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Although Graph Convolutional Networks (GCNs) have demonstrated their power invarious applications, the graph convolutional layers, as the most importantcomponent of GCN, are still using linear transformations and a simple poolingstep. In this paper, we propose a novel generalization of Factorized Bilinear(FB) layer to model the feature interactions in GCNs. FB performs twomatrix-vector multiplications, that is, the weight matrix is multiplied withthe outer product of the vector of hidden features from both sides. However,the FB layer suffers from the quadratic number of coefficients, overfitting andthe spurious correlations due to correlations between channels of hiddenrepresentations that violate the i.i.d. assumption. Thus, we propose a compactFB layer by defining a family of summarizing operators applied over thequadratic term. We analyze proposed pooling operators and motivate their use.Our experimental results on multiple datasets demonstrate that the GFB-GCN iscompetitive with other methods for text classification.

Graph Convolutional Network with Generalized Factorized Bilinear Aggregation