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Graph Neural Networks (GNNs) have led to state-of-the-art performance on avariety of machine learning tasks such as recommendation, node classificationand link prediction. Graph neural network models generate node embeddings bymerging nodes features with the aggregated neighboring nodes information. Mostexisting GNN models exploit a single type of aggregator (e.g., mean-pooling) toaggregate neighboring nodes information, and then add or concatenate the outputof aggregator to the current representation vector of the center node. However,using only a single type of aggregator is difficult to capture the differentaspects of neighboring information and the simple addition or concatenationupdate methods limit the expressive capability of GNNs. Not only that, existingsupervised or semi-supervised GNN models are trained based on the loss functionof the node label, which leads to the neglect of graph structure information.In this paper, we propose a novel graph neural network architecture, GraphAttention \& Interaction Network (GAIN), for inductive learning on graphs.Unlike the previous GNN models that only utilize a single type of aggregationmethod, we use multiple types of aggregators to gather neighboring informationin different aspects and integrate the outputs of these aggregators through theaggregator-level attention mechanism. Furthermore, we design a graphregularized loss to better capture the topological relationship of the nodes inthe graph. Additionally, we first present the concept of graph featureinteraction and propose a vector-wise explicit feature interaction mechanism toupdate the node embeddings. We conduct comprehensive experiments on twonode-classification benchmarks and a real-world financial news dataset. Theexperiments demonstrate our GAIN model outperforms current state-of-the-artperformances on all the tasks.

GAIN: Graph Attention & Interaction Network for Inductive Semi-Supervised Learning over Large-scale Graphs