helpful to eliminate noise among data. Moreover, the affinity matrices, consensus representation and final clustering labels matrix are learned simultaneously in a unified framework. By doing so, the three steps can negotiate with each other to best serve the clustering task, leading to improved performance. Accordingly, we propose an iterative algorithm to solve the resulting optimization problem. Extensive experiment results on benchmark datasets demonstrate the superiority of our method against other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Detecting anomalous objects from given data has a broad range of real-world applications. Although there is a rich number of outlier detection algorithms, most of them involve hidden assumptions and restrictions. This paper proposes a novel, yet effective outlier learning algorithm that is based on decomposing the full attributes space into different combinations of subspaces, in which the 3D-vectors, representing the data points per 3D-subspace, are rotated about the geometric median, using Rodrigues rotation formula, to construct the overall outlying score. The proposed approach is parameter-free, requires no distribution assumptions and easy to implement. Extensive experimental study and comparison are conducted on both synthetic and real-world datasets with six popular outlier detection algorithms, each from different category. The comparison is evaluated based on the precision @s, average precision, rank power, AUC ROC and time complexity metrics. The results show that the performance of the proposed method is competitive and promising. [ABSTRACT FROM AUTHOR]

Subgraph enumeration is important for many applications such as network motif discovery, community detection, and frequent subgraph mining. To accelerate the execution, recent works utilize graphics processing units (GPUs) to parallelize subgraph enumeration. The performances of these parallel schemes are dominated by the set intersection operations which account for up to 95 percent of the total processing time. (Un)surprisingly, a significant portion (as high as 99 percent) of these operations is actually redundant, i.e., the same set of vertices is repeatedly encountered and evaluated. Therefore, in this article, we seek to salvage and recycle the results of such operations to avoid repeated computation. Our solution consists of two phases. In the first phase, we generate a reusable plan that determines the opportunity for reuse. The plan is based on a novel reuse discovery mechanism that can identify available results to prevent redundant computation. In the second phase, the plan is executed to produce the subgraph enumeration results. This processing is based on a newly designed reusable parallel search strategy that can efficiently maintain and retrieve the results of set intersection operations. Our implementation on GPUs shows that our approach can achieve up to 5 times speedups compared with the state-of-the-art GPU solutions. [ABSTRACT FROM AUTHOR]

Representing nodes in a network as low-dimensional dense vectors can facilitate the analysis of complex networks, which is a challenging task and has attracted increasing attention. However, in the real world, networks are changing over time, such as cooperation in citation networks and communication in email networks. Most of the recent embedding methods only focus on static networks. Thus they ignore the critical temporal information, which serves as a supplement to structure information and has been proved to improve the quality of node embedding. In this work, we propose an unsupervised deep learning model called DTINE, which explores temporal information for further enhancing the robustness of node representations in dynamic networks. To preserve network topology, we pertinently design a temporal weight and sampling strategy to extract features from the neighborhoods. An attention mechanism will be applied on the recurrent neural network to measure the contributions of historical information and capture the evolution of the networks. Experimental results on four real-world networks demonstrate that the proposed method achieves better performance than state-of-the-art methods. [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]

With the fast development of various positioning techniques such as Global Position System (GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly available nowadays. Mining valuable knowledge from spatio-temporal data is critically important to many real-world applications including human mobility understanding, smart transportation, urban planning, public safety, health care and environmental management. As the number, volume and resolution of spatio-temporal data increase rapidly, traditional data mining methods, especially statistics-based methods for dealing with such data are becoming overwhelmed. Recently deep learning models such as recurrent neural network (RNN) and convolutional neural network (CNN) have achieved remarkable success in many domains due to the powerful ability in automatic feature representation learning, and are also widely applied in various spatio-temporal data mining (STDM) tasks such as predictive learning, anomaly detection and classification. In this paper, we provide a comprehensive review of recent progress in applying deep learning techniques for STDM. We first categorize the spatio-temporal data into five different types, and then briefly introduce the deep learning models that are widely used in STDM. Next, we classify existing literature based on the types of spatio-temporal data, the data mining tasks, and the deep learning models, followed by the applications of deep learning for STDM in different domains including transportation, on-demand service, climate & weather analysis, human mobility, location-based social network, crime analysis, and neuroscience. Finally, we conclude the limitations of current research and point out future research directions. [ABSTRACT FROM AUTHOR]

