Querying k-truss community in large and dynamic graphs

Huang, Xin; Cheng, Hong; Qin, Lu; Tian, Wentao; Yu, Jeffrey Xu

doi:10.1145/2588555.2610495

Cited by 388 publications

(289 citation statements)

References 22 publications

Supporting

Mentioning

283

Contrasting

Order By: Relevance

“…In addition, we also conduct comprehensive case studies on a coauthorship network to evaluate the effectiveness of the k-influential community model. The results demonstrate that using our community model is capable of finding meaningful influential communities in a network, which can not be identified by using the k-truss community model [15].…”

Section: Introductionmentioning

confidence: 89%

See 1 more Smart Citation

Influential community search in large networks

et al. 2015

Self Cite

View full text Add to dashboard Cite

Community search is a problem of finding densely connected subgraphs that satisfy the query conditions in a network, which has attracted much attention in recent years. However, all the previous studies on community search do not consider the influence of a community. In this paper, we introduce a novel community model called k-influential community based on the concept of k-core, which can capture the influence of a community. Based on the new community model, we propose a linear-time online search algorithm to find the top-r k-influential communities in a network. To further speed up the influential community search algorithm, we devise a linear-space index structure which supports efficient search of the top-r k-influential communities in optimal time. We also propose an efficient algorithm to maintain the index when the network is frequently updated. We conduct extensive experiments on 7 real-world large networks, and the results demonstrate the efficiency and effectiveness of the proposed methods.

show abstract

Section: Introductionmentioning

confidence: 89%

“…Here we compare the proposed community model with the truss community model [15], which is successfully applied to find query-dependent cohesive communities in a large network. For a fair comparison, we compare the k-influential community with the k + 1 truss community.…”

Section: Case Studiesmentioning

confidence: 99%

Influential community search in large networks

et al. 2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…On R-MAT graphs, the number of vertices is set to be 5000 and the edge-to-vertex ratio varies from 40 to 140. On SSCA graphs, the number of vertices is set to be 2 20 and the size of the maximum clique varies from 100 to 200.…”

Section: On Synthetic Datasetsmentioning

confidence: 99%

“…Orthogonal to our work, many works extended the definition of clique to other dense subgraph structures (e.g., maximal cliques in an uncertain graph [42], cross-graph quasi-cliques [21], k-truss [20], and densest-subgraph [35]), and studied their applications. The existing algorithms for these problem are centralized.…”

Section: Related Workmentioning

confidence: 99%

Efficient Maximal Clique Enumeration Over Graph Data

et al. 2016

View full text Add to dashboard Cite

In a wide variety of emerging data-intensive applications, such as social network analysis, Web document clustering, entity resolution, and detection of consistently coexpressed 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 ¼ ðV; EÞ, the GP algorithm has a space complexity of O(|E|) and a time complexity of OðjEjlðGÞÞ, where lðGÞ 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-ofthe-art BK approach. Our parallel solutions are implemented and evaluated on MapReduce, a popular shared-nothing parallel framework, but can easily generalize to other sharednothing or shared-memory parallel frameworks.

show abstract

“…Clearly, social community and collaboration circle require the cohesion to be persistent, or robust, regardless of the size of the subgraph. There are many definitions for dense subgraphs, e.g., densest subgraph [20], kPlex, quasi-clique, k-Core [11] and k-Truss [10]. Of these, only for λ-quasi-cliques, the cohesion is always at least λ as the subgraph size changes.…”

Section: Introductionmentioning

confidence: 99%

Query-Driven Maximum Quasi-Clique Search

Lee¹,

Lakshmanan²

2016

Proceedings of the 2016 SIAM International Conference on Data Mining

View full text Add to dashboard Cite

Quasi-cliques are an elegant way to model dense subgraphs, with each node adjacent to at least a fraction λ ∈ (0, 1] of other nodes in the subgraph. In this paper, we focus on a new graph mining problem, the query-driven maximum quasi-clique (QMQ) search, which aims to find the largest λ-quasi-clique containing a given query node set S. This problem has many applications and is proved to be NP-Hard and inapproximable. To solve the problem efficiently in practice, we propose the notion of core tree to organize dense subgraphs recursively, which reduces the search space and effectively helps find the solution within a few tree traversals. To optimize a solution to a better solution, we introduce three refinement operations: Add, Remove and Swap. We propose two iterative maximization algorithms, DIM and SUM, to approach QMQ by deterministic and stochastic means respectively. With extensive experiments on three real datasets, we demonstrate that our algorithms significantly outperform several baselines in running time and the quality.

show abstract

Querying k-truss community in large and dynamic graphs

Cited by 388 publications

References 22 publications

Influential community search in large networks

Influential community search in large networks

Efficient Maximal Clique Enumeration Over Graph Data

Query-Driven Maximum Quasi-Clique Search

Contact Info

Product

Resources

About