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Graphs have been widely used in many applications. One important graph analytics is to explore cohesive subgraphs in a large graph. Among several cohesive subgraphs studied, $k$-core is one that can be computed in linear time for a static graph. Since graphs are evolving in real applications, in this paper, we study core maintenance which is to reduce the computational cost to compute $k$-cores for a graph when graphs are updated from time to time dynamically. We identify drawbacks of the existing efficient algorithm, which needs a large search space to find the vertices that need to be updated, and has high overhead to maintain the index built, when a graph is updated. We propose a new order-based approach to maintain an order, called $k$-order, among vertices, while a graph is updated. Our new algorithm can significantly outperform the state-of-the-art algorithm up to 3 orders of magnitude for the 11 large real graphs tested. In addition, we also study the problem of partial core maintenance, which is to maintain the top-$t$ cores of the graph for a given positive integer $t$. By instead maintaining only a small subset of cores, further improvement in performance can be obtained.

IEEE Keyword search problem has been widely studied to retrieve relevant substructures from graphs. However, existing approaches aim at finding compact trees/subgraphs containing the keywords, and ignore density to evaluate how strongly and stablely the keyword nodes are connected. In this paper, we study the problem of finding cohesive subgraph containing query keywords with high density and compactness, and formulate it as minimal dense truss search problem based on k-truss model. However, unlike ${k}$-truss based community search that can be efficiently done by local search from a given set of nodes, this problem is nontrivial as the keyword nodes to be included in the retrieved substructure is previously unknown. To tackle this problem, we first design a novel hybrid KT-Index that keeps the keyword and truss information compactly to support efficient search of dense truss with the maximum trussness ${G_{den}}$. Then, we develop a novel refinement approach to extract minimal dense truss from ${G_{den}}$, by checking each node at most once based on the anti-monotonicity property of k-truss, together with several optimization strategies including batch based deletion, early-stop based deletion, and local exploration. Extensive experimental studies on real-world networks validated the effectiveness and efficiency of our approaches.

© 2020, Springer Nature Switzerland AG. Furthest Neighbor search in high-dimensional space has been widely used in many applications such as recommendation systems. Because of the “curse of dimensionality” problem, c-approximate furthest neighbor (C-AFN) is a substitute as a trade-off between result accuracy and efficiency. However, most of the current techniques for external memory are only suitable for low-dimensional space. In this paper, we propose a novel algorithm called reverse incremental LSH based on Indyk’s LSH scheme to solve the problem with theoretical guarantee. Unlike the previous methods using hashing scheme, reverse incremental LSH (RI-LSH) is designed for external memory and can achieve a good performance on I/O cost. We provide rigorous theoretical analysis to prove that RI-LSH can return a-AFN result with a constant possibility. Our comprehensive experiment results show that, compared with other-AFN methods with theoretical guarantee, our algorithm can achieve better I/O efficiency.

© 1989-2012 IEEE. Given a set of objects O (such as hotels), each can be represented as a point in a multi-dimensional feature space where each dimension corresponds to one attribute of the objects (such as price). Given the preference of a customer, the objects in O not dominated by any other object (i.e., beat in all dimensions) are those worthy to be further considered. Such objects are known as skyline objects in database community. Suppose we have an object o\in O-O. If o is a skyline point, other skyline objects are called peers of o. If o is not a skyline object, it must be dominated by some skyline objects which are called dominators of o. Given a large number of user preferences, an interesting problem is to identify the most frequent peer/dominator (MFP/MFD) of o. The MFP/MFD search has unique values in competitor analysis of various information systems. However, it is a challenging task because of the complexity to process a large number of user preferences. In this work, we provide robust solutions including exact and approximate methods. While the exact solutions explore the dominance relationship in the feature space, the approximate solutions are based on sampling techniques with theoretical bounds. We did extensive tests on large data sets which are up to 100 million user preferences generated from commercial surveys. The test resutls demonstrate the exact algorithms outperform various baseline algorithms significantly, and the approximate algorithms make further improvement by one order of magnitude with 90-98 percent accuracy.