Pattern mining (PM) refers to the process of discovering patterns of interest to users from data. However, most studies have considered only one pattern, such as frequent pattern or high-utility pattern. With the continuous requirement of businesses in various industries, the single-objective PM methods are difficult to meet the increasingly diverse needs of users. In this paper, a multi-objective problem model for high quality pattern mining (HQPM) is proposed, where the objectives are support, occupancy, and utility. In order to solve the proposed three-objective problem efficiently, an improved multi-objective evolutionary algorithm for HQPM (MOEA-PM) is proposed. Two kinds of population initialization strategies are designed, which is used to ensure the population is effectively distributed in the feasible solution space. By taking the properties of the model into consideration, an auxiliary tool is proposed to accelerate the convergence of the algorithm. Experimental results on real-world datasets show that the proposed three-objective problem model with the MOEA-PM algorithm can discover patterns that are both frequently occurring and has a high utility in the transaction datasets, while at the same time being relatively complete. Compared with the state-of-the-art MOEA-based HQPM algorithms, MOEA-PM has better performance in terms of efficiency, quality, and convergence speed. [ABSTRACT FROM AUTHOR]

Matrix decomposition is one of the fundamental tools to discover knowledge from big data generated by modern applications. However, it is still inefficient or infeasible to process very big data using such a method in a single machine. Moreover, big data are often distributedly collected and stored on different machines. Thus, such data generally bear strong heterogeneous noise. It is essential and useful to develop distributed matrix decomposition for big data analytics. Such a method should scale up well, model the heterogeneous noise, and address the communication issue in a distributed system. To this end, we propose a distributed Bayesian matrix decomposition model (DBMD) for big data mining and clustering. Specifically, we adopt three strategies to implement the distributed computing including 1) the accelerated gradient descent, 2) the alternating direction method of multipliers (ADMM), and 3) the statistical inference. We investigate the theoretical convergence behaviors of these algorithms. To address the heterogeneity of the noise, we propose an optimal plug-in weighted average that reduces the variance of the estimation. Synthetic experiments validate our theoretical results, and real-world experiments show that our algorithms scale up well to big data and achieves superior or competing performance compared to two typical distributed methods including Scalable-NMF and scalable k-means++. [ABSTRACT FROM AUTHOR]

Periodicity is a frequently happening phenomenon for social interactions in temporal networks. Mining periodic communities are essential to understanding periodic group behaviors in temporal networks. Unfortunately, most previous studies for community mining in temporal networks ignore the periodic patterns of communities. In this paper, we study the problem of seeking periodic communities in a temporal network, where each edge is associated with a set of timestamps. We propose novel models, including $\sigma$ σ -periodic $k$ k -core and $\sigma$ σ -periodic $k$ k -clique, that represent periodic communities in temporal networks. Specifically, a $\sigma$ σ -periodic $k$ k -core (or $\sigma$ σ -periodic $k$ k -clique) is a $k$ k -core (or clique with size larger than $k$ k ) that appears at least $\sigma$ σ times periodically in the temporal graph. The problem of searching periodic core is efficient but the resulting communities may be not enough cohesive; the problem of enumerating all periodic cliques is not efficient (NP-hard) but the resulting communities are very cohesive. To compute all of them efficiently, we first develop two effective graph reduction techniques to significantly prune the temporal graph. Then, we transform the temporal graph into a static graph and prove that mining the periodic communities in the temporal graph equals mining communities in the transformed graph. Subsequently, we propose a decomposition algorithm to search maximal $\sigma$ σ -periodic $k$ k -core, a Bron-Kerbosch style algorithm to enumerate all maximal $\sigma$ σ -periodic $k$ k -cliques, and a branch-and-bound style algorithm to find the maximum $\sigma$ σ -periodic clique. The results of extensive experiments on five real-life datasets demonstrate the efficiency, scalability, and effectiveness of our algorithms. [ABSTRACT FROM AUTHOR]

With widespread deployment of GPS devices, massive spatiotemporal trajectories became more accessible. This booming trend paved the solid data ground for researchers to discover the regularities or patterns of human mobility. However, there are still three challenges in semantic pattern extraction including semantic absence, semantic bias and semantic complexity. In this paper, we invent and apply a novel data structure namely City Semantic Diagram to overcome above three challenges. First, our approach resolves semantic absence by exactly identifying semantic behaviours from raw trajectories. Second, the design of semantic purification helps us to detect semantic complexity from human mobility. Third, we avoid semantic bias using objective data source such as ubiquitous GPS trajectories. Comprehensive and massive experiments have been conducted based on real taxi trajectories and points of interest in Shanghai. Compared with existing approaches, City Semantic Diagram is able to discover fine-grained semantic patterns effectively and accurately. [ABSTRACT FROM AUTHOR]

