Web Structure, Dynamics and Page Quality
SPIRE 2002 Proceedings of the 9th International Symposium on String Processing and Information Retrieval
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
On the temporal dimension of search
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
EventRank: a framework for ranking time-varying networks
Proceedings of the 3rd international workshop on Link discovery
A framework for analysis of dynamic social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Structure of Heterogeneous Networks
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Towards recency ranking in web search
Proceedings of the third ACM international conference on Web search and data mining
Community detection using a measure of global influence
SNAKDD'08 Proceedings of the Second international conference on Advances in social network mining and analysis
A framework for quantitative analysis of cascades on networks
Proceedings of the fourth ACM international conference on Web search and data mining
Snapshot Centrality Indices in Dynamic FIFO Networks
Journal of Mathematical Modelling and Algorithms
Visual Analysis of Dynamic Networks Using Change Centrality
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Incremental algorithm for updating betweenness centrality in dynamically growing networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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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.