Exploiting Common Neighbor Graph for Link Prediction

Abstract

Link prediction aims to predict whether two nodes in a network are likely to get connected. Motivated by its applications, e.g., in friend or product recommendation, link prediction has been extensively studied over the years. Most link prediction methods are designed based on specific assumptions that may or may not hold in different networks, leading to link prediction methods that are not generalizable. Here, for the first time, we address this problem by proposing general link prediction methods that can capture network-specific patterns. Most link prediction methods rely on computing similarities between between nodes. By learning a $\gamma$-decaying model, the proposed methods can measure the pairwise similarities between nodes more accurately, even when only using common neighbor information, which is often used by current techniques.

BibTeX Copy

@inproceedings{hao2020exploiting,
  title = {Exploiting Common Neighbor Graph for Link Prediction},
  author = {Hao Tian and Reza Zafarani},
  year = {2020},
  keywords = {conference},
  booktitle = {Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM)},
  abstract = {Link prediction aims to predict whether two nodes in a network are likely to get connected. Motivated by its applications, e.g., in friend or product recommendation, link prediction has been extensively studied over the years. Most link prediction methods are designed based on specific assumptions that may or may not hold in different networks, leading to link prediction methods that are not generalizable. Here, for the first time, we address this problem by proposing general link prediction methods that can capture network-specific patterns. Most link prediction methods rely on computing similarities between between nodes. By learning a $\gamma$-decaying model, the proposed methods can measure the pairwise similarities between nodes more accurately, even when only using common neighbor information, which is often used by current techniques.},
  pdf = {files/2020-CIKM-CN.pdf},
}