Abstract
AbstractThe closeness centrality of a vertex in a classical static graph is the reciprocal of the sum of the distances to all other vertices. However, networks are often dynamic and change over time. Temporal distances take these dynamics into account. In this work, we consider the harmonic temporal closeness with respect to the shortest duration distance. We introduce an efficient algorithm for computing the exact top-ktemporal closeness values and the corresponding vertices. The algorithm can be generalized to the task of computing all closeness values. Furthermore, we derive heuristic modifications that perform well on real-world data sets and drastically reduce the running times. For the case that edge traversal takes an equal amount of time for all edges, we lift two approximation algorithms to the temporal domain. The algorithms approximate the transitive closure of a temporal graph (which is an essential ingredient for the top-kalgorithm) and the temporal closeness for all vertices, respectively, with high probability. We experimentally evaluate all our new approaches on real-world data sets and show that they lead to drastically reduced running times while keeping high quality in many cases. Moreover, we demonstrate that the top-ktemporal and static closeness vertex sets differ quite largely in the considered temporal networks.
Funder
Deutsche Forschungsgemeinschaft
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Hardware and Architecture,Human-Computer Interaction,Information Systems,Software
Cited by
3 articles.
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2. TGLib: An Open-Source Library for Temporal Graph Analysis;2022 IEEE International Conference on Data Mining Workshops (ICDMW);2022-11
3. An MPI-Parallel Algorithm for Static and Dynamic Top-k Harmonic Centrality;2022 IEEE 34th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD);2022-11