The Experts below are selected from a list of 3759 Experts worldwide ranked by ideXlab platform
Zachary G Ives - One of the best experts on this subject based on the ideXlab platform.
-
parallelizing Maximal Clique and k plex enumeration over graph data
Journal of Parallel and Distributed Computing, 2017Co-Authors: Zhuo Wang, Qun Chen, Boyi Hou, Bo Suo, Wei Pan, Zachary G IvesAbstract:Abstract In a wide variety of emerging data-intensive applications, such as social network analysis, Web document clustering, entity resolution, and detection of consistently co-expressed genes in systems biology, the detection of dense subgraphs Cliques and k-plex is an essential component. Unfortunately, these problems are NP-Complete and thus computationally intensive at scale — hence there is a need to come up with techniques for distributing the computation across multiple machines such that the computation, which is too time-consuming on a single machine, can be efficiently performed on a machine cluster given that it is large enough. In this paper, we first propose a new approach for Maximal Clique and k-plex enumeration, which identifies dense subgraphs by binary graph partitioning. Given a connected graph G = ( V , E ) , it has a space complexity of O ( | E | ) and a time complexity of O ( | E | μ ( G ) ) , where μ ( G ) represents the number of different Cliques (k-plexes) existing in G . It recursively divides a graph until each task is sufficiently small to be processed in parallel. We then develop parallel solutions and demonstrate how graph partitioning can enable effective load balancing. Finally, we evaluate the performance of the proposed approach on real and synthetic graph data and show that it performs considerably better than existing approaches in both centralized and parallel settings. In the parallel setting, it can achieve the speedups of up to 10x over existing approaches on large graphs. Our parallel algorithms are primarily implemented and evaluated on MapReduce, a popular shared-nothing parallel framework, but can easily generalize to other shared-nothing or shared-memory parallel frameworks. The work presented in this paper is an extension of our preliminary work on the approach of binary graph partitioning for Maximal Clique enumeration. In this work, we extend the proposed approach to handle Maximal k-plex detection as well.
-
efficient Maximal Clique enumeration over graph data
Data Science and Engineering, 2016Co-Authors: Boyi Hou, Zhuo Wang, Qun Chen, Bo Suo, Chao Fang, Zachary G IvesAbstract:In a wide variety of emerging data-intensive applications, such as social network analysis, Web document clustering, entity resolution, and detection of consistently co-expressed genes in systems biology, the detection of dense subgraphs (Cliques) is an essential component. Unfortunately, this problem is NP-Complete and thus computationally intensive at scale—hence there is a need for efficient processing, as well as the techniques for distributing the computation across multiple machines such that the computation, which is too time-consuming on a single machine, can be efficiently performed on a machine cluster given that it is large enough. In this paper, we propose a new algorithm (called GP) for Maximal Clique enumeration. It identifies Cliques by the operation of binary graph partitioning, which iteratively divides a graph until each task is sufficiently small to be processed in parallel. Given a connected graph $$G=(V,E)$$ , the GP algorithm has a space complexity of O(|E|) and a time complexity of $$O(|E|\mu (G))$$ , where $$\mu (G)$$ represents the number of different Cliques existing in G. We also present a hybrid algorithm, which can effectively leverage the advantages of both the GP algorithm and the classical Bron-and-Kerbosch (BK) algorithm. Then, we develop corresponding parallel solutions based on the GP and hybrid algorithms. Finally, we evaluate the performance of the proposed solutions on real and synthetic graph data. Our extensive experiments show that in both centralized and parallel setting, our proposed GP and hybrid approaches achieve considerably better performance than the state-of-the-art BK approach. Our parallel solutions are implemented and evaluated on MapReduce, a popular shared-nothing parallel framework, but can easily generalize to other shared-nothing or shared-memory parallel frameworks.
