An Index For Temporal Closeness Computation in Evolving Graphs

Temporal closeness is a generalization of the classical closeness centrality measure for analyzing evolving networks. The temporal closeness of a vertex v is defined as the sum of the reciprocals of the temporal distances to the other vertices. Ranking all vertices of a network according to the temporal closeness is computationally expensive as it leads to a single-source-all-destination (SSAD) temporal distance query starting from each vertex of the graph. To reduce the running time of temporal closeness computations, we introduce an index to speed up SSAD temporal distance queries called Substream index. We show that deciding if a Substream index of a given size exists is NP-complete and provide an efficient greedy approximation. Moreover, we improve the running time of the approximation using min- hashing and parallelization. Our evaluation with real-world temporal networks shows a running time improvement of up to one order of magnitude compared to the state-of-the-art temporal closeness ranking algorithms.

  • Published in:
    SIAM International Conference on Data Mining
  • Type:
    Inproceedings
  • Authors:
    Oettershagen, Lutz; Mutzel, Petra
  • Year:
    2023

Citation information

Oettershagen, Lutz; Mutzel, Petra: An Index For Temporal Closeness Computation in Evolving Graphs, SIAM International Conference on Data Mining, 2023, https://epubs.siam.org/doi/10.1137/1.9781611977653.ch32, Oettershagen.Mutzel.2023a,

Associated Lamarr Researchers

lamarr institute person Mutzel Petra - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Prof. Dr. Petra Mutzel

Principal Investigator Hybrid ML to the profile