Mining Sequential Interaction Patterns in Social Networks
Date Issued
2011
Date
2011
Author(s)
Kao, Cheng-Li
Abstract
With advance of web technology, many social networks have been highly developed in recent years. A large amount of interactions between users in a social network have been collected into databases. Mining interaction patterns in social networks can help us to analyze user’s interactions and behavior, promote the technologies of running social networks, and formulate marketing and advertisement strategies. Therefore, in this thesis, we propose an efficient method, called MSIP (Maximal Sequential Interaction Patterns), to mine maximal interaction patterns in social network databases. The proposed algorithm consisted of two phases. First, we scan the database to find all frequent patterns of length one (1-patterns) and generate the projected database for each frequent 1-patterns. Next, we recursively mine all frequent patterns in a depth-first search (DFS) manner until no more frequent patterns can be found. During mining process, we employ three effective pruning strategies to prune unnecessary candidates and a closure checking scheme to remove non-maximal frequent patterns. Therefore, the proposed method can efficiently mine interaction patterns in social networks. The experimental results show that the MSIP algorithm outperforms the modified MSPX algorithm.
Subjects
interaction pattern
social network
frequent pattern
maximal pattern
data mining
Type
thesis
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