期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 49, 期 2, 页码 626-637出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2017.2783325
关键词
EM algorithm; power-law degree distribution; stochastic block models (SBMs)
类别
资金
- National Natural Science Foundation of China [61702145, 61622205, 61472110]
- Zhejiang Provincial Natural Science Foundation of China [LR15F020002]
- Australian Research Council [FL-170100117, DP-180103424, DP-140102164, LP-150100671]
Stochastic block models (SBMs) have been playing an important role in modeling clusters or community structures of network data. But, it is incapable of handling several complex features ubiquitously exhibited in real-world networks, one of which is the power-law degree characteristic. To this end, we propose a new variant of SBM, termed power-law degree SBM (PLD-SBM), by introducing degree decay variables to explicitly encode the varying degree distribution over all nodes. With an exponential prior, it is proved that PLD-SBM approximately preserves the scale-free feature in real networks. In addition, from the inference of variational E-Step, PLD-SBM is indeed to correct the bias inherited in SBM with the introduced degree decay factors. Furthermore, experiments conducted on both synthetic networks and two real-world datasets including Adolescent Health Data and the political blogs network verify the effectiveness of the proposed model in terms of cluster prediction accuracies.
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