Vertex Betweenness Centrality Computation Method over Temporal Graphs

被引：0
|
作者：
Zhang T. [1 ]
Zhao J. [1 ]
Jin L. [1 ]
Chen L. [2 ]
Cao B. [1 ]
Fan J. [1 ]
机构：
[1] College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou
[2] College of Computer Science and Technology, Zhejiang University, Hangzhou
来源：
Jisuanji Yanjiu yu Fazhan/Computer Research and Development | 2023年 / 60卷 / 10期
关键词：
betweenness centrality; graph algorithm; parallel processing; temporal graph; temporal path;
D O I：
10.7544/issn1000-1239.202220650
中图分类号：
学科分类号：
摘要：
In social network analysis, betweenness centrality is utilized to measure the contribution of a vertex to the network structure and is a widely used vertex importance metric. This metric evaluates the vertex importance mainly by counting the number of shortest paths through the vertices. The current studies for betweenness centrality computation mostly focus on general graphs, few focus on temporal ones. For general graphs, the betweenness centrality calculation methods are mainly designed based on the Brandes’ algorithm. The key theory is that the subpaths of a shortest path is still shortest, i.e., the optimal sub-structure property. However, temporal graphs contain temporal information, and there are various types of temporal paths that do not satisfy the optimal sub-structure property. Therefore, the theory and methods for betweenness centrality calculation on general graphs are no longer suitable for temporal graphs. In view of this, we define two types of temporal paths, i.e., strict (ascending timing order) and non-strict (non-descending timing order), and study the theory and methods for betweenness centrality on temporal graphs. An efficient two-stage iterative computing framework based on message propagation is proposed. The first stage adopts the top-down breadth-first traversal paradigm to calculate temporal shortest paths; the second stage employs the bottom-up method to calculate the contributions of the vertex’s successors and children to its betweenness centrality, and designs a message propagation based iterative accumulation method. In order to improve the efficiency and scalability, a multi-thread parallel FTBC (fast temporal betweenness centrality) algorithm based on OpenMP (open multiprocessing) framework is implemented. Using eight real temporal graphs, it’s showed that our proposed betweenness centrality calculation method has better computational performance than state-of-the-art methods in our experiment. © 2023 Science Press. All rights reserved.
引用
收藏
页码：2383 / 2393
页数：10
相关论文
共 41 条
  • [21] Cousins C, Wohlgemuth C, Riondato M., BAVARIAN: Betweenness centrality approximation with variance-aware rademacher averages [C], Proc of the 27th ACM SIGKDD Conf on Knowledge Discovery & Data Mining, pp. 196-206, (2021)
  • [22] Vapnik V., Statistical Learning Theory [M], (1998)
  • [23] Pellegrina L, Vandin F., SILVAN: Estimating betweenness centralities with progressive sampling and non-uniform rademacher bounds, (2021)
  • [24] Lee M J, Lee J, Park J Y, Et al., QUBE: A quick algorithm for updating betweenness centrality [C], Proc of the 21st Int Conf on World Wide Web, (2012)
  • [25] Green O, Mccoll R, Bader D A., A fast algorithm for streaming betweenness centrality [C], Proc of Int Conf on Privacy, Security, Risk and Trust and Int Conf on Social Computing, pp. 11-20, (2012)
  • [26] Kourtellis N, Morales G D F, Bonchi F., Scalable online betweenness centrality in evolving graphs[J], IEEE Transactions on Knowledge and Data Engineering, 27, 9, pp. 2494-2506, (2015)
  • [27] Kas M, Wachs M, Carley K M, Et al., Incremental algorithm for updating betweenness centrality in dynamically growing networks [C], Proc of IEEE/ACM Int Conf on Advances in Social Networks Analysis and Mining, pp. 33-40, (2013)
  • [28] Ramalingam G, Reps T., An incremental algorithm for a generalization of the shortest-path problem[J], Journal of Algorithms, 21, 2, (1996)
  • [29] Bergamini E, Meyerhenke H, Staudt C L., Approximating betweenness centrality in large evolving networks [C], Proc of the 17th Workshop on Algorithm Engineering and Experiments, pp. 133-146, (2014)
  • [30] Hayashi T, Akiba T, Yoshida Y., Fully dynamic betweenness centrality maintenance on massive networks[J], Proceedings of the VLDB Endowment, 9, 2, pp. 48-59, (2015)
12345