Abstract：Aiming at the problems of low efficiency and accuracy of data mining, a graph mining model using Markov clustering based on annular network motifs is proposed. Firstly, the Erdo″s-Rényi model is employed to generate random graphs according to the vertices set of the input graph. Annular sub-graphs are judged by the additive property of vectors in the process of sub-graph mining from input and random graphs. In the next step, the motif statistical characteristics are calculated and used to label the annular motifs. Then, the correlation matrix of absolute contribution of edges is solved in the graph, and the threshold is obtained by dynamic threshold method for the binarization of matrix. Finally, inflation and expansion processes are carried out on the sparsified graph data to achieve the state of convergence. Experimental results show that the proposed model can effectively reduce the running time and improve the mining efficiency of the graph with the guaranteed clustering quality.
任永功，索全明，刘洋. 基于环型网络模体应用马尔科夫聚类的图挖掘模型*[J]. 模式识别与人工智能, 2017, 30(9): 803-814.
REN Yonggong, SUO Quanming, LIU Yang. Graph Mining Model Using Markov Clustering Based on Annular Network Motifs. , 2017, 30(9): 803-814.
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