Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering important nodes through graph entropy the case of Enron email database
Proceedings of the 3rd international workshop on Link discovery
Dynamical Processes on Complex Networks
Dynamical Processes on Complex Networks
Social Network Analysis for Information Flow in Disconnected Delay-Tolerant MANETs
IEEE Transactions on Mobile Computing
Temporal distance metrics for social network analysis
Proceedings of the 2nd ACM workshop on Online social networks
On the impact of users availability in OSNs
Proceedings of the Fifth Workshop on Social Network Systems
A temporal network analysis reveals the unprofitability of arbitrage in The Prosper Marketplace
Expert Systems with Applications: An International Journal
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
Fast centrality-driven diffusion in dynamic networks
Proceedings of the 22nd international conference on World Wide Web companion
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
Analyzing temporal metrics of public transportation for designing scalable delay-tolerant networks
Proceedings of the 8th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks
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The study of influential members of human networks is an important research question in social network analysis. However, the current state-of-the-art is based on static or aggregated representation of the network topology. We argue that dynamically evolving network topologies are inherent in many systems, including real online social and technological networks: fortunately the nature of these systems is such that they allow the gathering of large quantities of finegrained temporal data on interactions amongst the network members. In this paper we propose novel temporal centrality metrics which take into account such dynamic interactions over time. Using a real corporate email dataset we evaluate the important individuals selected by means of static and temporal analysis taking two perspectives: firstly, from a semantic level, we investigate their corporate role in the organisation; and secondly, from a dynamic process point of view, we measure information dissemination and the role of information mediators. We find that temporal analysis provides a better understanding of dynamic processes and a more accurate identification of important people compared to traditional static methods.