MODEL: Motif-based Deep Feature Learning for Link Prediction

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%.

14 pages, 7 figures

References in corpus (18)

• Adam: A Method for Stochastic Optimization
• Efficient Estimation of Word Representations in Vector Space
• Distributed Representations of Words and Phrases and their Compositionality
• Community structure in social and biological networks
• DeepWalk: Online Learning of Social Representations
• LINE: Large-scale Information Network Embedding
• Link Prediction in Complex Networks: A Survey
• Structure and tie strengths in mobile communication networks
• Graph Embedding Techniques, Applications, and Performance: A Survey
• Higher-order organization of complex networks
• struc2vec: Learning Node Representations from Structural Identity
• Attributed Social Network Embedding
• Graph Evolution: Densification and Shrinking Diameters
• On Facebook, most ties are weak
• Motif-based communities in complex networks
• E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction
• Encoding Robust Representation for Graph Generation
• motif2vec: Motif Aware Node Representation Learning for Heterogeneous Networks

Cited by in corpus (2)

• CenGCN: Centralized Convolutional Networks with Vertex Imbalance for Scale-Free Graphs
• Evaluation and Comparison of Diffusion Models with Motif Features