The node classification task is one where the algorithm has to determine the labelling of samples (represented as nodes) by looking at the labels of their neighbours.
( Image credit: Fast Graph Representation Learning With PyTorch Geometric )
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Both solve the task of node classification but LPA propagates node label information across the edges of the graph, while GCN propagates and transforms node feature information.
We term this framework as Bilinear Graph Neural Network (BGNN), which improves GNN representation ability with bilinear interactions between neighbor nodes.
Then we propose to use graph convolution on the line graph of a hypergraph.
The richness in the content of various information networks such as social networks and communication networks provides the unprecedented potential for learning high-quality expressive representations without external supervision.
GNNs combine node features, connection patterns, and graph structure by using a neural network to embed node information and pass it through edges in the graph.
However, regardless of the fruitful progress, for some kind of graph applications, such as graph compression and edge partition, it is very hard to reduce them to some graph representation learning tasks.
In this work, we address semi-supervised classification of graph data, where the categories of those unlabeled nodes are inferred from labeled nodes as well as graph structures.