TY - GEN

T1 - Algorithmic Aspects of Temporal Betweenness

AU - Buß, Sebastian

AU - Molter, Hendrik

AU - Niedermeier, Rolf

AU - Rymar, MacIej

N1 - Publisher Copyright:
© 2020 ACM.

PY - 2020/8/23

Y1 - 2020/8/23

N2 - The betweenness centrality of a graph vertex measures how often this vertex is visited on shortest paths between other vertices of the graph. In the analysis of many real-world graphs or networks, betweenness centrality of a vertex is used as an indicator for its relative importance in the network. In recent years, a growing number of real-world networks is modeled as temporal graphs instead of conventional (static) graphs. In a temporal graph, we have a fixed set of vertices and there is a finite discrete set of time steps and every edge might be present only at some time steps. While shortest paths are straightforward to define in static graphs, temporal paths can be considered "optimal" with respect to many different criteria, including length, arrival time, and overall travel time (shortest, foremost, and fastest paths). This leads to different concepts of temporal betweenness centrality, posing new challenges on the algorithmic side. We provide a systematic study of temporal betweenness variants based on various concepts of optimal temporal paths both on a theoretical and empirical level.

AB - The betweenness centrality of a graph vertex measures how often this vertex is visited on shortest paths between other vertices of the graph. In the analysis of many real-world graphs or networks, betweenness centrality of a vertex is used as an indicator for its relative importance in the network. In recent years, a growing number of real-world networks is modeled as temporal graphs instead of conventional (static) graphs. In a temporal graph, we have a fixed set of vertices and there is a finite discrete set of time steps and every edge might be present only at some time steps. While shortest paths are straightforward to define in static graphs, temporal paths can be considered "optimal" with respect to many different criteria, including length, arrival time, and overall travel time (shortest, foremost, and fastest paths). This leads to different concepts of temporal betweenness centrality, posing new challenges on the algorithmic side. We provide a systematic study of temporal betweenness variants based on various concepts of optimal temporal paths both on a theoretical and empirical level.

KW - algorithm engineering

KW - counting complexity

KW - network centrality

KW - network science

KW - temporal graphs

KW - temporal paths

UR - http://www.scopus.com/inward/record.url?scp=85090410743&partnerID=8YFLogxK

U2 - 10.1145/3394486.3403259

DO - 10.1145/3394486.3403259

M3 - Conference contribution

AN - SCOPUS:85090410743

T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

SP - 2084

EP - 2092

BT - KDD 2020 - Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

PB - Association for Computing Machinery

T2 - 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020

Y2 - 23 August 2020 through 27 August 2020

ER -