SNE: Signed Network Embedding
Document Type
Conference Proceeding
Publication Date
2017
Keywords
Signed network embedding, log-bilinear model, node classification
Abstract
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.
Citation
Yuan S., Wu X., Xiang Y. (2017) SNE: Signed Network Embedding. In: Kim J., Shim K., Cao L., Lee JG., Lin X., Moon YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science, vol 10235. doi: https://doi.org/10.1007/978-3-319-57529-2_15
Comments
Principal Investigator: Xintao Wu
Acknowledgment: The authors acknowledge the support from the National Natural Science Foundation of China (71571136), the 973 Program of China (2014CB340404), and the Research Project of Science and Technology Commission of Shanghai Municipality (16JC1403000, 14511108002) to Shuhan Yuan and Yang Xiang, and from National Science Foundation (1564250) to Xintao Wu. This research was conducted while Shuhan Yuan visited University of Arkansas.