© 2019 Association for Computing Machinery. In many applications, graphs often involve the nodes with multi-dimensional numerical attributes, and it is desirable to retrieve a group of nodes that are both highly connected (e.g., clique) and optimal according to some ranking functions. It is well known that the skyline returns candidates for the optimal objects when ranking functions are not specified. Motivated by this, in this paper we formulate the novel model of skyline k-cliques over multi-valued attributed graphs and develop efficient algorithms to conduct the computation. To verify the group based dominance between two k-cliques, we make use of maximum bipartite matching and develop a set of optimization techniques to improve the verification efficiency. Then, a progressive computation algorithm is developed which enumerates the k-cliques in an order such that a k-clique is guaranteed not to be dominated by those generated after it. Novel pruning and early termination techniques are developed to exclude unpromising nodes or cliques by investigating the structural and attribute properties of the multi-valued attributed graph. Empirical studies on four real datasets demonstrate the effectiveness of the skyline k-clique model and the efficiency of the novel computing techniques.

© 1989-2012 IEEE. Hypergraphs are generalizations of graphs where the (hyper)edges can connect any number of vertices. They are powerful tools for representing complex and non-pairwise relationships. However, existing graph computation frameworks cannot accommodate hypergraphs without converting them into graphs, because they do not offer APIs that support (hyper)edges directly. This graph conversion may create excessive replicas and result in very large graphs, causing difficulties in workload balancing. A few tools have been developed for hypergraph partitioning, but they are not general-purpose frameworks for hypergraph processing. In this paper, we propose HyperX, a general-purpose distributed hypergraph processing framework built on top of Spark. HyperX is based on the computation paradigm Pregel, which is user-friendly and has been widely adopted by popular graph computation frameworks. To help create balanced workloads for distributed hypergraph processing, we further investigate the hypergraph partitioning problem and propose a novel label propagation partitioning (LPP) algorithm. We conduct extensive experiments using both real and synthetic data. The result shows that HyperX achieves an order of magnitude improvement for running hypergraph learning algorithms compared with graph conversion based approaches in terms of running time, network communication costs, and memory consumption. For hypergraph partitioning, LPP outperforms the baseline algorithms significantly in these measures as well.

© 1989-2012 IEEE. Core decomposition is a fundamental graph problem with a large number of applications. Most existing approaches for core decomposition assume that the graph is kept in memory of a machine. Nevertheless, many real-world graphs are too big to reside in memory. In this paper, we study I/O efficient core decomposition following a semi-external model, which only allows node information to be loaded in memory. We propose a semi-external algorithm and an optimized algorithm for I/O efficient core decomposition. To handle dynamic graph updates, we firstly show that our algorithm can be naturally extended to handle edge deletion. Then, we propose an I/O efficient core maintenance algorithm to handle edge insertion, and an improved algorithm to further reduce I/O and CPU cost. In addition, based on our core decomposition algorithms, we further propose an I/O efficient semi-external algorithm for degeneracy ordering, which is an important graph problem that is highly related to core decomposition. We also consider how to maintain the degeneracy order. We conduct extensive experiments on 12 real large graphs. Our optimal core decomposition algorithm significantly outperforms the existing I/O efficient algorithm in terms of both processing time and memory consumption. They are very scalable to handle web-scale graphs. As an example, we are the first to handle a web graph with 978.5 million nodes and 42.6 billion edges using less than 4.2 GB memory. We also show that our proposed algorithms for degeneracy order computation and maintenance can handle big graphs efficiently with small memory overhead.

© 1989-2012 IEEE. Supergraph search is a fundamental problem in graph databases that is widely applied in many application scenarios. Given a graph database and a query-graph, supergraph search retrieves all data-graphs contained in the query-graph from the graph database. Most existing solutions for supergraph search follow the pruning-and-verification framework, which prune false answers based on features in the pruning phase and perform subgraph isomorphism testings on the remaining graphs in the verification phase. However, they are not scalable to handle large-sized data-graphs and query-graphs due to three drawbacks. First, they rely on a frequent subgraph mining algorithm to select features which is expensive and cannot generate large features. Second, they require a costly verification phase. Third, they process features in a fixed order without considering their relationships to the query-graph. In this paper, we address the three drawbacks and propose new indexing and query processing algorithms. In indexing, we select features directly from the data-graphs without expensive frequent subgraph mining. The features form a feature-tree that contains all-sized features and both the cost sharing and pruning power of the features are considered. In query processing, we propose a new algorithm, where the order to process features is query-dependent by considering both the cost sharing and the pruning power. We explore two optimization strategies to further improve the algorithm efficiency. The first strategy applies a lightweight graph compression technique and the second strategy optimizes the inclusion of answers. We further introduce how to efficiently maintain the index incrementally when the graph database is updated dynamically. Moreover, we propose an approximation approach to significantly reduce the computational cost for large data-graphs and/or query-graphs while preserving a high result quality. Finally, we conduct extensive performance studies on two

