Structural Landmarking and Interaction Modelling: A “SLIM” Network for Graph Classification

Graph neural networks are a promising architecture for learning and inference with graph-structured data. Yet, how to generate informative, fixed-dimensional graph-level features for graphs with varying size and topology can still be challenging. Typically, this is achieved through graph-pooling, which summarizes a graph by compressing all its nodes into a single vector after convolutional operations. Is such a “collapsing-style” graph-pooling the only choice for graph classification? From complex system’s point of view, properties of a complex system arise largely from the interaction among its components. Therefore, we speculate that preserving the interacting relation between parts, instead of pooling them together, could benefit system-level prediction. To verify this, we propose SLIM, a graph neural network model for Structural Landmarking and Interaction Modelling. The main idea is to compute a set of end-to-end optimizable sub-structure landmarks, so that any input graph can be projected onto these (spatially) local structural representatives for a faithful, global characterization. By doing this, explicit interaction between component parts of a graph can be leveraged directly in generating useful graph-level representations despite significant topological variations. Encouraging results are observed on benchmark datasets for graph classification, demonstrating the value of interaction modelling in the design of graph neural networks.