Abstract
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.
Highlights
Our approach to link prediction in social networks is inspired by Newton’s law of universal gravitation, which states that the force exerted between two masses is proportional to the product of those masses, and inversely proportional to the squared distance between their centres [50]: F
The results show that overall low values of Area Under the Precision–Recall Curve (AUC) for a certain dataset do not necessarily mean that particular dataset has low predictability
We discuss below the results from the perspective of individual datasets and interpret those outcomes in the context of characteristics of each social network tested: 1. collegeMsg: Overall, performance of methods on collegeMsg does not appear to be very good when compared to the remaining datasets
Summary
Zachary’s karate club contains only 34 nodes and 78 vertices, whereas today’s social networks (e.g. Facebook, scientific paper citation, Twitter), contain billions of nodes and are far more complex and dynamic [6]. These largescale social networks are formed by social interactions, their topological properties and dynamics are similar to those of networks found in nature. Most biological networks exhibit power-law degree distribution, cellular networks have high clustering coefficient, network encoding the large-scale causal structure of spacetime in our accelerating universe exhibits power-law degree distribution and high clustering coefficient [4,7]. Both of these characteristics are commonly found in social networks
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