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

Chuanting Zhang, Ke-Ke Shang, Jingping Qiao

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Link prediction is a fundamental problem of data science, which usually calls for unfolding the mechanisms that govern the micro-dynamics of networks. In this regard, using features obtained from network embedding for predicting links has drawn widespread attention. Although methods based on edge features or node similarity have been proposed to solve the link prediction problem, many technical challenges still exist due to the unique structural properties of networks, especially when the networks are sparse. From the graph mining perspective, we first give empirical evidence of the inconsistency between heuristic and learned edge features. Then, we propose a novel link prediction framework, AdaSim, by introducing an Adaptive Similarity function using features obtained from network embedding based on random walks. The node feature representations are obtained by optimizing a graph-based objective function. Instead of generating edge features using binary operators, we perform link prediction solely leveraging the node features of the network. We define a flexible similarity function with one tunable parameter, which serves as a penalty of the original similarity measure. The optimal value is learned through supervised learning and thus is adaptive to data distribution. To evaluate the performance of our proposed algorithm, we conduct extensive experiments on eleven disparate networks of the real world. Experimental results show that AdaSim achieves better performance than state-of-the-art algorithms and is robust to different sparsities of the networks.
Original languageEnglish (US)
Pages (from-to)1-15
Number of pages15
StatePublished - Nov 19 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-11-22
Acknowledgements: This work was supported by the National Natural Science Foundation of China (61901247, 61803047), Natural Science Foundation of Shandong Province ZR2019BF032, Major Project of the National Social Science Foundation of China (19ZDA149 and 19ZDA324), and Fundamental Research Funds for the Central Universities (14370119 and 14390110).

ASJC Scopus subject areas

  • General


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