Link Prediction Based on Local Random Walk

The problem of missing link prediction in complex networks has attracted much attention recently. Two difficulties in link prediction are the sparsity and huge size of the target networks. Therefore, the design of an efficient and effective method is of both theoretical interests and practical significance. In this Letter, we proposed a method based on local random walk, which can give competitively good prediction or even better prediction than other random-walk-based methods while has a lower computational complexity.Comment: 6 pages, 2 figure

Growing networks with local rules: preferential attachment, clustering hierarchy and degree correlations

The linear preferential attachment hypothesis has been shown to be quite successful to explain the existence of networks with power-law degree distributions. It is then quite important to determine if this mechanism is the consequence of a general principle based on local rules. In this work it is claimed that an effective linear preferential attachment is the natural outcome of growing network models based on local rules. It is also shown that the local models offer an explanation to other properties like the clustering hierarchy and degree correlations recently observed in complex networks. These conclusions are based on both analytical and numerical results of different local rules, including some models already proposed in the literature.Comment: 17 pages, 14 figures (to appear in Phys. Rev E

Growing Biochemical Networks: Identifying the Intrinsic Properties

SCOPUS: cp.kinfo:eu-repo/semantics/publishe