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, Kdense subgraph, 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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