Graph Similarity
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/yunshengb/GraphSim
공식 코드Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching
https://github.com/zhuang-li/factual
공식 코드FACTUAL: A Benchmark for Faithful and Consistent Textual Scene Graph Parsing
https://github.com/NJUVISION/GraphSIM
https://github.com/Sangs3112/SimGNN
SimGNN: A Neural Network Approach to Fast Graph Similarity Computation
https://github.com/alexanderhberger/multiclass-bettimatching
clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation
https://github.com/benedekrozemberczki/SimGNN
SimGNN: A Neural Network Approach to Fast Graph Similarity Computation
https://github.com/dsulem/dynnet
Graph similarity learning for change-point detection in dynamic networks
https://github.com/google-research/google-research/tree/master/graph_embedding/ddgk
DDGK: Learning Graph Representations for Deep Divergence Graph Kernels
https://github.com/jacobkoenig/clDice-Loss
clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation
https://github.com/nstucki/betti-matching
clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation
https://github.com/pulkit1joshi/SimGNN
SimGNN: A Neural Network Approach to Fast Graph Similarity Computation
https://github.com/sheryl-ai/MVGCN
https://github.com/sheryl-ai/MemGCN
https://github.com/snap-stanford/neural-subgraph-learning-gnn
https://github.com/yunshengb/UGraphEmb
Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.