基于社区结构的集体预测算法研究
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摘要
如果网络结构已知,则可将网络结构特征用于预测任务,集体预测算法则是利用这个思路提高预测效果。传统的集体预测算法主要是基于节点内容和直接邻居节点信息进行预测训练。然而,一些直接邻居节点信息有可能与目标节点不一致。除此之外在邻居节点不足的情况下,非邻居节点信息也是很有用处的。本文不使用直接邻居节点信息,而是将社区结构用在预测任务中。社区发现算法被应用于集体预测过程中以进一步改进预测性能。实验结果表明我们提出的算法优于一些标准的预测算法,尤其是在标注训练集有限的情况下。
Collective prediction algorithms have been used to improve performances when network structures are involved in prediction tasks. Conventional collective prediction algorithms conduct predictions based on the content of a node and the information of its direct neighbors with a base classifier. However, the information of some direct neighbor nodes may be not consistent with the target one. In addition, the information of indirect neighbors can be helpful when that of direct neighbors is scant. In this paper, instead of using information of direct neighbors, we propose to apply community structures in networks to prediction tasks. A community detection method is aggregated into the collective prediction process to improve prediction performance. Experimental results show that the proposed algorithm outperforms a number of standard prediction algorithms specially under conditions that labeled training dataset are limited.
Collective prediction algorithms have been used to improve performances when network structures are involved in prediction tasks. Conventional collective prediction algorithms conduct predictions based on the content of a node and the information of its direct neighbors with a base classifier. However, the information of some direct neighbor nodes may be not consistent with the target one. In addition, the information of indirect neighbors can be helpful when that of direct neighbors is scant. In this paper, instead of using information of direct neighbors, we propose to apply community structures in networks to prediction tasks. A community detection method is aggregated into the collective prediction process to improve prediction performance. Experimental results show that the proposed algorithm outperforms a number of standard prediction algorithms specially under conditions that labeled training dataset are limited.
引文
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[5] S. Geman, D. Geman. Stochastic relaxation,Gibbs distributions, and the Bayesian restoration of images[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions. 1984, 6(6): 721-741.
[6] P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, T. Eliassi-Rad. Collective classification in network data[J].AI magazine. 2008, 29(3): 93-106.
[2] V. D. Blondel, J.-L. Guillaume, R. Lambiotte, E. Lefebvre, Fast unfolding of communities in large networks[J]. Journal of Statistical Mechanics: Theory and Experiment. 2008, 2008(10): 10008.
[3] A. Clauset, M. E. Newman, C. Moore. Finding community structure in very large networks[J]. Physical review E. 2004,70(6): 066111.
[4] K. Wakita, T. Tsurumi. Finding community structure in mega-scale social networks:[extended abstract][C]//Proceedings of the 16th international conference on World Wide Web. 2007: 1275-1276.
[5] S. Geman, D. Geman. Stochastic relaxation,Gibbs distributions, and the Bayesian restoration of images[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions. 1984, 6(6): 721-741.
[6] P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, T. Eliassi-Rad. Collective classification in network data[J].AI magazine. 2008, 29(3): 93-106.