A Cohesive Structure Based Bipartite Graph Analytics System

Bipartite graphs arise naturally when modeling two different types of entities such as user-item, author-paper, and director-board. In recent years, driven by numerous real-world applications in these networks, mining cohesive structures in bipartite graphs becomes a popular research topic. In this paper, we propose the first cohesive-structure-based bipartite graph analytics system, CohBGA. The key innovative features of our system are as follows. Firstly, we involve several cohesive-structure-based models and statistics in our system to analyze bipartite graphs at different levels of granularity. Secondly, CohBGA has a user-friendly and interactive visual interface with various functional tools to meet users' diverse query requirements. Thirdly, we implement state-of-the-art algorithms in CohBGA to support efficient query processing. Furthermore, as a generic framework is designed in CohBGA, CohBGA is going to be an open-source bipartite graph analytics platform that allows researchers to evaluate the effectiveness of more cohesive-structure-based models and algorithms for bipartite graphs.

[1]  Rui Zhou,et al.  Efficient Exact Algorithms for Maximum Balanced Biclique Search in Bipartite Graphs , 2020, SIGMOD Conference.

[2]  Srikanta Tirthapura,et al.  Butterfly Counting in Bipartite Networks , 2017, KDD.

[3]  Maria E. Orlowska,et al.  Efficient Bi-triangle Counting for Large Bipartite Networks , 2021, Proc. VLDB Endow..

[4]  Xuemin Lin,et al.  Cohesive Subgraph Search over Big Heterogeneous Information Networks: Applications, Challenges, and Solutions , 2021, SIGMOD Conference.

[5]  Ali Pinar,et al.  Peeling Bipartite Networks for Dense Subgraph Discovery , 2016, WSDM.

[6]  Stephen B. Seidman,et al.  Network structure and minimum degree , 1983 .

[7]  Hui Li,et al.  Efficient Fault-Tolerant Group Recommendation Using alpha-beta-core , 2017, CIKM.

[8]  Lijun Chang,et al.  Diversified top-k clique search , 2015, The VLDB Journal.

[9]  Lijun Chang,et al.  Index-Based Densest Clique Percolation Community Search in Networks , 2018, IEEE Transactions on Knowledge and Data Engineering.

[10]  Ying Zhang,et al.  Global Reinforcement of Social Networks: The Anchored Coreness Problem , 2020, SIGMOD Conference.

[11]  Tore Opsahl.  Triadic closure in two-mode networks: Redefining the global and local clustering coefficients , 2013, Soc. Networks.

[12]  Xuemin Lin,et al.  Efficiently Answering Reachability and Path Queries on Temporal Bipartite Graphs , 2021, Proc. VLDB Endow..

[13]  Peixiang Zhao,et al.  Truss-based Community Search: a Truss-equivalence Based Indexing Approach , 2017, Proc. VLDB Endow..

[14]  Fan Zhang,et al.  Discovering key users for defending network structural stability , 2021, World Wide Web.

[15]  Lu Qin,et al.  Efficient Bitruss Decomposition for Large-scale Bipartite Graphs , 2020, 2020 IEEE 36th International Conference on Data Engineering (ICDE).

[16]  Yun Yang,et al.  Finding a Summary for All Maximal Cliques , 2021, 2021 IEEE 37th International Conference on Data Engineering (ICDE).

[17]  Garry Robins,et al.  Small Worlds Among Interlocking Directors: Network Structure and Distance in Bipartite Graphs , 2004, Comput. Math. Organ. Theory.

[18]  Xin Wang,et al.  ExpFinder: Finding experts by graph pattern matching , 2013, 2013 IEEE 29th International Conference on Data Engineering (ICDE).

[19]  Lu Qin,et al.  Efficient Progressive Minimum k-core Search , 2019, Proc. VLDB Endow..

[20]  Hong Cheng,et al.  VizCS: Online Searching and Visualizing Communities in Dynamic Graphs , 2018, 2018 IEEE 34th International Conference on Data Engineering (ICDE).

[21]  Xuemin Lin,et al.  Efficient and Effective Community Search on Large-scale Bipartite Graphs , 2020, 2021 IEEE 37th International Conference on Data Engineering (ICDE).

[22]  Srikanta Tirthapura,et al.  Enumerating Maximal Bicliques from a Large Graph Using MapReduce , 2014, 2014 IEEE International Congress on Big Data.

[23]  Jianliang Xu,et al.  AutoG: a visual query autocompletion framework for graph databases , 2017, The VLDB Journal.

[24]  Kai Wang,et al.  Vertex Priority Based Butterfly Counting for Large-scale Bipartite Networks , 2018, Proc. VLDB Endow..

[25]  Xuemin Lin,et al.  Towards efficient solutions of bitruss decomposition for large-scale bipartite graphs , 2021, The VLDB Journal.

[26]  Xuemin Lin,et al.  Efficient (α, β)-core Computation: an Index-based Approach , 2019, WWW.

[27]  Jia Wang,et al.  Rectangle Counting in Large Bipartite Graphs , 2014, 2014 IEEE International Congress on Big Data.

[28]  Jeffrey Xu Yu,et al.  Querying k-truss community in large and dynamic graphs , 2014, SIGMOD Conference.

[29]  Tamara G. Kolda,et al.  Measuring and modeling bipartite graphs with community structure , 2016, J. Complex Networks.

[30]  Ying Zhang,et al.  Exploring Cohesive Subgraphs with Vertex Engagement and Tie Strength in Bipartite Graphs , 2020, Inf. Sci..

[31]  Martin G. Everett,et al.  Network analysis of 2-mode data , 1997 .

[32]  Kai Huang,et al.  C-Explorer: Browsing Communities in Large Graphs , 2017, Proc. VLDB Endow..

[33]  Kai Wang,et al.  Efficient Computing of Radius-Bounded k-Cores , 2018, 2018 IEEE 34th International Conference on Data Engineering (ICDE).