EFFECTIVE MINING ALGORITHM OF K-DENSE SUB-GRAPH IN COMPLEX NETWORK BASED ON PROBABILITY ATTRIBUTE GRAPH

Chunying Zhang, Liya Wang, and Baoxiang Liu

Keywords

Complex network, probability attribute graph, possible worldsemantic graph, K-dense sub-graph, mining algorithm

Abstract

In complicated network, the uncertainty of probability attribute graph (PA graph) is decided by edge, vertex and its attributes. Mining dense sub-graph of the PA graph is a very significant research direction. Firstly, the PA graph model was constructed based on probability graph, and its properties were analysed. Secondly, the sub-graph, dense sub-graph, expectation density function and existence probabilities were put forward from three points: probability I attribute graph, probability II attribute graph and PA graph. Finally, effective algorithm of mining K-dense sub-graph was designed. The experimental simulation shows the effectiveness and applicability of the mining algorithm.

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