One important problem that is insufficiently studied is finding densest lasting-subgraphs in large dynamic graphs, which considers the time duration of the subgraph pattern. We propose a framework called Expectation-Maximization with Utility functions (EMU), a novel stochastic approach that nontrivially extends the conventional EM approach. EMU has the flexibility of optimizing any user-defined utility functions. We validate our EMU approach by showing that it converges to the optimum—by proving that it is a specification of the general Minorization-Maximization (MM) framework with convergence guarantees. We devise EMU algorithms for the densest lasting subgraph problem, as well as several variants by varying the utility function. Using real-world data, we evaluate the effectiveness and efficiency of our techniques, and compare them with two prior approaches on dense subgraph detection. [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]

For graph-based multi-view clustering, a critical issue is to capture consensus cluster structures via a two-stage learning scheme. Specifically, first learn similarity graph matrices of multiple views and then fuse them into a unified superior graph matrix. Most current methods learn pairwise similarities between data points for each view independently, which is widely used in single view. However, the consensus information contained in multiple views are ignored, and the involved biases lead to an undesirable unified graph matrix. To this end, we propose a bipartite graph based multi-view clustering (BIGMC) approach. The consensus information can be represented by a small number of representative uniform anchor points for different views. A bipartite graph is constructed between data points and the anchor points. BIGMC constructs the bipartite graph matrices of all views and fuses them to produce a unified bipartite graph matrix. The unified bipartite graph matrix in turn improves the bipartite graph similarity matrix of each view and updates the anchor points. The final unified graph matrix forms the final clusters directly. In BIGMC, an adaptive weight is added for each view to avoid outlier views. A low-rank constraint is imposed on the Laplacian matrix of the unified matrix to construct a multi-component unified bipartite graph, where the component number corresponds to the required cluster number. The objective function is optimized in an alternating optimization fashion. Experimental results on synthetic and real-world data sets demonstrate its effectiveness and superiority compared with the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Heterogeneous information networks (HINs) are usually used to model information systems with multi-type objects and relations. In contrast, graphs that have a single type of nodes and edges, are often called homogeneous graphs. Measuring similarities among objects is an important task in data mining applications, such as web search, link prediction, and clustering. Currently, several similarity measures are defined for HINs. Most of these measures are based on meta-paths, which show sequences of node classes and edge types along the paths between two nodes. However, meta-paths, which are often designed by domain experts, are hard to enumerate and choose w.r.t. the quality of similarity scores. This makes using existing similarity measures in real applications difficult. To address this problem, we extend SimRank, a well-known similarity measure on homogeneous graphs, to HINs, by introducing the concept of the decay graph. The newly proposed similarity measure is called HowSim, which has the property of being meta-path free, and capturing the structural and semantic similarity simultaneously. The generality and effectiveness of HowSim, and the efficiency of our proposed algorithms for computing HowSim scores, are demonstrated by extensive experiments. [ABSTRACT FROM AUTHOR]

Data similarity (or distance) computation is a fundamental research topic which underpins many high-level applications based on similarity measures in machine learning and data mining. However, in large-scale real-world scenarios, the exact similarity computation has become daunting due to “3V” nature (volume, velocity and variety) of big data. In this case, the hashing techniques have been verified to efficiently conduct similarity estimation in terms of both theory and practice. Currently, MinHash is a popular technique for efficiently estimating the Jaccard similarity of binary sets and furthermore, weighted MinHash is generalized to estimate the generalized Jaccard similarity of weighted sets. This review focuses on categorizing and discussing the existing works of weighted MinHash algorithms. In this review, we mainly categorize the weighted MinHash algorithms into quantization-based approaches, “active index”-based ones and others, and show the evolution and inherent connection of the weighted MinHash algorithms, from the integer weighted MinHash ones to the real-valued weighted MinHash ones. Also, we have developed a Python toolbox for the algorithms, and released it in our github. We experimentally conduct a comprehensive study of the standard MinHash algorithm and the weighted MinHash ones in the similarity estimation error and the information retrieval task. [ABSTRACT FROM AUTHOR]

