Centrality Metric for Dynamic Networks

Abstract

Centrality is an important notion in network analysis and is used to measure the degree to which network structure contributes to the importance of a node in a network. While many different centrality measures exist, most of them apply to static networks. Most networks, on the other hand, are dynamic in nature, evolving over time through the addition or deletion of nodes and edges. A popular approach to analyzing such networks represents them by a static network that aggregates all edges observed over some time period. This approach, however, under or overestimates centrality of some nodes. We address this problem by introducing a novel centrality metric for dynamic network analysis. This metric exploits an intuition that in order for one node in a dynamic network to influence another over some period of time, there must exist a path that connects the source and destination nodes through intermediaries at different times. We demonstrate on an example network that the proposed metric leads to a very different ranking than analysis of an equivalent static network. We use dynamic centrality to study a dynamic citations network and contrast results to those reached by static network analysis.

in KDD workshop on Mining and Learning in Graphs (MLG)

References in corpus (4)

• Finding and evaluating community structure in networks
• Citation Statistics from 110 Years of Physical Review
• Stability of shortest paths in complex networks with random edge weights
• Structure of Heterogeneous Networks

Cited by in corpus (4)

• Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics
• Ranking to Learn: Feature Ranking and Selection via Eigenvector Centrality
• A Framework for Quantitative Analysis of Cascades on Networks
• Maximizing Coverage Centrality via Network Design: Extended Version