In this paper, we propose a next-generation link prediction method, Weisfeiler-Lehman
Neural Machine (WLNM), which learns topological features in the form of graph patterns that …

This paper examines important factors for link prediction in networks and provides a general,
high-performance framework for the prediction task. Link prediction in sparse networks …

We consider the graph link prediction task, which is a classic graph analytical problem with
many real-world applications. With the advances of deep learning, current link prediction …

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

Networks are ubiquitous in the real world. Link prediction, as one of the key problems for
network-structured data, aims to predict whether there exists a link between two nodes. The …

Link prediction is a fundamental task in such areas as social network analysis, information
retrieval, and bioinformatics. Usually link prediction methods use the link structures or node …

Link prediction is a popular research area with important applications in a variety of
disciplines, including biology, social science, security, and medicine. The fundamental …

Link prediction is a key problem for network-structured data. Link prediction heuristics use
some score functions, such as common neighbors and Katz index, to measure the likelihood …

Despite its outstanding performance in various graph tasks, vanilla Message Passing Neural
Network (MPNN) usually fails in link prediction tasks, as it only uses representations of two …

Network embedding aims to learn low-dimensional representations of nodes while capturing
structure information of networks. It has achieved great success on many tasks of network …