Work

MODEL: Motif-Based Deep Feature Learning for Link Prediction

Year: 2020

Type: article

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 article, 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%).

Source: IEEE Transactions on Computational Social Systems

DOI: 10.1109/tcss.2019.2962819

Authors: Lei Wang, Jing Ren, Bo Xu, Jianxin Li, Wei Luo, Feng Xia

Institutions: Dalian University of Technology, Deakin University, Federation University

Language: English

Topic: Advanced Graph Neural Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, Australian Research Council, Fundamental Research Funds for the Central Universities

Awards: G1801488578, G2054049363, G4788755484