Online social media have aroused much research interest in recent years. In contrast to previous work that focused on the detection of emerging topics, this article undertakes the prediction of active users in online social events, which is so far rarely explored. This prediction task is formulated as a binary classification problem that built on real-world tweet streams, taking Ferguson event and New York Chockhold event as examples. Then, a comprehensive user feature system is designed to characterize the events’ online participants, which includes not only basic statistical characteristics and image-pixel-level features, but also some emotional features and personality features. Next, the Weighted Random Forest (Weighted-RF) classifier is adopted to solve the classification problem. Based on the user feature system and the classifier, the experience of a previous event can be archived and applied to the prediction of later similar events. Experimental results show that the Weighted-RF trained by samples of Ferguson event can effectively predict active users in NYC event, with an AUC value around 0.8392. Besides, the image-content based personality model provides a new tool for depicting user portraits, which further contributes to the quantitative analysis of online social events. [ABSTRACT FROM AUTHOR]

Adverse event detection is critical for many real-world applications including timely identification of product defects, disasters, and major socio-political incidents. In the health context, adverse drug events account for countless hospitalizations and deaths annually. Since users often begin their information seeking and reporting with online searches, examination of search query logs has emerged as an important detection channel. However, search context - including query intent and heterogeneity in user behaviors – is extremely important for extracting information from search queries, and yet the challenge of measuring and analyzing these aspects has precluded their use in prior studies. We propose DeepSAVE, a novel deep learning framework for detecting adverse events based on user search query logs. DeepSAVE uses an enriched variational autoencoder encompassing a novel query embedding and user modeling module that work in concert to address the context challenge associated with search-based detection of adverse events. Evaluation results on three large real-world event datasets show that DeepSAVE outperforms existing detection methods as well as comparison deep learning auto encoders. Ablation analysis reveals that each component of DeepSAVE significantly contributes to its overall performance. Collectively, the results demonstrate the viability of the proposed architecture for detecting adverse events from search query logs. [ABSTRACT FROM AUTHOR]

Convolutional neural network (CNN) has been proven an effective way to extract high-level features from remote sensing (RS) images automatically. Many variants of the CNN model have been proposed, including principal component analysis network (PCANet), canonical correlation analysis network (CCANet), multiple scale CCANet (MS-CCANet) and multiview CCANet (MCCANet). The PCANet is specialized for single view feature abstraction, while in many real-world practices, the RS data are frequently observed from many more views. Although CCANet, MS-CCANet and MCCANet can be applied to two or more view data, they consider only the pair-wise correlation by calculating a series of two-order covariance matrices. However, the high-order consistence, which can only be explored by collectively and simultaneously examining all views, remains undiscovered. In this paper, we propose the tensor canonical correlation analysis network (TCCANet) to tackle this problem. Particularly, TCCANet learns filter banks by simultaneously maximizing arbitrary number of views with high-order-correlation and solves the optimization problem by decomposing a covariance tensor. After the convolutional stage, we utilize binarization and block-wise histogram strategies to generate the final feature. Furthermore, we also develop a Multiple Scale version of TCCANet, i.e., MS-TCCANet, to extract enriched representation of the RS data by incorporating all previous convolutional layers. Numerical experiment results on RSSCN7 and SAT-6 datasets demonstrate the advantages of TCCANet and MS-TCCANet for RS scene recognition. [ABSTRACT FROM AUTHOR]

In this paper, we focus on co-selection of instances and features in the semi-supervised learning scenario. In this context, co-selection becomes a more challenging problem as data contain labeled and unlabeled examples sampled from the same population. To carry out such semi-supervised co-selection, we propose a unified framework, called sCOs, which efficiently integrates labeled and unlabeled parts into the co-selection process. The framework is based on introducing both a sparse regularization term and a similarity preserving approach. It evaluates the usefulness of features and instances in order to select the most relevant ones, simultaneously. We propose two efficient algorithms that work for both convex and nonconvex functions. To the best of our knowledge, this paper offers, for the first time ever, a study utilizing nonconvex penalties for the co-selection of semi-supervised learning tasks. Experimental results on some known benchmark datasets are provided for validating sCOs and comparing it with some representative methods in the state-of-the art. [ABSTRACT FROM AUTHOR]

Matrix factorization (MF), a popular unsupervised learning technique for data representation, has been widely applied in data mining and machine learning. According to different application scenarios, one can impose different constraints on the factorization to find the desired basis, which captures high-level semantics for the given data, and learns the compact representation corresponding to the basis. We note that almost all previous work on MF in data mining has ignored to find such a basis, which can carry high-order semantics in the data. In this article, we propose a novel MF framework called Joint Hypergraph Embedding and Sparse Coding (JHESC), in which the obtained basis captures high-order semantic information in data. Specifically, we first propose a new hypergraph learning model to obtain a more discriminative basis by hypergraph-based Laplacian Eigenmap, then sparse coding is conducted on the learned basis such that the new representation has stronger identification capability. In addition, we extend the proposed method to the reproducing kernel Hilbert space for dealing with nonlinear data more effectively. Extensive experimental results on data clustering demonstrate that the proposed method consistently outperforms the other state-of-the-art matrix factorization methods. [ABSTRACT FROM AUTHOR]

