In: Physica A: Statistical Mechanics and its Applications, 2012, vol. 139, no. 22, p. 5769–5778
To evaluate the performance of prediction of missing links, the known data are randomly divided into two parts, the training set and the probe set. We argue that this straightforward and standard method may lead to terrible bias, since in real biological and information networks, missing links are more likely to be links connecting low-degree nodes. We therefore study how to uncover missing links...
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In: Physica A: Statistical Mechanics and its Applications, 2011, vol. 391, no. 4, p. 1777–1787
Identifying influential nodes that lead to faster and wider spreading in complex networks is of theoretical and practical significance. The degree centrality method is very simple but of little relevance. Global metrics such as betweenness centrality and closeness centrality can better identify influential nodes, but are incapable to be applied in large-scale networks due to the computational...
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In: International Journal of Modern Physics C, 2010, vol. 21, no. 6, p. 813-824
Two main difficulties in the problem of classification in partially labeled networks are the sparsity of the known labeled nodes and inconsistency of label information. To address these two difficulties, we propose a similarity-based method, where the basic assumption is that two nodes are more likely to be categorized into the same class if they are more similar. In this paper, we introduce ten...
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In: Physica A: Statistical Mechanics and its Applications, 2009, vol. 388, no. 23, p. 4867-4871
In this paper, by applying a diffusion process, we propose a new index to quantify the similarity between two users in a user–object bipartite graph. To deal with the discrete ratings on objects, we use a multi-channel representation where each object is mapped to several channels with the number of channels being equal to the number of different ratings. Each channel represents a certain...
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