https://github.com/dmlc/dgl/tree/master/examples/mxnet/monet
Geometric deep learning on graphs and manifolds using mixture model CNNs
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Graph Classification is a task that involves classifying a graph-structured data into different classes or categories. Graphs are a powerful way to represent relationships and interactions between different entities, and graph classification can be applied to a wide range of applications, such as social network analysis, bioinformatics, and recommendation systems. In graph classification, the input is a graph, and the goal is to learn a classifier that can accurately predict the class of the graph.
<span style="color:grey; opacity: 0.6">( Image credit: Hierarchical Graph Pooling with Structure Learning )</span>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Geometric deep learning on graphs and manifolds using mixture model CNNs
Composition-based Multi-Relational Graph Convolutional Networks
Hierarchical Graph Representation Learning with Differentiable Pooling
GNNExplainer: Generating Explanations for Graph Neural Networks
Graph Representation Learning via Hard and Channel-Wise Attention Networks
Supervised Community Detection with Line Graph Neural Networks
Geometric deep learning on graphs and manifolds using mixture model CNNs
Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning
Semi-Supervised Classification with Graph Convolutional Networks
Do Transformers Really Perform Bad for Graph Representation?
Variational Recurrent Neural Networks for Graph Classification
Semi-Supervised Classification with Graph Convolutional Networks
Semi-Supervised Classification with Graph Convolutional Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.