GraMMy: Graph representation learning based on micro–macro analysis

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Research Article

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Graph Neural Networks (GNNs) are robust variants of deep network models, typically designed to learn from graph-structured data. Despite the recent advancement of GNNs, the basic message passing scheme of learning often holds back these models in effectively capturing the influence of the nodes from higher order neighbourhood. Further, the state-of-the-art approaches mostly ignore the contextual significance of the paths through which the message/information propagates to a node. In order to deal with these two issues, we propose GraMMY as a novel framework for hierarchical semantics-driven graph representation learning based on Micro-Macro analysis. The key idea here is to study the graph structure from different levels of abstraction, which not only provides an opportunity for flexible flow of information from both local and higher-order neighbours but also helps in more concretely capturing how information travels within various hierarchical structures of the graph. We incorporate the knowledge gained from micro and macro level semantics into the embedding of a node and use this to perform graph classification. Experimentations on four bio-informatics and two social datasets exhibit the superiority of GraMMy over state-of-the-art GNN-based graph classifiers.

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