NDLI: Graph hashing and factorization for fast graph stream classification

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Graph hashing and factorization for fast graph stream classification

Content Provider	ACM Digital Library
Author	Chi, Lianhua Guo, Ting Zhu, Xingquan
Abstract	Graph stream classification concerns building learning models from continuously growing graph data, in which an essential step is to explore subgraph features to represent graphs for effective learning and classification. When representing a graph using subgraph features, all existing methods employ coarse-grained feature representation, which only considers whether or not a subgraph feature appears in the graph. In this paper, we propose a fine-grained graph factorization approach for Fast Graph Stream Classification (FGSC). Our main idea is to find a set of cliques as feature base to represent each graph as a linear combination of the base cliques. To achieve this goal, we decompose each graph into a number of cliques and select discriminative cliques to generate a transfer matrix called Clique Set Matrix (M). By using M as the base for formulating graph factorization, each graph is represented in a vector space with each element denoting the degree of the corresponding subgraph feature related to the graph, so existing supervised learning algorithms can be applied to derive learning models for graph classification.
Starting Page	1607
Ending Page	1612
Page Count	6
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505730
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Graph stream Matrix factorization Mining algorithm
Content Type	Text
Resource Type	Article

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