Graph learningContrastive learningLink predictionBipartite graphsGraph Neural Network(GNN) has achieved remarkable performance in classification tasks due to its strong distinctive power of different graph topo
Graph contrastive learning (GCL) has attracted extensive research interest due to its powerful ability to capture latent structural and semantic information of graphs in a self-supervised manner. Existing GCL methods commonly adopt predefined graph augmentations to generate two contrastive views. Subsequent...
其中 Graph representation learning 包括了 DeepWalk、LINE、node2vec、LightGCN;Graph contrastive learning 中包括了 SimpleGCL、DGI、GraphCL、GRACE、SGL;Graph generative and adversarial learning 中选择了 GraphGAN、AD-GCL、GraphMAE。 5.1.3 Parameter Settings. 5.1.4 Metrics. 5.2 Node Classification (RQ1) ...
论文名称:Graph Contrastive Learning with Augmentations 摘要 对于当前的图神经网络(GNNs)来说,在图结构数据上的可通用、可转移和鲁棒的表示学习仍然是一个挑战。与用于图像数据的卷积神经网络(CNNs)开发的技术不同,自我监督学习和预训练的探索较少。在本文中,我们提出了一个图对比学习(学习图数据无监督表示框架的...
Contrastive learning has achieved significant improvements over traditional methods in the area of natural language processing [33], [34], computer vision [35], [36], and recommender systems [37], [38]. For example, Hassani and Khasahmadi [39] introduce a self-supervised approach by comparing...
Hassani K, Khasahmadi AH (2020) Contrastive multi-view representation learning on graphs. In: International Conference on Machine Learning, pp. 4116–4126 . PMLR Fang Y, Fan D, Zha D, Tan Q (2024) Gaugllm: Improving graph contrastive learning for text-attributed graphs with large language mo...
I think in your case you need to use a contrastive loss (either hinge or cross entropy), using examples of positive and negative edges. i think the ogbl-biokg doesn’t have features on the nodes, @rusty1s , so I thought @sophiakrix needs to add some if she wants to use a GNN. ...
Contrastive Learning General Classification Graph Classification Graph Learning Graph Neural Network Graph Representation Learning Link Prediction Node Classification Representation Learning Datasets Edit Reddit IMDB-BINARY COLLAB IMDB-MULTI Results from the Paper Edit Submit results from this paper to get sta...
This paper introduces a novel method, M2CHGNN, for generating node representations in heterogeneous graphs by combining an attention mechanism with contrastive learning. The approach leverages meta-structures and meta-paths to capture complex hidden structures within heterogeneous graphs. While meta-paths ...
Link Prediction 如表1所示,在链路预测任务上,S2GAE在8个数据集中的5个上表现都优于生成性(generative)及对比性(contrastive)基线,而在另外3个数据集中的2个上也与基线(baseline)表现相当。此外,S2GAE在大规模的OGB数据集上有很大的性能提升。与MaskGAE相比,S2GAE在5个大规模数据集上获胜,而在3个小数据集上表...