Personalized Degrees: Effects on Link Formation in Dynamic Networks from an Egocentric Perspective

TL;DR

This paper introduces personalized degree from an egocentric perspective to better understand link formation in dynamic social networks, showing that neighbors with higher personalized degree are more influential in forming new links.

Contribution

It proposes the concept of personalized degree for neighbors and demonstrates its effectiveness in predicting link formation and improving link recommendation accuracy.

Findings

Neighbors with higher personalized degree are more likely to lead to new links.

Personalized out-degree has a stronger effect on link formation than personalized in-degree.

Incorporating personalized and global degrees improves link recommendation accuracy.

Abstract

Understanding mechanisms driving link formation in dynamic social networks is a long-standing problem that has implications to understanding social structure as well as link prediction and recommendation. Social networks exhibit a high degree of transitivity, which explains the successes of common neighbor-based methods for link prediction. In this paper, we examine mechanisms behind link formation from the perspective of an ego node. We introduce the notion of personalized degree for each neighbor node of the ego, which is the number of other neighbors a particular neighbor is connected to. From empirical analyses on four on-line social network datasets, we find that neighbors with higher personalized degree are more likely to lead to new link formations when they serve as common neighbors with other nodes, both in undirected and directed settings. This is complementary to the finding of Adamic and Adar that neighbor nodes with higher (global) degree are less likely to lead to new link formations. Furthermore, on directed networks, we find that personalized out-degree has a stronger effect on link formation than personalized in-degree, whereas global in-degree has a stronger effect than global out-degree. We validate our empirical findings through several link recommendation experiments and observe that incorporating both personalized and global degree into link recommendation greatly improves accuracy.