-
parallelizing Maximal Clique enumeration over graph data
Database Systems for Advanced Applications, 2016Co-Authors: Qun Chen, Zhuo Wang, Bo Suo, Chao Fang, Zachary G IvesAbstract:In a wide variety of emerging data-intensive applications, such as social network analysis, Web document clustering, entity resolution, and detection of consistently co-expressed genes in systems biology, the detection of dense subgraphs Cliques is an essential component. Unfortunately, this problem is NP-Complete and thus computationally intensive at scale -- hence there is a need to come up with techniques for distributing the computation across multiple machines such that the computation, which is too time-consuming on a single machine, can be efficiently performed on a machine cluster given that it is large enough. In this paper, we first propose a new approach for Maximal Clique enumeration, which identifies Cliques by recursive graph partitioning. Given a connected graph $$G=V,E$$G=V,E, it has a space complexity of O|E| and a time complexity of $$O|E|\mu G$$O|E|μG, where $$\mu G$$μG represents the number of different Cliques existing in G. It recursively divides a graph until each task is sufficiently small to be processed in parallel. We then develop parallel solutions and demonstrate how graph partitioning can enable effective load balancing. Finally, we evaluate the performance of the proposed approach on real and synthetic graph data and show that it performs considerably better than existing approaches in both centralized and parallel settings. Our parallel algorithms are implemented and evaluated on MapReduce, a popular shared-nothing parallel framework, but can easily generalize to other shared-nothing or shared-memory parallel frameworks.
Natarajan Meghanathan - One of the best experts on this subject based on the ideXlab platform.
-
a comprehensive analysis of the correlation between Maximal Clique size and centrality metrics for complex network graphs
Egyptian Informatics Journal, 2021Co-Authors: Natarajan MeghanathanAbstract:Abstract We seek to identify one or more computationally light-weight centrality metrics that have a high correlation with that of the Maximal Clique size (the maximum size of the Clique a node is part of) - a computationally hard measure. In this pursuit, we compute three well-known measures of evaluating the correlation between two datasets: Product-moment based Pearson's correlation coefficient, Rank-based Spearman's correlation coefficient and Concordance-based Kendall's correlation coefficient. We compute the above three correlation coefficient values between the Maximal Clique size and each of the four prominent node centrality metrics (degree, eigenvector, betweenness and closeness) for random network graphsand scale-free network graphs as well as for a suite of ten real-world network graphs whose degree distribution ranges from random to scale-free. We also explore the impact of the operating parameters of the theoretical models for generating random networks and scale-free networks on the correlation between Maximal Clique size and the centrality metrics.
-
correlation analysis between Maximal Clique size and centrality metrics for random networks and scale free networks
Computer and Information Science, 2016Co-Authors: Natarajan MeghanathanAbstract:The high-level contribution of this paper is a comprehensive analysis of the correlation levels between node centrality (a computationally light-weight metric) and Maximal Clique size (a computationally hard metric) in random network and scale-free network graphs generated respectively from the well-known Erdos-Renyi (ER) and Barabasi-Albert (BA) models. We use three well-known measures for evaluating the level of correlation: Product-moment based Pearson's correlation coefficient, Rank-based Spearman's correlation coefficient and Concordance-based Kendall's correlation coefficient. For each of the several variants of the theoretical graphs generated from the ER and BA models, we compute the above three correlation coefficient values between the Maximal Clique size for a node (maximum size of the Clique the node is part of) and each of the four prominent node centrality metrics (degree, eigenvector, betweenness and closeness). We also explore the impact of the operating parameters of the theoretical models for generating random networks and scale-free networks on the correlation between Maximal Clique size and the centrality metrics.