A heterogeneous information network (HIN) is one whose nodes model objects of different types and whose links model objects’ relationships. To enrich its information, objects in an HIN are typically associated with additional attributes. We call such an HIN an Attributed HIN or AHIN. We study the problem of clustering objects in an AHIN, taking into account objects’ similarities with respect to both object attribute values and their structural connectedness in the network. We show how supervision signal, expressed in the form of a must-link set and a cannot-link set, can be leveraged to improve clustering results. We put forward the SCHAIN algorithm to solve the clustering problem, and two highly efficient variants, SCHAIN-PI and SCHAIN-IRAM, which employ the power iteration based method and the implicitly restarted Arnoldi method respectively to compute eigenvectors of a matrix. We conduct extensive experiments comparing SCHAIN-based algorithms with other state-of-the-art clustering algorithms. Our results show that SCHAIN-IRAM outperforms other competitors in terms of clustering effectiveness and is highly efficient. [ABSTRACT FROM AUTHOR]

Time series are generated at an unprecedented rate in domains ranging from finance, medicine to education. Collections composed of heterogeneous, variable-length and misaligned times series are best explored using a plethora of dynamic time warping distances. However, the computational costs of using such elastic distances result in unacceptable response times. We thus design the first practical solution for the efficient GENeral EXploration of time series leveraging multiple warped distances. GENEX pre-processes time series data in metric point-wise distance spaces, while providing bounds for the accuracy of corresponding analytics derived in non-metric warped distance spaces. Our empirical evaluation on 66 benchmark datasets provides a comparative study of the accuracy and response times of diverse warped distances. We show that GENEX is a versatile yet highly efficient solution for processing expensive-to-compute warped distances over large datasets, with response times 3 to 5 orders of magnitude faster than state-of-art systems. [ABSTRACT FROM AUTHOR]

Predicting emerging hot events in an early stage is essential for various applications, including information dissemination mining, ads recommendation and etc. Existing techniques either require a long-term observation over the event or features that are expensive to extract. However, given limited data at the early stage of an emerging event, the temporal features of hot events and non-hot events are not distinctive enough yet. In this work, we introduce BEEP, a Bayesian perspective Early stage Event Prediction model, that tackles this dilemma. We formulate the hot event prediction problem by two Semi-Naive Bayes Classifiers, where we consider both the temporal features and structural features and perform distribution test for the selected features. Theoretical analysis and extensive empirical evaluations on two real datasets demonstrate the effectiveness of our methods. [ABSTRACT FROM AUTHOR]

We propose a novel statistical approach to evaluate the statistical significance (reliability) of the results from discriminative sub-trajectory mining, which we call Statistically Discriminative Sub-trajectory Mining (Stat-DSM). Given two groups of trajectories, the goal of Stat-DSM is to extract moving patterns in the form of sub-trajectories that occur statistically significantly more often in one group than in the other. An advantage of the proposed method is that the statistical significance of the extracted sub-trajectories are properly controlled in the sense that the probability of finding a falsely discriminative sub-trajectory is smaller than a specified significance threshold $\alpha$ α (e.g., 0.05), which is crucial when the method is used in scientific or social science studies under noisy environments. Finding such statistically discriminative sub-trajectories from a massive trajectory dataset is both computationally and statistically challenging. In the Stat-DSM method, we address these difficulties by introducing a tree representation of sub-trajectories, and applying an efficient permutation-based statistical inference method to the tree. To the best of our knowledge, Stat-DSM is the first method that provides a statistical approach to quantify the reliability of discriminative sub-trajectory mining results. We illustrate the effectiveness and scalability of the Stat-DSM method by applying it to a real-world dataset containing 1,000,000 trajectories. [ABSTRACT FROM AUTHOR]

