dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learning

Understanding and analyzing graphs is an essential topic that has been widely studied over the past decades. Many real world problems can be formulated as link predictions in graphs (Gehrke, Ginsparg, and Kleinberg 2003; Freeman 2000; Theocharidis et al. 2009; Goyal, Sapienza, and Ferrara 2018). For example, link prediction in an author collaboration network (Gehrke, Ginsparg, and Kleinberg 2003) can be used to predict potential future author collaboration. Similarly, new connections between proteins can be discovered using protein interaction networks (Pavlopoulos, Wegener, and Schneider 2008), and new friendships can be predicted using social networks (Wasserman and Faust 1994). Recent work on obtaining such predictions use graph representation learning. These methods represent each node in the network with a fixed dimensional embedding, and map link prediction in the network space to a nearest neighbor search in the embedding space (Goyal and Ferrara 2018). It has been shown that such techniques can outperform traditional link prediction methods on graphs (Grover and Leskovec 2016; Ou et al. 2016a).