© 1989-2012 IEEE. Real social networks contain many communities, where members within each community are densely connected with each other, while they are sparsely connected with the members outside of the community. Since each member can join multiple communities simultaneously, communities in social networks are usually overlapping with each other. How to efficiently and effectively identify overlapping communities in a large social network becomes a fundamental problem in the big data era. Most existing studies on community finding focused on non-overlapping communities based on several well-known community fitness metrics. However, recent investigations have shown that these fitness metrics may suffer free rider and separation effects where the overlapping region of two communities always belongs to the denser one, rather to both of them. In this paper, we study the overlapping community detection problem in social networks that not only takes the quality of the found overlapping communities but also incorporate both free rider and separation effects on the found communities into consideration. Specifically, in this paper, we first propose a novel community fitness metric - triangle based fitness metric, for overlapping community detection that can minimize the free rider and separation effects on found overlapping communities, and show that the problem is NP-hard. We then propose an efficient yet scalable algorithm for the problem that can deliver a feasible solution. We finally validate the effectiveness of the proposed fitness metric and evaluate the performance of the proposed algorithm, through conducting extensive experiments on real-world datasets with over 100 million vertices and edges. Experimental results demonstrate that the proposed algorithm is very promising.

© 2018 IEEE. Keyword search problem has been widely studied to retrieve related substructures from graphs for a keyword set. However, existing well-studied approaches aim at finding compact trees/subgraphs containing the keywords, and ignore a critical measure, density, to reflect how strongly and stablely the keyword nodes are connected in the substructure. In this paper, we study the problem of finding a cohesive subgraph containing the query keywords based on the k-Truss model, and formulate it as minimal dense truss search problem, i.e., finding minimal subgraph with maximum trussness covering the keywords. We first propose an efficient algorithm to find the dense truss with the maximum trussness containing keywords based on a novel hybrid KT-Index (Keyword-Truss Index). Then, we develop a novel refinement approach to extract the minimal dense truss based on the anti-monotonicity property of k-Truss. Experimental studies on real datasets show the outperformance of our method.

Background Aligning protein-protein interaction (PPI) networks is very important to discover the functionally conserved sub-structures between different species. In recent years, the global PPI network alignment problem has been extensively studied aiming at finding the one-to-one alignment with the maximum matching score. However, finding large conserved components remains challenging due to its NP-hardness. Results We propose a new graph matching method GMAlign for global PPI network alignment. It first selects some pairs of important proteins as seeds, followed by a gradual expansion to obtain an initial matching, and then it refines the current result to obtain an optimal alignment result iteratively based on the vertex cover. We compare GMAlign with the state-of-the-art methods on the PPI network pairs obtained from the largest BioGRID dataset and validate its performance. The results show that our algorithm can produce larger size of alignment, and can find bigger and denser common connected subgraphs as well for the first time. Meanwhile, GMAlign can achieve high quality biological results, as measured by functional consistency and semantic similarity of the Gene Ontology terms. Moreover, we also show that GMAlign can achieve better results which are structurally and biologically meaningful in the detection of large conserved biological pathways between species. Conclusions GMAlign is a novel global network alignment tool to discover large conserved functional components between PPI networks. It also has many potential biological applications such as conserved pathway and protein complex discovery across species. The GMAlign software and datasets are avaialbile at https://github.com/yzlwhu/GMAlign

© 2018 Association for Computing Machinery. In this paper, we focus on accelerating a widely employed computing pattern-set intersection, to boost a group of graph algorithms. Graph's adjacency-lists can be naturally considered as node sets, thus set intersection is a primitive operation in many graph algorithms. We propose QFilter, a set intersection algorithm using SIMD instructions. QFilter adopts a merge-based framework and compares two blocks of elements iteratively by SIMD instructions. The key insight for our improvement is that we quickly filter out most of unnecessary comparisons in one byte-checking step. We also present a binary representation called BSR that encodes sets in a compact layout. By combining QFilter and BSR, we achieve dataparallelism in two levels-inter-chunk and intra-chunk parallelism. Moreover, we find that node ordering impacts the performance of intersection by affecting the compactness of BSR.We formulate the graph reordering problem as an optimization of the compactness of BSR, and prove its strong NP-completeness. Thus we propose an approximate algorithm that can find a better ordering to enhance the intra-chunk parallelism. We conduct extensive experiments to confirm that our approach can improve the performance of set intersection in graph algorithms significantly.