-
Maximal Clique size versus centrality a correlation analysis for complex real world network graphs
2016Co-Authors: Natarajan MeghanathanAbstract:The paper presents the results of correlation analysis between node centrality (a computationally lightweight metric) and the Maximal Clique size (a computationally hard metric) that each node is part of in complex real-world network graphs, ranging from regular random graphs to scale-free graphs. The Maximal Clique size for a node is the size of the largest Clique (number of constituent nodes) the node is part of. The correlation coefficient between the centrality value and the Maximal Clique size for a node is observed to increase with increase in the spectral radius ratio for node degree (a measure of the variation of node degree in the network). As the real-world networks get increasingly scale-free, the correlation between the centrality value and the Maximal Clique size increases. The degree-based centrality metrics are observed to be relatively better correlated with the Maximal Clique size compared to the shortest path-based centrality metrics.
-
correlation coefficient analysis centrality vs Maximal Clique size for complex real world network graphs
International Journal of Network Science, 2015Co-Authors: Natarajan MeghanathanAbstract:The high-level contribution of this paper is correlation analysis between the centrality values observed for nodes (a computationally lightweight metric) and the Maximal Clique size (a computationally hard metric) that each node is part of in complex real-world network graphs. The real-world network graphs studied range from regular random network graphs to scale-free network graphs. The Maximal Clique size for a node is the size of the largest Clique (in terms of the number of constituent nodes) the node is part of. We observe the degree-based centrality metrics such as the degree centrality and eigenvector centrality to be relatively better correlated with the Maximal Clique size compared to the shortest path-based centrality metrics such as the closeness centrality and betweenness centrality. As the real-world networks get increasingly scale-free, we observe the correlation between the centrality value and the Maximal Clique size to increase.
-
distribution of Maximal Clique size of the vertices for theoretical small world networks and real world networks
arXiv: Social and Information Networks, 2015Co-Authors: Natarajan MeghanathanAbstract:Our primary objective in this paper is to study the distribution of the Maximal Clique size of the vertices in complex networks. We define the Maximal Clique size for a vertex as the maximum size of the Clique that the vertex is part of and such a Clique need not be the maximum size Clique for the entire network. We determine the Maximal Clique size of the vertices using a modified version of a branch-and-bound based exact algorithm that has been originally proposed to determine the maximum size Clique for an entire network graph. We then run this algorithm on two categories of complex networks: One category of networks capture the evolution of small-world networks from regular network (according to the well-known Watts-Strogatz model) and their subsequent evolution to random networks; we show that the distribution of the Maximal Clique size of the vertices follows a Poisson-style distribution at different stages of the evolution of the small-world network to a random network; on the other hand, the Maximal Clique size of the vertices is observed to be in-variant and to be very close to that of the maximum Clique size for the entire network graph as the regular network is transformed to a small-world network. The second category of complex networks studied are real-world networks (ranging from random networks to scale-free networks) and we observe the Maximal Clique size of the vertices in five of the six real-world networks to follow a Poisson-style distribution. In addition to the above case studies, we also analyze the correlation between the Maximal Clique size and clustering coefficient as well as analyze the assortativity index of the vertices with respect to Maximal Clique size and node degree.
Lihua Zhou - One of the best experts on this subject based on the ideXlab platform.
-
Mining Spatial Co-Location Patterns Based on Overlap Maximal Clique Partitioning
2019 20th IEEE International Conference on Mobile Data Management (MDM), 2019Co-Authors: Vanha Tran, Lizhen Wang, Lihua ZhouAbstract:Spatial co-location patterns are groups of spatial features whose instances are frequently located together in spatial proximity. Most existing algorithms of discovering spatial co-location patterns are based on the candidate-test model, which is computationally expensive. When the user adjusts the participation index (PI) threshold, these algorithms have to be re-executed from the size 2 co-location patterns. In this paper, we propose a novel spatial instance partition method for mining co-location patterns which called overlap Maximal Clique partitioning algorithm (OMCP). The OMCP co-location mining algorithm divides instances of an input spatial dataset into a set of overlap Maximal Cliques. Table instances of all colocation patterns are collected by the overlap Maximal Cliques. Prevalent co-location patterns are directly calculated without generating the candidate patterns. The OMCP algorithm only needs to execute once to get the PI of all patterns, without re-executing when the PI threshold is adjusted. Our algorithm is performed on both synthetic and real-world datasets to demonstrate that the OMCP algorithm improvements in efficiency of co-location pattern mining.
