Graph representation learning is crucial for many real-world ap- plications (e.g. social relation analysis). A fundamental problem for graph representation learning is how to effectively learn rep- resentations without human labeling, which is usually costly and time-consuming. Graph contrastive learning (GCL) addresses this problem by pulling the positive node pairs (or similar nodes) closer while pushing the negative node pairs (or dissimilar nodes) apart in the representation space. Despite the success of the existing GCL methods, they primarily sample node pairs based on the node- level proximity yet the community structures have rarely been taken into consideration. As a result, two nodes from the same community might be sampled as a negative pair. We argue that the community information should be considered to identify node pairs in the same communities, where the nodes insides are seman- tically similar. To address this issue, we propose a novel Graph Communal Contrastive Learning (ππΆπππΏ) framework to jointly learn the community partition and learn node representations in an end-to-end fashion. Specifically, the proposed ππΆπππΏ consists of two components: a Dense Community Aggregation (π·ππΆπ΄) algo- rithm for community detection and a Reweighted Self-supervised Cross-contrastive (π
πππΆ) training scheme to utilize the community information. Additionally, the real-world graphs are complex and often consist of multiple views. In this paper, we demonstrate that the proposed ππΆπππΏ can also be naturally adapted to multiplex graphs. Finally, we comprehensively evaluate the proposed ππΆπππΏ on a variety of real-world graphs. The experimental results show that the ππΆπππΏ outperforms the state-of-the-art methods.
more »
« less
This content will become publicly available on August 20, 2025
Enhancing Graph Contrastive Learning with Node Similarity.
Graph Neural Networks (GNN) have proven successful for graph-related tasks. However, many GNNs methods require labeled data, which is challenging to obtain. To tackle this, graph contrastive learning (GCL) have gained attention. GCL learns by contrasting similar nodes (positives) and dissimilar nodes (negatives). Current GCL methods, using data augmentation for positive samples and random selection for negative samples, can be sub-optimal due to limited positive samples and the possibility of false-negative samples. In this study, we propose an enhanced objective addressing these issues. We first introduce an ideal objective with all positive and no false-negative samples, then transform it probabilistically based on sampling distributions. We next model these distributions with node similarity and derive an enhanced objective. Comprehensive experiments have shown the effectiveness of the proposed enhanced objective for a broad set of GCL models.
more »
« less
- Award ID(s):
- 2406648
- PAR ID:
- 10544869
- Publisher / Repository:
- KDD
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)Network alignment plays an important role in a variety of applications. Many traditional methods explicitly or implicitly assume the alignment consistency which might suffer from over-smoothness, whereas some recent embedding based methods could somewhat embrace the alignment disparity by sampling negative alignment pairs. However, under different or even competing designs of negative sampling distributions, some methods advocate positive correlation which could result in false negative samples incorrectly violating the alignment consistency, whereas others champion negative correlation or uniform distribution to sample nodes which may contribute little to learning meaningful embeddings. In this paper, we demystify the intrinsic relationships behind various network alignment methods and between these competing design principles of sampling. Specifically, in terms of model design, we theoretically reveal the close connections between a special graph convolutional network model and the traditional consistency based alignment method. For model training, we quantify the risk of embedding learning for network alignment with respect to the sampling distributions. Based on these, we propose NeXtAlign which strikes a balance between alignment consistency and disparity. We conduct extensive experiments that demonstrate the proposed method achieves significant improvements over the state-of-the-arts.more » « less
-
more » « less
Contrastive learning is an effective unsupervised method in graph representation learning. The key component of contrastive learning lies in the construction of positive and negative samples. Previous methods usually utilize the proximity of nodes in the graph as the principle. Recently, the data-augmentation-based contrastive learning method has advanced to show great power in the visual domain, and some works have extended this method from images to graphs. However, unlike the data augmentation on images, the data augmentation on graphs is far less intuitive and it is much harder to provide high-quality contrastive samples, which leaves much space for improvement. In this work, by introducing an adversarial graph view for data augmentation, we propose a simple but effective method,
Adversarial Graph Contrastive Learning (ArieL ), to extract informative contrastive samples within reasonable constraints. We develop a new technique calledinformation regularization for stable training and use subgraph sampling for scalability. We generalize our method from node-level contrastive learning to the graph level by treating each graph instance as a super-node.ArieL consistently outperforms the current graph contrastive learning methods for both node-level and graph-level classification tasks on real-world datasets. We further demonstrate thatArieL is more robust in the face of adversarial attacks. -
Self-supervised learning of graph neural networks (GNN) is in great need because of the widespread label scarcity issue in real-world graph/network data. Graph contrastive learning (GCL), by training GNNs to maximize the correspondence between the representations of the same graph in its different augmented forms, may yield robust and transferable GNNs even without using labels. However, GNNs trained by traditional GCL often risk capturing redundant graph features and thus may be brittle and provide sub-par performance in downstream tasks. Here, we propose a novel principle, termed adversarial-GCL (\textit{AD-GCL}), which enables GNNs to avoid capturing redundant information during the training by optimizing adversarial graph augmentation strategies used in GCL. We pair AD-GCL with theoretical explanations and design a practical instantiation based on trainable edge-dropping graph augmentation. We experimentally validate AD-GCL by comparing with the state-of-the-art GCL methods and achieve performance gains of up-to~14\% in unsupervised, ~6\% in transfer and~3\% in semi-supervised learning settings overall with 18 different benchmark datasets for the tasks of molecule property regression and classification, and social network classification.more » « less
-
Self-supervised learning of graph neural networks (GNN) is in great need because of the widespread label scarcity issue in real-world graph/network data. Graph contrastive learning (GCL), by training GNNs to maximize the correspondence between the representations of the same graph in its different augmented forms, may yield robust and transferable GNNs even without using labels. However, GNNs trained by traditional GCL often risk capturing redundant graph features and thus may be brittle and provide sub-par performance in downstream tasks. Here, we propose a novel principle, termed adversarial-GCL (\textit{AD-GCL}), which enables GNNs to avoid capturing redundant information during the training by optimizing adversarial graph augmentation strategies used in GCL. We pair AD-GCL with theoretical explanations and design a practical instantiation based on trainable edge-dropping graph augmentation. We experimentally validate AD-GCL by comparing with the state-of-the-art GCL methods and achieve performance gains of up-to~14\% in unsupervised, ~6\% in transfer and~3\% in semi-supervised learning settings overall with 18 different benchmark datasets for the tasks of molecule property regression and classification, and social network classification.more » « less