© 1989-2012 IEEE. RDF question/answering (Q/A) allows users to ask questions in natural languages over a knowledge base represented by RDF. To answer a natural language question, the existing work takes a two-stage approach: question understanding and query evaluation. Their focus is on question understanding to deal with the disambiguation of the natural language phrases. The most common technique is the joint disambiguation, which has the exponential search space. In this paper, we propose a systematic framework to answer natural language questions over RDF repository (RDF Q/A) from a graph data-driven perspective. We propose a semantic query graph to model the query intention in the natural language question in a structural way, based on which, RDF Q/A is reduced to subgraph matching problem. More importantly, we resolve the ambiguity of natural language questions at the time when matches of query are found. The cost of disambiguation is saved if there are no matching found. More specifically, we propose two different frameworks to build the semantic query graph, one is relation (edge)-first and the other one is node-first. We compare our method with some state-of-the-art RDF Q/A systems in the benchmark dataset. Extensive experiments confirm that our method not only improves the precision but also speeds up query performance greatly.

© Springer International Publishing AG, part of Springer Nature 2018. An online social network can be used for the diffusion of malicious information like derogatory rumors, disinformation, hate speech, revenge pornography, etc. This motivates the study of influence minimization that aim to prevent the spread of malicious information. Unlike previous influence minimization work, this study considers the influence minimization in relation to a particular group of social network users, called targeted influence minimization. Thus, the objective is to protect a set of users, called target nodes, from malicious information originating from another set of users, called active nodes. This study also addresses two fundamental, but largely ignored, issues in different influence minimization problems: (i) the impact of a budget on the solution; (ii) robust sampling. To this end, two scenarios are investigated, namely unconstrained and constrained budget. Given an unconstrained budget, we provide an optimal solution; Given a constrained budget, we show the problem is NP-hard and develop a greedy algorithm with an (1 - 1 / e) -approximation. More importantly, in order to solve the influence minimization problem in large, real-world social networks, we propose a robust sampling-based solution with a desirable theoretic bound. Extensive experiments using real social network datasets offer insight into the effectiveness and efficiency of the proposed solutions.

© 1989-2012 IEEE. SimRank is an important measure of vertex-pair similarity according to the structure of graphs. Although progress has been achieved, existing methods still face challenges to handle large graphs. Besides huge index construction and maintenance cost, existing methods may require considerable search space and time overheads in the online SimRank query. In this paper, we design a Monte Carlo based method, UniWalk, to enable the fast top- $k$ SimRank computation over large undirected graphs. UniWalk directly locates the top- $k$ similar vertices for any single source vertex $u$ via $R$ sampling paths originating from $u$ , which avoids selecting candidate vertex set $\mathcal{C}$ and the following $O(|\mathcal{C}|R)$ bidirectional sampling paths. We also devise a path enumeration strategy to improve the SimRank precision by using path probabilities instead of path frequencies when sampling, a space-efficient method to reduce intermediate results, and a path-sharing strategy to lower the redundant path sampling cost for multiple source vertices. Furthermore, we extend UniWalk to existing distributed graph processing frameworks to improve its scalability. We conduct extensive experiments to illustrate that UniWalk has high scalability, and outperforms the state-of-the-art methods by orders of magnitude.