Qun Chen - One of the best experts on this subject based on the ideXlab platform.
-
parallelizing Maximal Clique and k plex enumeration over graph data
Journal of Parallel and Distributed Computing, 2017Co-Authors: Zhuo Wang, Qun Chen, Boyi Hou, Bo Suo, Wei Pan, Zachary G IvesAbstract:Abstract In a wide variety of emerging data-intensive applications, such as social network analysis, Web document clustering, entity resolution, and detection of consistently co-expressed genes in systems biology, the detection of dense subgraphs Cliques and k-plex is an essential component. Unfortunately, these problems are NP-Complete and thus computationally intensive at scale — hence there is a need to come up with techniques for distributing the computation across multiple machines such that the computation, which is too time-consuming on a single machine, can be efficiently performed on a machine cluster given that it is large enough. In this paper, we first propose a new approach for Maximal Clique and k-plex enumeration, which identifies dense subgraphs by binary graph partitioning. Given a connected graph G = ( V , E ) , it has a space complexity of O ( | E | ) and a time complexity of O ( | E | μ ( G ) ) , where μ ( G ) represents the number of different Cliques (k-plexes) existing in G . It recursively divides a graph until each task is sufficiently small to be processed in parallel. We then develop parallel solutions and demonstrate how graph partitioning can enable effective load balancing. Finally, we evaluate the performance of the proposed approach on real and synthetic graph data and show that it performs considerably better than existing approaches in both centralized and parallel settings. In the parallel setting, it can achieve the speedups of up to 10x over existing approaches on large graphs. Our parallel algorithms are primarily implemented and evaluated on MapReduce, a popular shared-nothing parallel framework, but can easily generalize to other shared-nothing or shared-memory parallel frameworks. The work presented in this paper is an extension of our preliminary work on the approach of binary graph partitioning for Maximal Clique enumeration. In this work, we extend the proposed approach to handle Maximal k-plex detection as well.
-
efficient Maximal Clique enumeration over graph data
Data Science and Engineering, 2016Co-Authors: Boyi Hou, Zhuo Wang, Qun Chen, Bo Suo, Chao Fang, Zachary G IvesAbstract:In a wide variety of emerging data-intensive applications, such as social network analysis, Web document clustering, entity resolution, and detection of consistently co-expressed genes in systems biology, the detection of dense subgraphs (Cliques) is an essential component. Unfortunately, this problem is NP-Complete and thus computationally intensive at scale—hence there is a need for efficient processing, as well as the techniques for distributing the computation across multiple machines such that the computation, which is too time-consuming on a single machine, can be efficiently performed on a machine cluster given that it is large enough. In this paper, we propose a new algorithm (called GP) for Maximal Clique enumeration. It identifies Cliques by the operation of binary graph partitioning, which iteratively divides a graph until each task is sufficiently small to be processed in parallel. Given a connected graph $$G=(V,E)$$ , the GP algorithm has a space complexity of O(|E|) and a time complexity of $$O(|E|\mu (G))$$ , where $$\mu (G)$$ represents the number of different Cliques existing in G. We also present a hybrid algorithm, which can effectively leverage the advantages of both the GP algorithm and the classical Bron-and-Kerbosch (BK) algorithm. Then, we develop corresponding parallel solutions based on the GP and hybrid algorithms. Finally, we evaluate the performance of the proposed solutions on real and synthetic graph data. Our extensive experiments show that in both centralized and parallel setting, our proposed GP and hybrid approaches achieve considerably better performance than the state-of-the-art BK approach. Our parallel solutions are implemented and evaluated on MapReduce, a popular shared-nothing parallel framework, but can easily generalize to other shared-nothing or shared-memory parallel frameworks.
