Deep Network Embedding for Graph Representation Learning in Signed Networks

By X. Shen, Fu-lai Chung. 2019

100 citations recorded by the source corpus.

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 semisupervised 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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Graph Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Shirui Pan
2. Guodong Long
3. Chengqi Zhang
4. Jing Jiang
5. Philip S. Yu
6. Dacheng Tao
7. Chuan Zhou
8. Ruiqi Hu

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Shirui Pan
3. Philip S. Yu
4. Xianglong Liu
5. Chengqi Zhang
6. Jia Wu
7. Jing Jiang
8. Chuan Zhou

In Statistical and Nonlinear Physics

1. Shirui Pan
2. Chengqi Zhang
3. Guodong Long
4. Ruiqi Hu
5. Philip S. Yu
6. Jing Jiang
7. Dacheng Tao
8. Cécile Paris

In Graph Learning

1. Shirui Pan
2. Dacheng Tao
3. Philip S. Yu
4. Jia Wu
5. Surya Nepal
6. Quan Z. Sheng
7. Jing Jiang
8. Chuan Zhou

Citations in the corpus

• Regionwise Generative Adversarial Image Inpainting for Large Missing Areas (2019, background): “Notable successes include image classification [27]–[29], image generation [30]–[32], representation learning [33], [34], image retrieval [35], [36], object detection [37]–[39], video applications [40], and image translation [41].”
• A Comprehensive Survey on Community Detection With Deep Learning (2021, background): “Similarly, deep network embedding with structural balance preservation (DNE-SBP) [72] incorporates the adjusting weight on pairwise constraints for signed networks so that the stacked AEs cluster the closest nodes distinguished by positive and negative connections.”
• Learning Graph Embedding With Adversarial Training Methods (2019, background): “is proposed for signed network embedding with a stacked autoencoder framework [25].”