© 2018 Association for Computing Machinery. Given a scientific collaboration network, how can we find a group of collaborators with high research indicator (e.g., hindex) and diverse research interests? Given a social network, how can we identify the communities that have high influence (e.g., PageRank) and also have similar interests to a specified user? In such settings, the network can be modeled as a multi-valued network where each node has d (d = 1) numerical attributes (i.e., h-index, diversity, PageRank, similarity score, etc.). In the multi-valued network, we want to find communities that are not dominated by the other communities in terms of d numerical attributes. Most existing community search algorithms either completely ignore the numerical attributes or only consider one numerical attribute of the nodes. To capture d numerical attributes, we propose a novel community model, called skyline community, based on the concepts of k-core and skyline. A skyline community is a maximal connected k-core that cannot be dominated by the other connected k-cores in the d-dimensional attribute space. We develop an elegant space-partition algorithm to efficiently compute the skyline communities. Two striking advantages of our algorithm are that (1) its time complexity relies mainly on the size of the answer s (i.e., the number of skyline communities), thus it is very efficient if s is small; and (2) it can progressively output the skyline communities, which is very useful for applications that only require part of the skyline communities. Extensive experiments on both synthetic and real-world networks demonstrate the efficiency, scalability, and effectiveness of the proposed algorithm.

© 2018 Association for Computing Machinery. Motivated by the applicability of HogWild!-style algorithms, people turn their focus on system architectures that provide ultra-high throughput random-access with very limited or no isolation guarantees, and build inconsistent-tolerant applications (i.e., large scale optimization algorithms) on top of them. Although some optimization algorithms have theoretical convergence guarantees, sometimes these systems fail to compute the correct results when the presumptions of convergence cannot hold. Moreover, there is no practical way to tell whether a given result is accurate (without cross validation) or to tune the isolation strength on-the-fly. To resolve these problems, these systems need an indicator to report the number of lbad eventž caused by lout-of-orderž executions. In this paper, we tackle this problem. Based on transaction processing theory, we find the number of cycles in the dependency graph, and demonstrate it is a good indicator. With this observation, we propose the first real-time isolation anomalies monitor. Our monitor is at least 1000x faster than naive implementations and reports accurate isolation anomalies levels with less than 1% extra overhead. Monitoring anomalies in a real-time manner efficiently protects the systems from excessive isolation anomalies which could lead to incorrect results. We verify the performance and effectiveness of our monitor via extensive experimental studies.

© 2017, Springer-Verlag Berlin Heidelberg. Reachability query is a fundamental graph operation which answers whether a vertex can reach another vertex over a large directed graph G with n vertices and m edges and has been extensively studied. In the literature, all the approaches compute a label for every vertex in a graph G by index construction offline. The query time for answering reachability queries online is affected by the quality of the labels computed in index construction. The three main costs are the index construction time, the index size, and the query time. Some of the up-to-date approaches can answer reachability queries efficiently, but spend nonlinear time to construct an index. Some of the up-to-date approaches construct an index in linear time and space, but may need to depth-first search G at run-time in O(n+ m). In this paper, we discuss a new randomized labeling approach, named IP label, to answer reachability queries with probability guarantee, and the randomness is by independent permutation. Two additional labels are also proposed to further enhance the query processing. In addition, to deal with dynamic graphs, we discuss the label maintenance over dynamic graphs and give efficient algorithms for the labels proposed. We conduct extensive experimental studies to compare with the up-to-date approaches using 19 large real datasets used in the existing work and synthetic datasets. We confirm the efficiency and scalability of our approach in static graphs testing, and our maintenance algorithms are about one order of magnitude faster than the existing ones in dynamic graphs testing.

© 2018, Higher Education Press and Springer-Verlag GmbH Germany. Indexing is one of the most important techniques to facilitate query processing over a multi-dimensional dataset. A commonly used strategy for such indexing is to keep the tree-structured index balanced. This strategy reduces query processing cost in the worst case, and can handle all different queries equally well. In other words, this strategy implies that all queries are uniformly issued, which is partially because the query distribution is not possibly known and will change over time in practice. A key issue we study in this work is whether it is the best to fully rely on a balanced tree-structured index in particular when datasets become larger and larger in the big data era. This means that, when a dataset becomes very large, it becomes unreasonable to assume that all data in any subspace are equally important and are uniformly accessed by all queries at the index level. Given the existence of query skew and the possible changes of query skew, in this paper, we study how to handle such query skew and such query skew changes at the index level without sacrifice of supporting any possible queries in a wellbalanced tree index and without a high overhead. To tackle the issue, we propose index-view at the index level, where an index-view is a short-cut in a balanced tree-structured index to access objects in the subspaces that are more frequently accessed, and propose a new index-view-centric framework for query processing using index-views in a bottom-up manner. We study index-views selection problem in both static and dynamic setting, and we confirm the effectiveness of our approach using large real and synthetic datasets.

