Weisfeiler-Lehman Neural Machine for Link Prediction
Muhan Zhang (Washington University in St. Louis);Yixin Chen (Washington University in St. Louis)
Abstract
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 promote the formation of links. Wlnm has unmatched advantages including higher performance than state-of-the-art methods and universal applicability over various kinds of networks. Wlnm extracts an enclosing subgraph of each target link and encodes the subgraph as an adjacency matrix. The key novelty of the encoding comes from a fast hashing-based Weisfeiler-Lehman (WL) algorithm that labels the vertices according to their structural roles in the subgraph while preserving the subgraph
