Paper Info
Title
Graph hashing and factorization for fast graph stream classification
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.
Year
DOI
Venue
2013
10.1145/2505515.2505730
CIKM
Keywords
Field
DocType
fast graph stream classification,feature base,coarse-grained feature representation,subgraph feature,corresponding subgraph,graph classification,fine-grained graph factorization approach,effective learning,graph factorization,graph stream classification concern,graph data,matrix factorization
Data mining,Computer science,Theoretical computer science,Distance-hereditary graph,Artificial intelligence,Line graph,Clique graph,Pattern recognition,Graph factorization,Simplex graph,Null graph,Factor-critical graph,Butterfly graph
Conference
Citations 
PageRank 
References 
4
0.39
3
Authors
3
Name
Order
Citations
PageRank
Ting Guo1267.49
Lianhua Chi2797.82
Xingquan Zhu33086181.95