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 …

Studying networks to predict the emerging interactions is a common research problem for
both fields of network science and machine learning. The problem of predicting future or …

Link prediction refers to predicting the connection probability between two nodes in terms of
existing observable network information, such as network structural topology and node …

Link prediction plays an important role in network analysis and applications. Recently,
approaches for link prediction have evolved from traditional similarity-based algorithms into …

Link prediction is a fundamental task in network analysis, with the objective of predicting
missing or potential links. While existing studies have mainly concentrated on single …

Link prediction has long been the focus in the analysis of network-structured data. Though
straightforward and efficient, heuristic approaches like Common Neighbors perform link …

Background: The link prediction issue is one of the most widely used problems in complex
network analysis. Link prediction requires knowing the background of previous link …

Given the ubiquitous existence of graph-structured data, learning the representations of
nodes for the downstream tasks ranging from node classification, link prediction to graph …

Link prediction aim is to use known information of network to infer missing edges, identify
spurious interactions, evaluate network evolving mechanisms, and so on. Currently, with the …

Link prediction plays a significant role in various applications of complex networks. The
existing link prediction methods can be divided into two categories: structural similarity …