Given that heterogeneous information networks (HIN) encompass nodes and edges belonging to different semantic types, they can model complex data in real-world scenarios. Thus, HIN embedding has received increasing attention, which aims to learn node representations in a low-dimensional space, in order to preserve the structural and semantic information on the HIN. In this regard, metagraphs, which model common and recurring patterns on HINs, emerge as a powerful tool to capture semantic-rich and often latent relationships on HINs. Although metagraphs have been employed to address several specific data mining tasks, they have not been thoroughly explored for the more general HIN embedding. In this paper, we leverage metagraphs to learn relationship-preserving HIN embedding in a self-supervised setting, to support various relationship mining tasks. In particular, we observe that most of the current approaches often under-utilize metagraphs, which are only applied in a pre-processing step and do not actively guide representation learning afterwards. Thus, we propose the novel framework of mg2vec, which learns the embeddings for metagraphs and nodes jointly. That is, metagraphs actively participates in the learning process by mapping themselves to the same embedding space as the nodes do. Moreover, metagraphs guide the learning through both first- and second-order constraints on node embeddings, to model not only latent relationships between a pair of nodes, but also individual preferences of each node. Finally, we conduct extensive experiments on three public datasets. Results show that mg2vec significantly outperforms a suite of state-of-the-art baselines in relationship mining tasks including relationship prediction, search and visualization. [ABSTRACT FROM AUTHOR]

Geographical topic models have been used to mine geo-tagged documents for topical region and geographical topics, and also have applications in recommendations, user mobility modeling, event detection, etc. Existing studies focus on learning effective geographical topic models while ignoring the efficiency issue. However, it is very expensive to train geographical topic models — it may take days to train a geographical topic model of a small scale on a collection of documents with millions of word tokens. In this paper, we propose the first distributed solution, called ${\sf PGeoTopic}$ PGeoTopic , for training geographical topic models. The proposed solution comprises several novel technical components to increase parallelism, reduce memory requirement, and reduce communication cost. Experiments show that our approach for mining geographical topic models is scalable with both model size and data size on distributed systems. [ABSTRACT FROM AUTHOR]

Cyber attacks have become increasingly complicated, persistent, organized, and weaponized. Faces with this situation, drives a rising number of organizations across the world are showing a growing willingness to leverage the open exchange of cyber threat intelligence (CTI) for obtaining a full picture of the fast-evolving cyber threat situation and protecting themselves against cyber-attacks. However, modeling CTI is challenging due to the explicit and implicit relationships among CTI and the heterogeneity of cyber-threat infrastructure nodes involved in CTI. Owing to the limited labels of cyber threat infrastructure nodes involved in CTI, automatically identifying the threat type of infrastructure nodes for early warning is also challenging. To tackle these challenges, a practical system called HinCTI is developed for modeling cyber threat intelligence and identifying threat types. We first design a threat intelligence meta-schema to depict the semantic relatedness of infrastructure nodes. We then model cyber threat intelligence on heterogeneous information network (HIN), which can integrate various types of infrastructure nodes and rich relations among them. Following, we define a meta-path and meta-graph instances-based threat Infrastructure similarity (MIIS) measure between threat infrastructure nodes and present a MIIS measure-based heterogeneous graph convolutional network (GCN) approach to identify the threat types of infrastructure nodes involved in CTI. Moreover, through the hierarchical regularization strategy, our model can alleviate the problem of overfitting and achieve good results in the threat type identification of infrastructure nodes. To the best of our knowledge, this work is the first to model CTI on HIN for threat identification and propose a heterogeneous GCN-based approach for threat type identification of infrastructure nodes. With HinCTI, comprehensive experiments are conducted on real-world datasets, and experimental results demonstrate that our proposed approach can significantly improve the performance of threat type identification compared to the existing state-of-the-art baseline methods. Our work is beneficial to greatly relieve security analysts from heavy analysis work and efficiently protect organizations against cyber-attacks. [ABSTRACT FROM AUTHOR]

Nonnegative matrix factorization (NMF) has been successfully applied in several data mining tasks. Recently, there is an increasing interest in the acceleration of NMF, due to its high cost on large matrices. On the other hand, the privacy issue of NMF over federated data is worthy of attention, since NMF is prevalently applied in image and text analysis which may involve leveraging privacy data (e.g, medical image and record) across several parties (e.g., hospitals). In this paper, we study the acceleration and security problems of distributed NMF. First, we propose a distributed sketched alternating nonnegative least squares(DSANLS) framework for NMF, which utilizes a matrix sketching technique to reduce the size of nonnegative least squares subproblems with a convergence guarantee. For the second problem, we show that DSANLS with modification can be adapted to the security setting, but only for one or limited iterations. Consequently, we propose four efficient distributed NMF methods in both synchronous and asynchronous settings with a security guarantee. We conduct extensive experiments on several real datasets to show the superiority of our proposed methods. The implementation of our methods is available at https://github.com/qianyuqiu79/DSANLS. [ABSTRACT FROM AUTHOR]

