Continuous-Time Dynamic Network Embeddings

Giang Hoang Nguyen, John Boaz Lee, Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh, Sungchul Kim
2018 Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18  
Networks evolve continuously over time with the addition, deletion, and changing of links and nodes. Although many networks contain this type of temporal information, the majority of research in network representation learning has focused on static snapshots of the graph and has largely ignored the temporal dynamics of the network. In this work, we describe a general framework for incorporating temporal information into network embedding methods. The framework gives rise to methods for learning
more » ... time-respecting embeddings from continuous-time dynamic networks. Overall, the experiments demonstrate the effectiveness of the proposed framework and dynamic network embedding approach as it achieves an average gain of 11.9% across all methods and graphs. The results indicate that modeling temporal dependencies in graphs is important for learning appropriate and meaningful network representations.
doi:10.1145/3184558.3191526 dblp:conf/www/NguyenLRAKK18 fatcat:c57uz7xqereczhtvghak66apbq