A Dynamic Algorithm for Updating Katz Centrality in Graphs

Many large datasets from a variety of fields of research can be represented as graphs. A common query is to identify the most important, or highly ranked, vertices in a graph. Centrality metrics are used to obtain numerical scores for each vertex in the graph. The scores can then be translated to ra...

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Veröffentlicht in:Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 S. 149 - 154
Hauptverfasser: Nathan, Eisha, Bader, David A.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: New York, NY, USA ACM 31.07.2017
Schriftenreihe:ACM Conferences
Schlagworte:
Applied computing
Applied computing > Physical sciences and engineering
Applied computing > Physical sciences and engineering > Mathematics and statistics
Computing methodologies
Computing methodologies > Symbolic and algebraic manipulation
Computing methodologies > Symbolic and algebraic manipulation > Symbolic and algebraic algorithms
Information systems
Information systems > Information systems applications
Mathematics of computing
Mathematics of computing > Discrete mathematics
Mathematics of computing > Discrete mathematics > Graph theory
Mathematics of computing > Discrete mathematics > Graph theory > Graph algorithms
Mathematics of computing > Probability and statistics
Theory of computation
Theory of computation > Design and analysis of algorithms
ISBN:1450349935, 9781450349932
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Beschreibung
Zusammenfassung:Many large datasets from a variety of fields of research can be represented as graphs. A common query is to identify the most important, or highly ranked, vertices in a graph. Centrality metrics are used to obtain numerical scores for each vertex in the graph. The scores can then be translated to rankings identifying relative importance of vertices. In this work we focus on Katz Centrality, a linear algebra based metric. In many real applications, since data is constantly being produced and changed, it is necessary to have a dynamic algorithm to update centrality scores with minimal computation when the graph changes. We present an algorithm for updating Katz Centrality scores in a dynamic graph that incrementally updates the centrality scores as the underlying graph changes. Our proposed method exploits properties of iterative solvers to obtain updated Katz scores in dynamic graphs. Our dynamic algorithm improves performance and achieves speedups of over two orders of magnitude compared to a standard static algorithm while maintaining high quality of results.
ISBN:1450349935
9781450349932
DOI:10.1145/3110025.3110034