Deep Network Embedding for Graph Representation Learning in Signed Networks

Abstract

Network embedding has attracted an increasing attention over the past few years. As an effective approach to solve graph mining problems, network embedding aims to learn a low-dimensional feature vector representation for each node of a given network. The vast majority of existing network embedding algorithms, however, are only designed for unsigned networks, and the signed networks containing both positive and negative links, have pretty distinct properties from the unsigned counterpart. In this paper, we propose a deep network embedding model to learn the low-dimensional node vector representations with structural balance preservation for the signed networks. The model employs a semi-supervised stacked auto-encoder to reconstruct the adjacency connections of a given signed network. As the adjacency connections are overwhelmingly positive in the real-world signed networks, we impose a larger penalty to make the auto-encoder focus more on reconstructing the scarce negative links than the abundant positive links. In addition, to preserve the structural balance property of signed networks, we design the pairwise constraints to make the positively connected nodes much closer than the negatively connected nodes in the embedding space. Based on the network representations learned by the proposed model, we conduct link sign prediction and community detection in signed networks. Extensive experimental results in real-world datasets demonstrate the superiority of the proposed model over the state-of-the-art network embedding algorithms for graph representation learning in signed networks.

References in corpus (7)

• Efficient Estimation of Word Representations in Vector Space
• DeepWalk: Online Learning of Social Representations
• LINE: Large-scale Information Network Embedding
• The Slashdot Zoo: Mining a Social Network with Negative Edges
• Analysis of community structure in networks of correlated data
• Governance in Social Media: A case study of the Wikipedia promotion process
• Clustering Signed Networks with the Geometric Mean of Laplacians

Cited by in corpus (13)

• Learning Graph Embedding with Adversarial Training Methods
• A Comprehensive Survey on Community Detection with Deep Learning
• Graph Neural Networks: Taxonomy, Advances and Trends
• Network Together: Node Classification via Cross network Deep Network Embedding
• Domain-adaptive Message Passing Graph Neural Network
• Hyperparameter-free and Explainable Whole Graph Embedding
• Riemannian Adaptive Optimization Algorithm and Its Application to Natural Language Processing
• Whole-Graph Representation Learning For the Classification of Signed Networks
• Adversarial Deep Network Embedding for Cross-network Node Classification
• Smoothness Sensor: Adaptive Smoothness-Transition Graph Convolutions for Attributed Graph Clustering
• A generalized motif-based Naïve Bayes model for sign prediction in complex networks
• Adversarial Training Methods for Network Embedding
• Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex Networks