Mr2vec: Multiple Role-Based Social Network Embedding

Learning representation of networks is increasingly important in social network analysis. Network embedding techniques have a wide range of applications in various downstream tasks, such as node classification and clustering. However, many existing approaches focus on representing each node in a network as a single vector, which is often not practical. In this paper, we propose a multiple role-based social network embedding, called mr2vec, by identifying multiple roles of each node through overlapping clustering and encoding each of them to a vector. Our proposed algorithm transforms a given network into the persona graph using overlapping clustering. Each pair of nodes and roles is mapped to a persona node, enabling us to learn multi-role-based embedding using conventional network embedding techniques. Moreover, we develop a new loss function to have close vector representations when the similarity between the nodes in a persona graph is high based not only on the structural information of node, but also on the role information of nodes. We conducted link prediction experiments within the network to demonstrate the effectiveness of representation learning that takes into account the multiple role information discovered through overlapping clustering of each node. Through extensive experiments on social networks, we have shown that mr2vec achieves higher accuracy than the state-of-the-art algorithms for single-or multi-aspect network embedding.