A modified GNN architecture with enhanced aggregator and Message Passing Functions

Abstract

Graph neural networks (GNN) uphold the essence of irregularly structured information embedded in a graph via message passing among the nodes and aggregating the node features at various levels of the graph. In the past, researchers have extensively used the GNN models for several semi-supervised node classification tasks. Existing GNN models do not use nodes’ information sufficiently. The use of inter-node feature-level correlational information with the existing GNN models might lead to more powerful learning models. Here, a weighting scheme has been developed for message passing and aggregation functions. This model has been named “Vector GNN”, or in short, “VecGNN”, due to its relationship with vector space. VecGNN takes into consideration the relative position of a node with respect to its neighboring nodes in the feature space, which influences the weight of features passed to the information aggregation phase. These weights are assigned using two different statistical measures: Jaccard’s coefficient and Cosine similarity. The proposed weighting scheme uses a generalized approach that can be easily incorporated into several GNN frameworks. VecGNN is evaluated using three citation datasets: Citeseer, Pubmed, and Cora. On these datasets, three sets of experiments have been conducted with varying numbers of training and testing nodes. We have used training, validation, and test set nodes with ratios of 1:1:8, 2:1:7, and 3:1:6. Experimenting on these, we observe an improvement of 2%–4% over the baseline models: Graph Convolution Network (GCN), Graph Attention Network (GAT), and Jumping Knowledge Networks (JKNets). The source code is available at the link https://github.com/sourodeeproy/VecGNN.