The structure of information pathways in a social communication network
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding a team of experts in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Prediction of telephone user attributes based on network neighborhood information
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
A temporal network analysis reveals the unprofitability of arbitrage in The Prosper Marketplace
Expert Systems with Applications: An International Journal
Detecting stochastic temporal network motifs for human communication patterns analysis
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Social networks mediate not only the relations between entities, but also the patterns of information propagation among them and their communication behavior. In this paper, we extensively study the temporal annotations (e.g., time stamps and duration) of historical communications in social networks and propose two novel tools -- communication motifs and maximum-flow communication motifs -- for characterizations of the patterns of information propagation in social networks. Using these motifs, we verify the following hypothesis in social communication network: 1) the functional behavioral patterns of information propagation within both social networks are stable over time; 2) the patterns of information propagation in synchronous and asynchronous social networks are different and sensitive to the cost of communication; and 3) the speed and the amount of information that is propagated through a network are correlated and dependent on individual profiles.