Link Prediction Based on Deep Convolutional Neural Network

Wentao Wang,Rongbo Zhu,Ye Huang,Hao Wang,Lintao Wu

doi:10.3390/info10050172

Abstract

In recent years, endless link prediction algorithms based on network representation learning have emerged. Network representation learning mainly constructs feature vectors by capturing the neighborhood structure information of network nodes for link prediction. However, this type of algorithm only focuses on learning topology information from the simple neighbor network node. For example, DeepWalk takes a random walk path as the neighborhood of nodes. In addition, such algorithms only take advantage of the potential features of nodes, but the explicit features of nodes play a good role in link prediction. In this paper, a link prediction method based on deep convolutional neural network is proposed. It constructs a model of the residual attention network to capture the link structure features from the sub-graph. Further study finds that the information flow transmission efficiency of the residual attention mechanism was not high, so a densely convolutional neural network model was proposed for link prediction. We evaluate our proposed method on four published data sets. The results show that our method is better than several other benchmark algorithms on link prediction.

Highlights

Complex systems in the real world can usually be constructed in the form of networks with nodes representing different entities in the system and links representing the relationships between these entities
To address the above-mentioned problems, we propose a link prediction method based on the deep convolution neural network
We find that the residual attention mechanism may impede the information flow in the whole network

Summary

Introduction

Complex systems in the real world can usually be constructed in the form of networks with nodes representing different entities in the system and links representing the relationships between these entities. The similarity-based method assumes that the more similar the nodes are, the greater the possibility of links are between them [7,8] It calculates the similarity between nodes by defining a function that can use some network information, such as network topology or node attributes, to calculate the similarity between nodes, and utilize the similarity between nodes to predict the possibility of links between nodes. The learning-based method constructs a model that can extract various features to build a model for the given network, train the model with the existing information, and use the trained model to predict whether there will be links between the nodes

Methods

Results

Conclusion