Node Classification
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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Latest papers without code
Unifying Graph Convolutional Neural Networks and Label Propagation
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.
Vertex-reinforced Random Walk for Network Embedding
In this paper, we study the fundamental problem of random walk for network embedding.
Bilinear Graph Neural Network with Node Interactions
We term this framework as Bilinear Graph Neural Network (BGNN), which improves GNN representation ability with bilinear interactions between neighbor nodes.
Line Hypergraph Convolution Network: Applying Graph Convolution for Hypergraphs
Then we propose to use graph convolution on the line graph of a hypergraph.
MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
A large number of real-world graphs or networks are inherently heterogeneous, involving a diversity of node types and relation types.
Graph Representation Learning via Graphical Mutual Information Maximization
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.
GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION
Explain Graph Neural Networks to Understand Weighted Graph Features in Node Classification
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.
Which way? Direction-Aware Attributed Graph Embedding
Most studies ignore the directionality, so as to learn high-quality representations optimized for node classification.
Graph Ordering: Towards the Optimal by Learning
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.
COMBINATORIAL OPTIMIZATION COMMUNITY DETECTION GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION
Graph Inference Learning for Semi-supervised Classification
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.