Representing Social Networks as Dynamic Heterogeneous Graphs

Graph representations for real-world social networks in the past have missed two important elements: (i) the multiplexity of connections and (ii) representing time. This paper presents a dynamic heterogeneous graph representation for social networks which includes time in every component of the graph, i.e., nodes and edges, each of different types that captures heterogeneity. We illustrate the power of this representation by presenting four time-dependent queries and deep learning problems that cannot easily be handled in conventional homogeneous graph representations. As a proof of concept we present a detailed representation of a relatively new social media platform (Steemit), which we use to illustrate both the dynamic querying capability as well as prediction tasks using Graph Neural Networks (GNNs). We also illustrate opportunities for future work in query optimization as well as new dynamic prediction tasks on heterogeneous graph structures.