MODEL: Motif-based Deep Feature Learning for Link Prediction

TL;DR

This paper introduces a motif-based embedding algorithm for link prediction that leverages higher-order network structures, outperforming traditional and existing embedding methods across various network types.

Contribution

The paper presents a novel motif-based embedding method that captures higher-order network structures for improved link prediction accuracy.

Findings

Outperforms traditional similarity algorithms by 20%.

Outperforms state-of-the-art embedding algorithms by 19%.

Effective across social, biological, and academic networks.

Abstract

Link prediction plays an important role in network analysis and applications. Recently, approaches for link prediction have evolved from traditional similarity-based algorithms into embedding-based algorithms. However, most existing approaches fail to exploit the fact that real-world networks are different from random networks. In particular, real-world networks are known to contain motifs, natural network building blocks reflecting the underlying network-generating processes. In this paper, we propose a novel embedding algorithm that incorporates network motifs to capture higher-order structures in the network. To evaluate its effectiveness for link prediction, experiments were conducted on three types of networks: social networks, biological networks, and academic networks. The results demonstrate that our algorithm outperforms both the traditional similarity-based algorithms by 20% and the state-of-the-art embedding-based algorithms by 19%.