作者
Lianhua Chi, Bin Li, Xingquan Zhu
发表日期
2013
研讨会论文
Advances in Knowledge Discovery and Data Mining: 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part I 17
页码范围
225-236
出版商
Springer Berlin Heidelberg
简介
As many data mining applications involve networked data with dynamically increasing volumes, graph stream classification has recently extracted significant research interest. The aim of graph stream classification is to learn a discriminative model from a stream of graphs represented by sets of edges on a complex network. In this paper, we propose a fast graph stream classification method using DIscriminative Clique Hashing (DICH). The main idea is to employ a fast algorithm to decompose a compressed graph into a number of cliques to sequentially extract clique-patterns over the graph stream as features. Two random hashing schemes are employed to compress the original edge set of the graph stream and map the unlimitedly increasing clique-patterns onto a fixed-size feature space, respectively. The hashed cliques are used to update an “in-memory” fixed-size pattern-class table, which will be finally …
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L Chi, B Li, X Zhu - Advances in Knowledge Discovery and Data Mining …, 2013