Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
research-article

Time-topology analysis

Published: 01 September 2021 Publication History

Abstract

Many real-world networks have been evolving, and are finely modeled as temporal graphs from the viewpoint of the graph theory. A temporal graph is informative, and always contains two types of information, i.e., the temporal information and topological information, where the temporal information reflects the time when the relationships are established, and the topological information focuses on the structure of the graph. In this paper, we perform time-topology analysis on temporal graphs to extract useful information. Firstly, a new metric named T-cohesiveness is proposed to evaluate the cohesiveness of a temporal subgraph. It defines the cohesiveness of a temporal subgraph from the time and topology dimensions jointly. Specifically, given a temporal graph Gs = (Vs, εEs), cohesiveness in the time dimension reflects whether the connections in Gs happen in a short period of time, while cohesiveness in the topology dimension indicates whether the vertices in Vs are densely connected and have few connections with vertices out of Gs. Then, T-cohesiveness is utilized to perform time-topology analysis on temporal graphs, and two time-topology analysis methods are proposed. In detail, T-cohesiveness evolution tracking traces the evolution of the T-cohesiveness of a subgraph, and combo searching finds out all the subgraphs that contain the query vertex and have T-cohesiveness larger than a given threshold. Moreover, a pruning strategy is proposed to improve the efficiency of combo searching. Experimental results confirm the efficiency of the proposed time-topology analysis methods and the pruning strategy.