© 2017 IEEE. Triadic closure is ubiquitous in social networks, which refers to the property among three individuals, A, B, and C, such thatif there exist strong ties between A-B and A-C, then there must be a strong or weak tie between B-C. Related to triadic closure, thenumber of triangles has been extensively studied since it can be effectively used as a metric to analyze the structure and function of anetwork. In this paper, from a different viewpoint, we study triangle-free dense structures which have received little attention. We focuson K3;3 where there are two subsets of three vertices, a vertex in a subset has an edge connected to every vertex in another subsetwhile it does not have an edge to any other vertex in the same subset. Such Kn;n in general implies a philosophy contradiction: (a) Anytwo individuals are friends if they have no common friends, and (b) Any two individuals are not friends if they have common friends.However, we find such induced K3;3 does exist frequently, and they do not disappear over time over a real academic collaborationnetwork. In addition, in the real datasets tested, nearly all edges appearing in K3;3 appear in some triangles. We analyze the expectednumbers of induced K3;3 and triangles (D) in four representative random graph models, namely, Erdos-Renyi random graph model,Watts-Strogatz small-world model, Barabasi-Albert preferential attachment model, and configuration model, and give an algorithm toenumerate all distinct K3;3 in an undirected social network. We conduct extensive experiments on both real and synthetic datasets toconfirm our findings. As an application, such K3;3 found helps to find new stars collaborated by well-known figures who themselves donot collaborate.

© 2017, Springer-Verlag Berlin Heidelberg. A broad class of data, ranging from similarity networks, workflow networks to protein networks, can be modeled as graphs with data values as vertex labels. Both vertex labels and neighbors could be dirty for various reasons such as typos or erroneous reporting of results in scientific experiments. Neighborhood constraints, specifying label pairs that are allowed to appear on adjacent vertices in the graph, are employed to detect and repair erroneous vertex labels and neighbors. In this paper, we study the problem of repairing vertex labels and neighbors to make graphs satisfy neighborhood constraints. Unfortunately, the problem is generally hard, which motivates us to devise approximation methods for repairing and identify interesting special cases (star and clique constraints) that can be efficiently solved. First, we propose several label repairing approximation algorithms including greedy heuristics, contraction method and an approach combining both. The performances of algorithms are also analyzed for the special case. Moreover, we devise a cubic-time constant-factor graph repairing algorithm with both label and neighbor repairs (given degree-bounded instance graphs). Our extensive experimental evaluation on real data demonstrates the effectiveness of eliminating frauds in several types of application networks.

© Springer International Publishing AG 2017. Graph is a powerful tool to model interactions in disparate applications, and how to assess the structure of a graph is an essential task across all the domains. As a classic measure to characterize the connectivity of graphs, clustering coefficient and its variants are of particular interest in graph structural analysis. However, the largest of today’s graphs may have nodes and edges in billion scale, which makes the simple task of computing clustering coefficients quite complicated and expensive. Thus, approximate solutions have attracted much attention from researchers recently. However, they only target global and binned degree wise clustering coefficient estimation, and their techniques are not suitable for local clustering coefficient estimation that is of great importance for individual nodes. In this paper, we propose a new sampling scheme to estimate the local clustering coefficient with error bounded, where global and binned degree-wise clustering coefficients can be considered as special cases. Meanwhile, based on our sampling scheme, we propose a new framework to estimate all the three clustering coefficients in a unified way. To make it scalable on massive graphs, we further design an efficient MapReduce algorithm under this framework. Extensive experiments validate the efficiency and effectiveness of our algorithms, which significantly outperform state-of-the-art exact and approximate algorithms on many real graph datasets.

© 1989-2012 IEEE. Graph keyword search has drawn many research interests, since graph models can generally represent both structured and unstructured databases and keyword searches can extract valuable information for users without the knowledge of the underlying schema and query language. In practice, data graphs can be extremely large, e.g., a Web-scale graph containing billions of vertices. The state-of-the-art approaches employ centralized algorithms to process graph keyword searches, and thus they are infeasible for such large graphs, due to the limited computational power and storage space of a centralized server. To address this problem, we investigate keyword search for Web-scale graphs deployed in a distributed environment. We first give a naive search algorithm to answer the query efficiently. However, the naive search algorithm uses a flooding search strategy that incurs large time and network overhead. To remedy this shortcoming, we then propose a signature-based search algorithm. Specifically, we design a vertex signature that encodes the shortest-path distance from a vertex to any given keyword in the graph. As a result, we can find query answers by exploring fewer paths, so that the time and communication costs are low. Moreover, we reorganize the graph data in the cluster after its initial random partitioning so that the signature-based techniques are more effective. Finally, our experimental results demonstrate the feasibility of our proposed approach in performing keyword searches over Web-scale graph data.

