Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification

• URL: http://arxiv.org/abs/2006.15763v1
• Date: Mon, 29 Jun 2020 01:01:42 GMT
• Title: Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification
• Authors: Kai Zhang, Yaokang Zhu, Jun Wang, Jie Zhang, Hongyuan Zha
• Abstract summary: We study the intrinsic difficulty in graph classification under the unified concept of resolution dilemmas'' We propose SLIM'', an inductive neural network model for Structural Landmarking and Interaction Modelling.
• Score: 50.83222170524406
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph neural networks are promising architecture for learning and inference with graph-structured data. Yet difficulties in modelling the ``parts'' and their ``interactions'' still persist in terms of graph classification, where graph-level representations are usually obtained by squeezing the whole graph into a single vector through graph pooling. From complex systems point of view, mixing all the parts of a system together can affect both model interpretability and predictive performance, because properties of a complex system arise largely from the interaction among its components. We analyze the intrinsic difficulty in graph classification under the unified concept of ``resolution dilemmas'' with learning theoretic recovery guarantees, and propose ``SLIM'', an inductive neural network model for Structural Landmarking and Interaction Modelling. It turns out, that by solving the resolution dilemmas, and leveraging explicit interacting relation between component parts of a graph to explain its complexity, SLIM is more interpretable, accurate, and offers new insight in graph representation learning.

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