NDLI: Evolution pattern discovery in dynamic networks

Content Provider	IEEE Xplore Digital Library
Author	Guimin Qin Lin Gao Jianye Yang Jiajia Li
Copyright Year	2011
Description	Author affiliation: School of Computer Science and Technology, Xidian University, Xi'an, China (Lin Gao; Jianye Yang; Jiajia Li) \|\| School of Computer Science and Technology, Software School, Xidian University, Xi'an, China (Guimin Qin)
Abstract	The majority of recent graph mining approaches have focused on analyzing static interaction networks, neglecting the fact that most real-world networks are dynamic in nature. In this paper, we define a framework to find evolution patterns which are regular in dynamic networks. These patterns can be used to characterize the local properties of dynamic networks, and predict future behavior. In our framework, different snapshots of the dynamic network are transformed to a summary graph, and then occurrence rules are discovered for searching for evolution patterns. We also take noise into account by finding quasi-patterns instead of only precise ones. We analyze the time- and space-complexity of the approach. Experiments on synthetic dynamic networks and real-world dynamic networks show that our approach is efficient, so it can be used to find patterns in large scale networks with many snapshots. Furthermore, we obtain meaningful and interesting evolution patterns from social dynamic networks.
Starting Page	1
Ending Page	6
File Size	325059
Page Count	6
File Format	PDF
ISBN	9781457708930
e-ISBN	9781457708947
DOI	10.1109/ICSPCC.2011.6061813
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Computer science Evolving graphs Dynamic networks Heuristic algorithms Jitter Educational institutions Evolution patterns Electronic mail Complexity theory Quasi-patterns
Content Type	Text
Resource Type	Article

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