© 2017 Association for Computing Machinery. The ever-increasing knowledge graphs impose an urgent demand of providing effective and easy-to-use query techniques for end users. Structured query languages, such as SPARQL, offer a powerful expression ability to query RDF datasets. However, they are difficult to use. Keywords are simple but have a very limited expression ability. Natural language question (NLQ) is promising on querying knowledge graphs. A huge challenge is how to understand the question clearly so as to translate the unstructured question into a structured query. In this paper, we present a data + oracle approach to answer NLQs over knowledge graphs. We let users verify the ambiguities during the query understanding. To reduce the interaction cost, we formalize an interaction problem and design an efficient strategy to solve the problem. We also propose a query prefetch technique by exploiting the latency in the interactions with users. Extensive experiments over the QALD dataset demonstrate that our proposed approach is effective as it outperforms state-of-the-art methods in terms of both precision and recall.

© Springer International Publishing AG 2017. Extracting keyphrases from documents automatically is an important and interesting task since keyphrases provide a quick summarization for documents. Although lots of efforts have been made on keyphrase extraction, most of the existing methods (the co-occurrence based methods and the statistic-based methods) do not take semantics into full consideration. The co-occurrence based methods heavily depend on the co-occurrence relations between two words in the input document, which may ignore many semantic relations. The statistic-based methods exploit the external text corpus to enrich the document, which introduces more unrelated relations inevitably. In this paper, we propose a novel approach to extract keyphrases using knowledge graphs, based on which we could detect the latent relations of two keyterms (i.e., noun words and named entities) without introducing many noises. Extensive experiments over real data show that our method outperforms the state-of-art methods including the graph-based co-occurrence methods and statistic-based clustering methods.

© 2017, Springer International Publishing AG. Extracting keyphrases from documents for providing a quick and insightful summarization is an interesting and important task, on which lots of research efforts have been laid. Most of the existing methods could be categorized as co-occurrence based, statistic-based, or semantics-based. The co-occurrence based methods do not take various word relations besides co-occurrence into full consideration. The statistic-based methods introduce more unrelated noises inevitably due to the inclusion of external text corpus, while the semantic-based methods heavily depend on the semantic meanings of words. In this paper, we propose a novel graph-based approach to extract keyphrases by considering heterogeneous latent word relations (the co-occurrence and the semantics). The underlying random walk model behind the proposed approach is made possible and reasonable by exploiting nearest neighbor documents. Extensive experiments over real data show that our method outperforms the state-of-art methods.

Matching dependencies (MDS) have recently been proposed to make data dependencies tolerant to various information representations, and found useful in data quality applications such as record matching. Instead of the strict equality function used in traditional dependency syntax (e.g., functional dependencies), MDS specify constraints based on similarity and identification.However, in practice, MDS may still be too strict and applicable only in a subset of tuples in a relation. Thereby, we study the conditional matching dependencies (CMDS), which bindmatching dependencies only in a certain part of a table, i.e., MDS conditionally applicable in a subset of tuples. Compared to MDS, CMDS have more expressive power that enables them to satisfy wider application needs. In this article, we study several important theoretical and practical issues of CMDS, including irreducible CMDS with respect to the implication, discovery of CMDS from data, reliable CMDS agreed most by a relation, approximate CMDS almost satisfied in a relation, and finally applications of CMDS in record matching and missing value repairing. Through an extensive experimental evaluation in real data sets, we demonstrate the efficiency of proposed CMDS discovery algorithms and effectiveness of CMDS in real applications.

