Papers with Code 아카이브
Graph Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 1
- 보존된 평가 기록
- 6
Graph Generation is an important research area with significant applications in drug and material designs.
Source: Graph Deconvolutional Generation
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/shtuplus/pysgg
Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation
https://github.com/tum-daml/magnet
Junction Tree Variational Autoencoder for Molecular Graph Generation
https://github.com/uoguelph-mlrg/ggm-metrics
Efficient Graph Generation with Graph Recurrent Attention Networks
https://github.com/wengong-jin/hgraph2graph
Hierarchical Generation of Molecular Graphs using Structural Motifs
https://github.com/xiyou3368/DGVAE
https://github.com/xygaog/cgc
https://github.com/xzhangfox/ANALYSE-AND-CLASSIFY-MUSIC-BY-LYRICS
https://github.com/zacharie12/zacharie
Learning to Compose Dynamic Tree Structures for Visual Contexts
https://github.com/zhangce01/HiKER-SGG
https://github.com/zhuang-li/factual
DiscoSG: Towards Discourse-Level Text Scene Graph Parsing through Iterative Graph Refinement
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.