Subsequence clustering is an important issue in time series data mining. Observing that most time series consist of various patterns with different unknown lengths, we propose an optimization framework to adaptively estimate the lengths and representations for different patterns. Our framework minimizes the inner subsequence cluster errors with respect to subsequence clusters and segmentation under time series cover constraint where the subsequence cluster lengths can be variable. To optimize our framework, we first generate abundant initial subsequence clusters with different lengths. Then, three cluster operations, i.e., cluster splitting, combination and removing, are used to iteratively refine the cluster lengths and representations by respectively splitting clusters consisting of different patterns, joining neighboring clusters belonging to the same pattern and removing clusters to the predefined cluster number. During each cluster refinement, we employ an efficient algorithm to alternatively optimize subsequence clusters and segmentation based on dynamic programming. Our method can automatically and efficiently extract the unknown variable-length subsequence clusters in the time series. Comparative results with the state-of-the-art are conducted on various synthetic and real time series, and quantitative and qualitative performances demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Multi-view data are becoming common in real-world applications and many multi-view clustering algorithms have thus been proposed. The existing algorithms usually focus on the cooperation of different visible views in the original space but neglect the influence of the hidden information among these visible views, or they only consider the hidden information among the views. The algorithms are therefore not efficient since the available information is not fully exploited, particularly the otherness information in different views and the consistency information among them. In practice, the otherness and consistency information in multi-view data are both very useful for effective clustering analyses. In this study, a Multi-View clustering algorithm with the Cooperation of Visible and Hidden views, i.e., MV-Co-VH, is proposed. The MV-Co-VH algorithm first projects the multiple views from different visible spaces to the common hidden space by using non-negative matrix factorization to obtain the common hidden view data. Collaborative learning is then implemented in the clustering procedure based on the visible views and the shared hidden view. The experimental results of extensive experiments on UCI multi-view datasets and real-world image multi-view datasets show that the clustering performance of the proposed algorithm is competitive with or even better than that of the existing algorithms. [ABSTRACT FROM AUTHOR]

We address the problem of biclustering on heterogeneous data, that is, data of various types (binary, numeric, symbolic, temporal). We propose a new method, HBC-t (Heterogeneous BiClustering for temporal data), designed to extract biclusters from heterogeneous, temporal, large-scale, sparse data matrices. HBC-t is based on HBC, using similar mechanisms but adding support for temporal data. The goal of this method is to handle Electronic Health Records (EHR) data gathered by hospitals on patients, stays, acts, diagnoses, prescriptions, etc.; and to provide valuable insights on this data. Temporal data accounts for a majority of the data available for this study, and in EHR in general where medical events are timestamped. Therefore, it is crucial to have an algorithm that supports this type of data. The proposed algorithm takes advantage of the data sparsity and uses a constructive greedy heuristic to build a large number of possibly overlapping biclusters. HBC-t is successfully compared with several other biclustering algorithms on numeric and temporal data. Experiments on full-scale real-life data sets further assert its scalability and efficiency. [ABSTRACT FROM AUTHOR]

Feature selection is one of the most important techniques to deal with the high-dimensional data for a variety of machine learning and data mining tasks, such clustering, classification, and retrieval, etc. Fuzziness is a widespread nature of data in nature human society. However, most existing feature selection methods ignore the existence of fuzziness in the data, resulting in sub-optimal feature subsets. To address the problem, we propose a novel unsupervised feature selection method, called Unsupervised Discriminative Projection for Feature Selection (UDPFS) to select discriminative features by conducting fuzziness learning and sparse learning, simultaneously. Specifically, we use projection matrix transform data as its low-dimensional representation, which are partitioned into clusters by using membership matrix with sparse constraint. In addition, $\ell _{2, 1}$ ℓ 2 , 1 -norm regularization is applied to the projection matrix. Then, a discriminative projection matrix with row sparse is obtained by perform fuzziness learning and sparse learning, simultaneously. An effective alternative optimization algorithm is proposed to solve the objective function. Evaluate experimental results on several real-world datasets show the effectiveness and superiority of the proposed unsupervised feature selection method. [ABSTRACT FROM AUTHOR]

