On the randomized complexity of monotone graph properties
Acta Cybernetica
Community evolution in dynamic multi-mode networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Managing and Mining Graph Data
Managing and Mining Graph Data
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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.