COSINE: Community-Preserving Social Network Embedding From Information Diffusion Cascades

Authors

  • Yuan Zhang Peking University
  • Tianshu Lyu Peking University
  • Yan Zhang Peking University

Abstract

This paper studies the problem of social network embedding without relying on network structures that are usually not observed in many cases. We address that the information diffusion process across networks naturally reflects rich proximity relationships between users. Meanwhile, social networks contain multiple communities regularizing communication pathways for information propagation. Based on the above observations, we propose a probabilistic generative model, called COSINE, to learn community-preserving social network embeddings from the recurrent and time-stamped social contagion logs, namely information diffusion cascades. The learned embeddings therefore capture the high-order user proximities in social networks. Leveraging COSINE, we are able to discover underlying social communities and predict temporal dynamics of social contagion. Experimental results on both synthetic and real-world datasets show that our proposed model significantly outperforms the existing approaches.

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Published

2018-04-26

How to Cite

Zhang, Y., Lyu, T., & Zhang, Y. (2018). COSINE: Community-Preserving Social Network Embedding From Information Diffusion Cascades. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11856

Issue

Section

Main Track: Machine Learning Applications