References

[1]
Esra Akbas and Peixiang Zhao. 2017. Truss-based community search: a truss-equivalence based indexing approach. Proceedings of the VLDB Endowment 10, 11 (2017), 1298--1309.
[2]
V. D. Blondel, J. L. Guillaume, R. Lambiotte, and E. Lefebvre. 2008. Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment 2008, 10 (Oct. 2008), P10008.
[3]
Lu Chen, Chengfei Liu, Kewen Liao, Jianxin Li, and Rui Zhou. 2019. Contextual community search over large social networks. In ICDE. IEEE, 88--99.
[4]
Jonathan Cohen. 2008. Trusses: Cohesive subgraphs for social network analysis. National security agency technical report 16 (2008), 3--29.
[5]
Leon Cohen. 1995. Time-frequency analysis. Vol. 778. Prentice hall. 70--81 pages.
[6]
Wanyun Cui, Yanghua Xiao, Haixun Wang, Yiqi Lu, and Wei Wang. 2013. Online search of overlapping communities. In Proceedings of the 2013 ACM SIGMOD international conference on Management of data. 277--288.
[7]
Wanyun Cui, Yanghua Xiao, Haixun Wang, and Wei Wang. 2014. Local search of communities in large graphs. In Proceedings of the 2014 ACM SIGMOD international conference on Management of data. 991--1002.
[8]
TA Dang and Emmanuel Viennet. 2012. Community detection based on structural and attribute similarities. In International conference on digital society (icds). 7--12.
[9]
Maximilien Danisch, Oana Balalau, and Mauro Sozio. 2018. Listing k-cliques in sparse real-world graphs. In Proceedings of the 2018 World Wide Web Conference. 589--598.
[10]
Maximilien Danisch, Oana Balalau, and Mauro Sozio. 2018. Listing k-cliques in sparse real-world graphs. In Proceedings of the 2018 World Wide Web Conference. 589--598.
[11]
Zeineb Dhouioui and Jalel Akaichi. 2014. Tracking dynamic community evolution in social networks. In ASONAM 2014. IEEE, 764--770.
[12]
Yixiang Fang, Xin Huang, Lu Qin, Ying Zhang, Wenjie Zhang, Reynold Cheng, and Xuemin Lin. 2020. A survey of community search over big graphs. The VLDB Journal 29, 1 (2020), 353--392.
[13]
Yixiang Fang, Yixing Yang, Wenjie Zhang, Xuemin Lin, and Xin Cao. 2020. Effective and efficient community search over large heterogeneous information networks. Proceedings of the VLDB Endowment 13, 6 (2020), 854--867.
[14]
S. Fortunato and D. Hric. 2016. Community detection in networks: A user guide. Physics Reports 659 (Nov. 2016), 1--44.
[15]
M. Girvan and M. E. J. Newman. 2002. Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99, 12 (June 2002), 7821--7826.
[16]
Chonghui Guo, Jiajia Wang, and Zhen Zhang. 2014. Evolutionary community structure discovery in dynamic weighted networks. Physica A: Statistical Mechanics and its Applications 413 (2014), 565--576.
[17]
Wentao Han, Youshan Miao, Kaiwei Li, Ming Wu, Fan Yang, Lidong Zhou, Vijayan Prabhakaran, Wenguang Chen, and Enhong Chen. 2014. Chronos: a graph engine for temporal graph analysis. In Proceedings of the Ninth European Conference on Computer Systems. 1--14.
[18]
Xin Huang, Hong Cheng, Lu Qin, Wentao Tian, and Jeffrey Xu Yu. 2014. Querying k-truss community in large and dynamic graphs. In Proceedings of the 2014 ACM SIGMOD international conference on Management of data. 1311--1322.
[19]
Xin Huang, Laks VS Lakshmanan, and Jianliang Xu. 2019. Community search over big graphs. Vol. 14. Morgan & Claypool Publishers. 1--206 pages.
[20]
Caiyan Jia, Yafang Li, Matthew B Carson, Xiaoyang Wang, and Jian Yu. 2017. Node attribute-enhanced community detection in complex networks. Scientific Reports 7, 1 (2017), 1--15.
[21]
Wissam Khaouid, Marina Barsky, Venkatesh Srinivasan, and Alex Thomo. 2015. K-core decomposition of large networks on a single PC. Proceedings of the VLDB Endowment 9, 1 (2015), 13--23.
[22]
Jure Leskovec, Jon Kleinberg, and Christos Faloutsos. 2007. Graph evolution: Densification and shrinking diameters. ACM transactions on Knowledge Discovery from Data (TKDD) 1, 1 (2007), 2-es.
[23]
I. X. Y. Leung, H. Pan, P. Lio, and J. Crowcroft. 2009. Towards real-time community detection in large networks. Physical review. E, Statistical, nonlinear, and soft matter physics 79, 6 Pt 2 (June 2009), 066107.
[24]
Michael Levi and Peter Reuter. 2006. Money laundering. Crime and Justice 34, 1 (2006), 289--375.
[25]
Rong-Hua Li, Jiao Su, Lu Qin, Jeffrey Xu Yu, and Qiangqiang Dai. 2018. Persistent community search in temporal networks. In ICDE. IEEE, 797--808.
[26]
Qing Liu, Yifan Zhu, Minjun Zhao, Xin Huang, Jianliang Xu, and Yunjun Gao. 2020. VAC: Vertex-Centric Attributed Community Search. In 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 937--948.
[27]
Yunkai Lou, Chaokun Wang, Tiankai Gu, Hao Feng, Jun Chen, and Jeffrey Xu Yu. 2021. Time-Topology Analysis (Full Version). Technical Report. Tsinghua University, Beijing, China.