© 2017 IEEE. Recent studies show that individuals in a social network can be divided into different groups of densely connected communities, and these individuals who bridge different communities, referred to as structural hole spanners, have great potential to acquire resources/information from communities and thus benefit from the access. Structural hole spanners are crucial in many real applications such as community detections, diffusion controls, viral marketing, etc. In spite of their importance, little attention has been paid to them. Particularly, how to accurately characterize the structural hole spanners and how to devise efficient yet scalable algorithms to find them in a large social network are fundamental issues. In this paper, we study the top-k structural hole spanner problem. We first provide a novel model to measure the quality of structural hole spanners through exploiting the structural hole spanner properties. Due to its NP-hardness, we then devise two efficient yet scalable algorithms, by developing innovative filtering techniques that can filter out unlikely solutions as quickly as possible, while the proposed techniques are built up on fast estimations of the upper and lower bounds on the cost of an optimal solution and make use of articulation points in real social networks. We finally conduct extensive experiments to validate the effectiveness of the proposed model, and to evaluate the performance of the proposed algorithms using real world datasets. The experimental results demonstrate that the proposed model can capture the characteristics of structural hole spanners accurately, and the structural hole spanners found by the proposed algorithms are much better than those by existing algorithms in all considered social networks, while the running times of the proposed algorithms are very fast.

© 2016, Springer Science+Business Media New York. Representative skyline computation is a fundamental issue in database area, which has attracted much attention in recent years. A notable definition of representative skyline is the distance-based representative skyline (DBRS). Given an integer k, a DBRS includes k representative skyline points that aims at minimizing the maximal distance between a non-representative skyline point and its nearest representative. In the 2D space, the state-of-the-art algorithm to compute the DBRS is based on dynamic programming (DP) which takes O(km2) time complexity, where m is the number of skyline points. Clearly, such a DP-based algorithm cannot be used for handling large scale datasets due to the quadratic time cost. To overcome this problem, in this paper, we propose a new approximate algorithm called ARS, and a new exact algorithm named PSRS, based on a carefully-designed parametric search technique. We show that the ARS algorithm can guarantee a solution that is at mostlarger than the optimal solution. The proposed ARS and PSRS algorithms run in and O(k2 log3m) time respectively, where T is no more than the maximal distance between any two skyline points. We also propose an improved exact algorithm, called PSRS+, based on an effective lower and upper bounding technique. We conduct extensive experimental studies over both synthetic and real-world datasets, and the results demonstrate the efficiency and effectiveness of the proposed algorithms.

© 2017, Springer-Verlag Berlin Heidelberg. Multiple pattern detection is needed in applications like disease analysis over gene networks, bug detection in program flow networks. This paper takes pattern detection to investigate the evaluation and optimization of multiple jobs in existing distributed graph processing frameworks. The evaluation plan for multiple pattern detection should be parallelizable and can capture and reuse the shared parts among pattern queries easily. In this paper, we design a path-based holistic plan for multiple pattern queries. Specifically, (1) we design a path-based edge-covered plan for an individual pattern. The paths in the plan can be easily captured and reused among different queries. Additionally, the evaluation plan is fully parallelizable, in which each data vertex performs necessary join operations independently during exploring graph. (2) We extend the individual plan to a holistic evaluation plan for multiple queries, whose results are equivalent to those of individual queries. The plan reduces the overall cost by finding frequent paths among queries and reusing the shared part in the holistic plan. (3) We devise various optimization strategies over the holistic plan. The experimental studies, conducted on Giraph, illustrate the high effectiveness of our holistic approaches.

© 2017 IEEE. Detecting social communities in large social networks provides an effective way to analyze the social media users' behaviors and activities. It has drawn extensive attention from both academia and industry. One essential aspect of communities in social networks is outer influence which is the capability to spread internal information of communities to external users. Detecting the communities of high outer influence has particular interest in a wide range of applications, e.g., Ads trending analytics, social opinion mining and news propagation pattern discovery. However, the existing detection techniques largely ignore the outer influence of the communities. To fill the gap, this work investigates the Most Influential Community Search problem to disclose the communities with the highest outer influences. We firstly propose a new community model, maximal kr-Clique community, which has desirable properties, i.e., society, cohesiveness, connectivity, and maximum. Then, we design a novel tree-based index structure, denoted as C-Tree, to maintain the offline computed r-cliques. To efficiently search the most influential communities, we also develop four advanced index-based algorithms which improve the search performance of non-indexed solution by about 200 times. The efficiency and effectiveness of our solution have been extensively verified using six real datasets and a small case study.