Representing Social Networks as Dynamic Heterogeneous Graphs

TL;DR

This paper introduces a novel dynamic heterogeneous graph model for social networks that incorporates time into all components, enabling advanced queries and predictions with GNNs, demonstrated on a social media platform.

Contribution

It presents a new dynamic heterogeneous graph representation for social networks that includes temporal information in nodes and edges, facilitating complex time-dependent analysis.

Findings

Effective dynamic querying demonstrated on Steemit data

Successful prediction tasks using graph neural networks

Illustrated future research directions in query optimization

Abstract

Graph representations for real-world social networks in the past have missed two important elements: the multiplexity of connections as well as representing time. To this end, in this paper, we present a new dynamic heterogeneous graph representation for social networks which includes time in every single 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 commonly used. As a proof of concept we present a detailed representation of a 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). The results illustrate the power of the dynamic heterogeneous graph representation to model social networks. Given that this is a relatively understudied area we also illustrate opportunities for future work in query optimization as well as new dynamic prediction tasks on heterogeneous graph structures.