Heterogeneous information network (HIN) embedding aims to learn the low-dimensional representations of nodes while preserving structures and semantics in HINs. Although most existing methods consider heterogeneous relations and achieve promising performance, they usually employ one single model for all relations without distinction, which inevitably restricts the capability of HIN embedding. In this paper, we argue that heterogeneous relations have different structural characteristics, and propose a novel Relation structure-aware HIN Embedding model, called RHINE. By exploring four real-world networks with thorough analysis, we present two structure-related measures which consistently distinguish heterogeneous relations into two categories: Affiliation Relations (ARs) and Interaction Relations (IRs). To respect the distinctive structural characteristics of relations, in RHINE, we propose different models specifically tailored to handle ARs and IRs, which can better capture the structures in HINs. Finally, we combine and optimize these models in a unified manner. Furthermore, considering that nodes connected via heterogeneous relations may have multi-aspect semantics and each relation focuses on one aspect, we introduce relation-specific projection matrices to learn node and relation embeddings in separate spaces rather than a common space, which can better preserve the semantics in HINs, referring to a new model RHINE-M. Experiments on four real-world datasets demonstrate that our models significantly outperform the state-of-the-art methods in four tasks. [ABSTRACT FROM AUTHOR]

Subgraph matching finds all embeddings from a data graph that are identical to a query graph. Recent algorithms work by generating a tree-structured index on the data graph based on the query graph, ordering the vertices path-by-path in the tree, and enumerating the embeddings following the matching order. However, we find such path-based ordering and tree-structured index based enumeration inherently limit the performance due to the lack of consideration on the edges among the vertices across tree paths. To address this problem, we propose an approach that generates the matching order based on a cost model considering both the edges among query vertices and the number of candidates. Furthermore, we create a bigraph index for candidate vertices and their selected neighbors in the data graph, and use this index to perform enumeration along the matching order. Our experiments on both real-world and synthetic datasets show that our method outperforms the state of the art by orders of magnitude. [ABSTRACT FROM AUTHOR]

Tensor factorization methods have recently gained increased popularity. A key feature that renders tensors attractive is the ability to directly model multi-relational data. In this work, we propose ParaSketch, a parallel tensor factorization algorithm that enables massive parallelism, to deal with large tensors. The idea is to compress the large tensor into multiple small tensors, decompose each small tensor in parallel, and combine the results to reconstruct the desired latent factors. Prior art in this direction entails potentially very high complexity in the (Gaussian) compression and final combining stages. Adopting sketching matrices for compression, the proposed method enjoys a dramatic reduction in compression complexity, and features a much lighter combining step. Moreover, theoretical analysis shows that the compressed tensors inherit latent identifiability under mild conditions, hence establishing correctness of the overall approach. Numerical experiments corroborate the theory and demonstrate the effectiveness of the proposed algorithm. [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]

Visualization charts are widely utilized for presenting structured data. Under many circumstances, people want to digitalize the data in the charts collected from various sources (e.g., papers and websites), in oder to further analyze the data or create new charts. However, existing automatic and semi-automatic approaches are not always effective due to the variety of charts. In this paper, we introduce a crowdsourcing approach that leverages human ability to extract data from visualization charts. There are several challenges. The first is how to avoid tedious human interaction with charts and design effective crowdsourcing tasks. Second, it is challenging to evaluate worker’s quality for truth inference, because workers may not only provide inaccurate values but also misalign values to wrong data series. Third, to guarantee quality, one may assign a task to many workers, leading to a high crowdsourcing cost. To address these challenges, we design an effective crowdsourcing task scheme that splits a chart into simple micro-tasks. We introduce a novel worker quality model by considering worker’s accuracy and task difficulty. We also devise effective task assignment and early-termination mechanisms to save the cost. We evaluate our approach on real-world datasets on real crowdsourced platforms, and the results demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Network embedding, aiming to project a network into a low-dimensional space, is increasingly becoming a focus of network research. Semi-supervised network embedding takes advantage of labeled data, and has shown promising performance. However, existing semi-supervised methods would get unappealing results in the completely-imbalanced label setting where some classes have no labeled nodes at all. To alleviate this, we propose two novel semi-supervised network embedding methods. The first one is a shallow method named RSDNE. Specifically, to benefit from the completely-imbalanced labels, RSDNE guarantees both intra-class similarity and inter-class dissimilarity in an approximate way. The other method is RECT which is a new class of graph neural networks. Different from RSDNE, to benefit from the completely-imbalanced labels, RECT explores the class-semantic knowledge. This enables RECT to handle networks with node features and multi-label setting. Experimental results on several real-world datasets demonstrate the superiority of the proposed methods. [ABSTRACT FROM AUTHOR]