graph-clusteringgraph-contrastive-learningkdd2024 UpdatedJun 26, 2024 Python ✨ Implementation of Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning with pytorch and PyG graph-embeddingsheterogeneous-information-networksgraph-neural-networksgraph-attention-modelheterogeneous-graphheterog...
Pytorch implementation of Influence Augmented Contrastive (IAC) loss and SCGC : Self-Supervised Contrastive Graph Clustering (https://arxiv.org/abs/2204.12656) - gayanku/SCGC
Multi-view Contrastive Graph ClusteringNeurIPS 2021paper Directed Graph Contrastive LearningNeurIPS 2021paper Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited LabelsNeurIPS 2021paper Adversarial Graph Augmentation to Improve Graph Contrastive LearningNeurIPS 2021paper ...
Contrastive Deep Graph Clustering YearTitleVenuePaperCode 2024Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning PerspectiveSIGKDD-link 2024GLAC-GCN: Global and Local Topology-Aware Contrastive Graph Clustering Network (GLAC-GCN)TAIlinklink ...
Contrastive objectives: computes the likelihood score for positive and negative pairs. Negative mining strategies: improves the negative sample set by considering the relative similarity (the hardness) of negative sample. We also implement utilities for training models, evaluating model performance, and ma...
2024 Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective SIGKDD link link 2024 GLAC-GCN: Global and Local Topology-Aware Contrastive Graph Clustering Network (GLAC-GCN) TAI link link 2024 Contrastive Multiview Attribute Graph Clustering With Adaptive Encoders TNNLS...
GCC is acontrastive learningframework that implements unsupervised structural graph representation pre-training and achieves state-of-the-art on 10 datasets on 3 graph mining tasks. Installation Requirements Quick Start Pretraining Pre-training datasets ...
[AAAI 2023] An official source code for paper Hard Sample Aware Network for Contrastive Deep Graph Clustering. - Mitchell-xiyunfeng/HSAN
RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning, IJCAI 2021, Hankook Lee, Sungsoo Ahn, Seung-Woo Seo, You Young Song, Eunho Yang, Sung-Ju Hwang, Jinwoo Shin Accurate Prediction of Free Solvation Energy of Organic Molecules via Graph Attention Network and Message Pa...
In this paper, a self-supervised efficient low-pass contrastive graph clustering (SLCGC) is introduced for HSIs. Our approach begins with homogeneous region generation, which aggregates pixels into spectrally consistent regions to preserve local spatial-spectral coherence while drastically reducing graph...