Graph Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 72
- 보존된 평가 기록
- 808
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>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ykrmm/sota_gnn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/yshenaw/GF_CF
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
https://github.com/ytchx1999/GCN_res-FLAG
Robust Optimization as Data Augmentation for Large-scale Graphs
https://github.com/ytchx1999/Graphormer/tree/main/examples/ogb
Do Transformers Really Perform Bad for Graph Representation?
https://github.com/yujiali/ggnn
https://github.com/yunshengb/UGraphEmb
Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity
https://github.com/yururyuru00/GAT_pyG
https://github.com/zhangbo2008/GAT_network
https://github.com/zhanglab-aim/cancer-net
https://github.com/zhao-tong/GNNs-easy-to-use
https://github.com/zhiqiang00/Hon-GCN
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/zhliping/Deep-Learning
https://github.com/zml72062/dr-fwl-2
https://github.com/zxhhh97/ABot
https://gitlab.com/birder/birder
https://worksheets.codalab.org/worksheets/0xfafccca55b584e6eb1cf71979ad8e778
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.