-
parallelizing Maximal Clique enumeration over graph data
Database Systems for Advanced Applications, 2016Co-Authors: Qun Chen, Zhuo Wang, Bo Suo, Chao Fang, Zachary G IvesAbstract:In a wide variety of emerging data-intensive applications, such as social network analysis, Web document clustering, entity resolution, and detection of consistently co-expressed genes in systems biology, the detection of dense subgraphs Cliques is an essential component. Unfortunately, this problem is NP-Complete and thus computationally intensive at scale -- hence there is a need to come up with techniques for distributing the computation across multiple machines such that the computation, which is too time-consuming on a single machine, can be efficiently performed on a machine cluster given that it is large enough. In this paper, we first propose a new approach for Maximal Clique enumeration, which identifies Cliques by recursive graph partitioning. Given a connected graph $$G=V,E$$G=V,E, it has a space complexity of O|E| and a time complexity of $$O|E|\mu G$$O|E|μG, where $$\mu G$$μG represents the number of different Cliques existing in G. It recursively divides a graph until each task is sufficiently small to be processed in parallel. We then develop parallel solutions and demonstrate how graph partitioning can enable effective load balancing. Finally, we evaluate the performance of the proposed approach on real and synthetic graph data and show that it performs considerably better than existing approaches in both centralized and parallel settings. Our parallel algorithms are implemented and evaluated on MapReduce, a popular shared-nothing parallel framework, but can easily generalize to other shared-nothing or shared-memory parallel frameworks.
Mengchi Liu - One of the best experts on this subject based on the ideXlab platform.
-
a memory efficient Maximal Clique enumeration method for sparse graphs with a parallel implementation
Parallel Computing, 2019Co-Authors: Mengchi LiuAbstract:Abstract Maximal Clique enumeration (MCE) is a widely studied problem that plays a crucial role in structure mining of undirected graphs. The increasing scale of real-world graphs has brought the challenges of high memory cost and high CPU workload to the problem. In this paper, we propose a memory efficient method named CMC-bit for MCE on sparse graphs. It reduces the memory cost via minimizing the candidate Cliques and representing them by the data structure bitset. It generates an appropriate order for the vertex set according to two optimized principles to reduce the CPU cost. We further design a partition-based CMC-bit algorithm with a one-side extending strategy to solve the memory-limited problem. We parallelize the CMC-bit algorithm based on MapReduce with a range-based partition strategy to make an optimal trade-off between the shuffling workload of graph decomposition and load balance in the Reduce phase. We conduct extensive experiments on 30 real-world datasets. The results demonstrate that both the CMC-bit algorithm and its parallel implementation significantly outperform the respective state-of-the-art algorithms in speed. We also show that the parallel CMC-bit algorithm achieves good performance on the scalability with respect to both the reducer number and the CPU number.
-
a linear time algorithm for Maximal Clique enumeration in large sparse graphs
Information Processing Letters, 2017Co-Authors: Mengchi LiuAbstract:Abstract A Maximal Clique is one of the most fundamental dense substructures in an undirected graph, and Maximal Clique enumeration (MCE) plays an essential role in densely connected subgraphs discovering. Existing algorithms of Maximal Clique enumeration employ recursive iteration of adjacent nodes as guiding thought, which incurs high time complexity. In this paper, we propose a linear time algorithm, CM-Constructor (Candidate Map Constructor), for Maximal Clique enumeration in large sparse graphs which generates a novel data structure called candidate map as result. A candidate map holds not only all Maximal Cliques of an undirected graph but also some non-Maximal Cliques that can be easily discarded via an inverted Clique tree. To the best of our knowledge, CM-Constructor is the first algorithm to tackle Maximal Clique enumeration problem utilizing linear procedure. It generates all Maximal Cliques without duplications for an undirected graph G = ( V , E ) within O ( | E | ) time.