SNE: Signed Network Embedding

By Shuhan Yuan, Xintao Wu, Yang Xiang. 2017

147 citations recorded by the source corpus.

Several network embedding models have been developed for unsigned networks. However, these models based on skip-gram cannot be applied to signed networks because they can only deal with one type of link. In this paper, we present our signed network embedding model called SNE. Our SNE adopts the log-bilinear model, uses node representations of all nodes along a given path, and further incorporates two signed-type vectors to capture the positive or negative relationship of each edge along the path. We conduct two experiments, node classification and link prediction, on both directed and undirected signed networks and compare with four baselines including a matrix factorization method and three state-of-the-art unsigned network embedding models. The experimental results demonstrate the effectiveness of our signed network embedding.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

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. Xing Xie
2. Hongwei Wang
3. Fuzheng Zhang
4. Minyi Guo
5. Jiliang Tang
6. Tyler Derr
7. Yao Ma
8. Rik Sarkar

In Statistical and Nonlinear Physics

1. Rik Sarkar
2. Yao Ma
3. Tyler Derr
4. Xing Xie
5. Minyi Guo
6. Jiliang Tang
7. Ryan Davies
8. Fuzheng Zhang

Citations in the corpus

• SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction (2017, background): “ address unsigned and homogeneous networks. Additionally, several studies focus on representation learning in the scenario of heterogeneous network [3, 32], attributed network [10], or signed network [29, 33]. However, these methods are specialized in only one particular type of networks, which is n”
• GEMSEC: Graph Embedding with Self Clustering (2018, background): “Specifically, there is work looking at graphs with signed edges [35] and rooted subgraphs [18].”
• Signed Graph Convolutional Networks (2018, methodology): “Later, SiNE [19], which developed a deep learning framework, and SNE [27], that uses a log-bilinear model with random walks, were developed.”