[28]
Jiehuan Luo, Xin Cao, Xike Xie, Qiang Qu, Zhiqiang Xu, and Christian S Jensen. 2020. Efficient Attribute-Constrained Co-Located Community Search. In 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 1201--1212.
[29]
Pietro Panzarasa, Tore Opsahl, and Kathleen M Carley. 2009. Patterns and dynamics of users' behavior and interaction: Network analysis of an online community. Journal of the American Society for Information Science and Technology 60, 5 (2009), 911--932.
[30]
Ashwin Paranjape, Austin R Benson, and Jure Leskovec. 2017. Motifs in temporal networks. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. 601--610.
[31]
David H Pyle. 1999. Bank risk management: theory. In Risk Management and regulation in banking. Springer, 7--14.
[32]
U. N. Raghavan, R. Albert, and S. Kumara. 2007. Near linear time algorithm to detect community structures in large-scale networks. Physical Review E 76, 3 (Sept. 2007), 036106.
[33]
Giulio Rossetti and Rémy Cazabet. 2018. Community discovery in dynamic networks: a survey. ACM Computing Surveys (CSUR) 51, 2 (2018), 1--37.
[34]
Ryan A. Rossi and Nesreen K. Ahmed. 2015. The Network Data Repository with Interactive Graph Analytics and Visualization. In AAAI. 4292--4293. http://networkrepository.com
[35]
Stephen B Seidman. 1983. Network structure and minimum degree. Social networks 5, 3 (1983), 269--287.
[36]
Mauro Sozio and Aristides Gionis. 2010. The community-search problem and how to plan a successful cocktail party. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. 939--948.
[37]
Jimeng Sun, Christos Faloutsos, Spiros Papadimitriou, and Philip S Yu. 2007. Graphscope: parameter-free mining of large time-evolving graphs. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. 687--696.
[38]
Mansoureh Takaffoli, Farzad Sangi, Justin Fagnan, and Osmar R Zäiane. 2011. Community evolution mining in dynamic social networks. Procedia-Social and Behavioral Sciences 22 (2011), 49--58.
[39]
Chaokun Wang and Junchao Zhu. 2019. Forbidden nodes aware community search. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 758--765.
[40]
Meng Wang, Chaokun Wang, Jeffrey Xu Yu, and Jun Zhang. 2015. Community detection in social networks: an in-depth benchmarking study with a procedure-oriented framework. Proceedings of the VLDB Endowment 8, 10 (2015), 998--1009.
[41]
Xiao Wang, Di Jin, Xiaochun Cao, Liang Yang, and Weixiong Zhang. 2016. Semantic community identification in large attribute networks. In Thirtieth AAAI Conference on Artificial Intelligence. 265--271.
[42]
Zhuo Wang, Weiping Wang, Chaokun Wang, Xiaoyan Gu, Bo Li, and Dan Meng. 2019. Community focusing: yet another query-dependent community detection. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 329--337.
[43]
Yubao Wu, Ruoming Jin, Jing Li, and Xiang Zhang. 2015. Robust local community detection: on free rider effect and its elimination. Proceedings of the VLDB Endowment 8, 7 (2015), 798--809.
[44]
Zhonggang Wu, Zhao Lu, and Shan-Yuan Ho. 2016. Community detection with topological structure and attributes in information networks. TIST 8, 2 (2016), 1--17.
[45]
Chen Zhe, Aixin Sun, and Xiaokui Xiao. 2019. Community detection on large complex attribute network. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. 2041--2049.
[46]
Di Zhuang, Morris J Chang, and Mingchen Li. 2019. DynaMo: Dynamic community detection by incrementally maximizing modularity. TKDE (2019), 1934--1945.

Cited By

View all
  • (2024)Efficient Maximal Frequent Group Enumeration in Temporal Bipartite GraphsProceedings of the VLDB Endowment10.14778/3681954.368199717:11(3243-3255)Online publication date: 30-Aug-2024

Index Terms

  1. Time-topology analysis
      Index terms have been assigned to the content through auto-classification.

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image Proceedings of the VLDB Endowment
      Proceedings of the VLDB Endowment  Volume 14, Issue 13
      September 2021
      168 pages
      ISSN:2150-8097
      Issue’s Table of Contents

      Publisher

      VLDB Endowment

      Publication History

      Published: 01 September 2021
      Published in PVLDB Volume 14, Issue 13

      Qualifiers

      • Research-article

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)14
      • Downloads (Last 6 weeks)1
      Reflects downloads up to 12 Jan 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2024)Efficient Maximal Frequent Group Enumeration in Temporal Bipartite GraphsProceedings of the VLDB Endowment10.14778/3681954.368199717:11(3243-3255)Online publication date: 30-Aug-2024

      View Options

      Login options

      Full Access

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media