A New Heterogeneous Graph Representation in a Social Media Platform: Steemit

• URL: http://arxiv.org/abs/2209.03144v1
• Date: Fri, 2 Sep 2022 04:59:21 GMT
• Title: A New Heterogeneous Graph Representation in a Social Media Platform: Steemit
• Authors: Negar Maleki, Balaji Padamanabhan, Kaushik Dutta
• Abstract summary: We present a new heterogeneous graph representation including time in every single component of the graph. We also introduce four time-dependent queries to address machine learning or deep learning problems.
• Score: 2.127049691404299
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, temporal graphs have substituted dynamic graphs as many real-world problems evolve in continuous time rather than in discrete time, and besides time almost all problems are designed in a heterogeneous format rather than a homogeneous one. However, most existing graph representations do not consider time in their components. To this end, in this paper, we present a new heterogeneous graph representation including time in every single component of the graph, i.e., nodes and edges. We also introduce four time-dependent queries to address machine learning or deep learning problems. Our findings reveal that considering the size of the enormous graphs, our time-dependent queries execute efficiently. In order to show the expressive power of time in graph representation, we construct a graph for a new social media platform (Steemit), and address a DL prediction task using graph neural networks (GNNs). Predicting the payout for a newly published post is one of the most fascinating classification problems in the Steemit setting, and we address this problem with two approaches followed by GNN models.

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