  Graph Neural Networks (GNNs) have led to state-of-the-art performance on a
variety of machine learning tasks such as recommendation, node classification
and link prediction. Graph neural network models generate node embeddings by
merging nodes features with the aggregated neighboring nodes information. Most
existing GNN models exploit a single type of aggregator (e.g., mean-pooling) to
aggregate neighboring nodes information, and then add or concatenate the output
of aggregator to the current representation vector of the center node. However,
using only a single type of aggregator is difficult to capture the different
aspects of neighboring information and the simple addition or concatenation
update methods limit the expressive capability of GNNs. Not only that, existing
supervised or semi-supervised GNN models are trained based on the loss function
of the node label, which leads to the neglect of graph structure information.
In this paper, we propose a novel graph neural network architecture, Graph
Attention \& Interaction Network (GAIN), for inductive learning on graphs.
Unlike the previous GNN models that only utilize a single type of aggregation
method, we use multiple types of aggregators to gather neighboring information
in different aspects and integrate the outputs of these aggregators through the
aggregator-level attention mechanism. Furthermore, we design a graph
regularized loss to better capture the topological relationship of the nodes in
the graph. Additionally, we first present the concept of graph feature
interaction and propose a vector-wise explicit feature interaction mechanism to
update the node embeddings. We conduct comprehensive experiments on two
node-classification benchmarks and a real-world financial news dataset. The
experiments demonstrate our GAIN model outperforms current state-of-the-art
performances on all the tasks.
