TGNN: A Joint Semi-supervised Framework for Graph-level Classification
- URL: http://arxiv.org/abs/2304.11688v1
- Date: Sun, 23 Apr 2023 15:42:11 GMT
- Title: TGNN: A Joint Semi-supervised Framework for Graph-level Classification
- Authors: Wei Ju, Xiao Luo, Meng Qu, Yifan Wang, Chong Chen, Minghua Deng,
Xian-Sheng Hua, Ming Zhang
- Abstract summary: We propose a novel semi-supervised framework called Twin Graph Neural Network (TGNN)
To explore graph structural information from complementary views, our TGNN has a message passing module and a graph kernel module.
We evaluate our TGNN on various public datasets and show that it achieves strong performance.
- Score: 34.300070497510276
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper studies semi-supervised graph classification, a crucial task with
a wide range of applications in social network analysis and bioinformatics.
Recent works typically adopt graph neural networks to learn graph-level
representations for classification, failing to explicitly leverage features
derived from graph topology (e.g., paths). Moreover, when labeled data is
scarce, these methods are far from satisfactory due to their insufficient
topology exploration of unlabeled data. We address the challenge by proposing a
novel semi-supervised framework called Twin Graph Neural Network (TGNN). To
explore graph structural information from complementary views, our TGNN has a
message passing module and a graph kernel module. To fully utilize unlabeled
data, for each module, we calculate the similarity of each unlabeled graph to
other labeled graphs in the memory bank and our consistency loss encourages
consistency between two similarity distributions in different embedding spaces.
The two twin modules collaborate with each other by exchanging instance
similarity knowledge to fully explore the structure information of both labeled
and unlabeled data. We evaluate our TGNN on various public datasets and show
that it achieves strong performance.
Related papers
- Bridging Domain Adaptation and Graph Neural Networks: A Tensor-Based Framework for Effective Label Propagation [23.79865440689265]
Graph Neural Networks (GNNs) have recently become the predominant tools for studying graph data.
Despite state-of-the-art performance on graph classification tasks, GNNs are overwhelmingly trained in a single domain under supervision.
We propose the Label-Propagation Graph Neural Network (LP-TGNN) framework to bridge the gap between graph data and traditional domain adaptation methods.
arXiv Detail & Related papers (2025-02-12T15:36:38Z) - Revisiting Graph Neural Networks on Graph-level Tasks: Comprehensive Experiments, Analysis, and Improvements [54.006506479865344]
We propose a unified evaluation framework for graph-level Graph Neural Networks (GNNs)
This framework provides a standardized setting to evaluate GNNs across diverse datasets.
We also propose a novel GNN model with enhanced expressivity and generalization capabilities.
arXiv Detail & Related papers (2025-01-01T08:48:53Z) - Learning Strong Graph Neural Networks with Weak Information [64.64996100343602]
We develop a principled approach to the problem of graph learning with weak information (GLWI)
We propose D$2$PT, a dual-channel GNN framework that performs long-range information propagation on the input graph with incomplete structure, but also on a global graph that encodes global semantic similarities.
arXiv Detail & Related papers (2023-05-29T04:51:09Z) - Semi-Supervised Hierarchical Graph Classification [54.25165160435073]
We study the node classification problem in the hierarchical graph where a 'node' is a graph instance.
We propose the Hierarchical Graph Mutual Information (HGMI) and present a way to compute HGMI with theoretical guarantee.
We demonstrate the effectiveness of this hierarchical graph modeling and the proposed SEAL-CI method on text and social network data.
arXiv Detail & Related papers (2022-06-11T04:05:29Z) - CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph
Similarity Learning [65.1042892570989]
We propose a contrastive graph matching network (CGMN) for self-supervised graph similarity learning.
We employ two strategies, namely cross-view interaction and cross-graph interaction, for effective node representation learning.
We transform node representations into graph-level representations via pooling operations for graph similarity computation.
arXiv Detail & Related papers (2022-05-30T13:20:26Z) - KGNN: Harnessing Kernel-based Networks for Semi-supervised Graph
Classification [13.419578861488226]
We propose a Kernel-based Graph Neural Network (KGNN) for semi-supervised graph classification.
We show that KGNN achieves impressive performance over competitive baselines.
arXiv Detail & Related papers (2022-05-21T10:03:46Z) - SHGNN: Structure-Aware Heterogeneous Graph Neural Network [77.78459918119536]
This paper proposes a novel Structure-Aware Heterogeneous Graph Neural Network (SHGNN) to address the above limitations.
We first utilize a feature propagation module to capture the local structure information of intermediate nodes in the meta-path.
Next, we use a tree-attention aggregator to incorporate the graph structure information into the aggregation module on the meta-path.
Finally, we leverage a meta-path aggregator to fuse the information aggregated from different meta-paths.
arXiv Detail & Related papers (2021-12-12T14:18:18Z) - Imbalanced Graph Classification via Graph-of-Graph Neural Networks [16.589373163769853]
Graph Neural Networks (GNNs) have achieved unprecedented success in learning graph representations to identify categorical labels of graphs.
We introduce a novel framework, Graph-of-Graph Neural Networks (G$2$GNN), which alleviates the graph imbalance issue by deriving extra supervision globally from neighboring graphs and locally from graphs themselves.
Our proposed G$2$GNN outperforms numerous baselines by roughly 5% in both F1-macro and F1-micro scores.
arXiv Detail & Related papers (2021-12-01T02:25:47Z) - Multilevel Graph Matching Networks for Deep Graph Similarity Learning [79.3213351477689]
We propose a multi-level graph matching network (MGMN) framework for computing the graph similarity between any pair of graph-structured objects.
To compensate for the lack of standard benchmark datasets, we have created and collected a set of datasets for both the graph-graph classification and graph-graph regression tasks.
Comprehensive experiments demonstrate that MGMN consistently outperforms state-of-the-art baseline models on both the graph-graph classification and graph-graph regression tasks.
arXiv Detail & Related papers (2020-07-08T19:48:19Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.