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How to Predict Social Relationships — Physics–inspired Approach to Link Prediction.

Ashraf, A. W.-U.-, Budka, M. and Musial, K., 2019. How to Predict Social Relationships — Physics–inspired Approach to Link Prediction. Physica A: Statistical Mechanics and its Applications, 523 (June), 1110-1129.

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DOI: 10.1016/j.physa.2019.04.246


Link prediction in social networks has a long history in complex network research area. The formation of links in networks has been approached by scientists from different backgrounds, ranging from physics to computer science. To predict the formation of new links, we consider measures which originate from network science and use them in the place of mass and distance within the formalism of Newton’s Gravitational Law. The attraction force calculated in this way is treated as a proxy for the likelihood of link formation. In particular, we use three different measures of vertex centrality as mass, and 13 dissimilarity measures including shortest path and inverse Katz score in place of distance, leading to over 50 combinations that we evaluate empirically. Combining these through gravitational law allows us to couple popularity with similarity, two important characteristics for link prediction in social networks. Performance of our predictors is evaluated using Area Under the Precision-Recall Curve (AUC) for seven different real-world network datasets. The experiments demonstrate that this approach tends to outperform the setting in which vertex similarity measures like Katz are used on their own. Our approach also gives us the opportunity to combine network’s global and local properties for predicting future or missing links. Our study shows that the use of the physical law which combines node importance with measures quantifying how distant the nodes are, is a promising research direction in social link prediction.

Item Type:Article
Uncontrolled Keywords:Social network; Link prediction; Network dynamics; Physics-inspired network predictive model; Newton gravitational law
Group:Faculty of Science & Technology
ID Code:32191
Deposited By: Symplectic RT2
Deposited On:24 Apr 2019 08:56
Last Modified:14 Mar 2022 14:15


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