Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Finding recent frequent itemsets adaptively over online data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
An experimental study of large-scale mobile social network
Proceedings of the 18th international conference on World wide web
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In this paper, we study a new problem of mining individual friendship pattern (IFP) for characterizing the interaction behavior of each user in cellular phone call logs. The IFP represents the user's recent frequent relationships and their importance in social lives, which is a unique feature like fingerprint and is useful for many applications such as user resolution and viral marketing etc. We first give the definition and the efficient mining algorithm of the IFP. Then we solve the problem that how much data or time is adequate for characterizing the pattern by introducing a concept of the stable time and a hybrid similarity measure between the IFPs. The experimental result on the real massive cellular phone call logs demonstrates that most users' IFPs can be characterized by a small data set or time.