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Link prediction is a fundamental problem of data science, which usually callsfor unfolding the mechanisms that govern the micro-dynamics of networks. Inthis regard, using features obtained from network embedding for predictinglinks has drawn widespread attention. Though edge features-based or nodesimilarity-based methods have been proposed to solve the link predictionproblem, many technical challenges still exist due to the unique structuralproperties of networks, especially when the networks are sparse. From the graphmining perspective, we first give empirical evidence of the inconsistencybetween heuristic and learned edge features. Then we propose a novel linkprediction framework, AdaSim, by introducing an Adaptive Similarity functionusing features obtained from network embedding based on random walks. The nodefeature representations are obtained by optimizing a graph-based objectivefunction. Instead of generating edge features using binary operators, weperform link prediction solely leveraging the node features of the network. Wedefine a flexible similarity function with one tunable parameter, which servesas a penalty of the original similarity measure. The optimal value is learnedthrough supervised learning thus is adaptive to data distribution. To evaluatethe performance of our proposed algorithm, we conduct extensive experiments oneleven disparate networks of the real world. Experimental results show thatAdaSim achieves better performance than state-of-the-art algorithms and isrobust to different sparsities of the networks.

Adaptive Similarity Function with Structural Features of Network Embedding for Missing Link Prediction