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So far, many network-structure-based link prediction methods have beenproposed. However, these methods only highlight one or two structural featuresof networks, and then use the methods to predict missing links in differentnetworks. The performances of these existing methods are not always satisfiedin all cases since each network has its unique underlying structural features.In this paper, by analyzing different real networks, we find that thestructural features of different networks are remarkably different. Inparticular, even in the same network, their inner structural features areutterly different. Therefore, more structural features should be considered.However, owing to the remarkably different structural features, thecontributions of different features are hard to be given in advance. Inspiredby these facts, an \emph{adaptive} fusion model regarding link prediction isproposed to incorporate multiple structural features. In the model, a logisticfunction combing multiple structural features is defined, then the weight ofeach feature in the logistic function is \emph{adaptively} determined byexploiting the known structure information. Last, we use the "learnt" logisticfunction to predict the connection probabilities of missing links. According toour experimental results, we find that the performance of our adaptive fusionmodel is better than many similarity indices.

Improving link prediction in complex networks by adaptively exploiting multiple structural features of networks