SAC-GCL: Structure-Aware Compression for Graph Contrastive Learning
Authors: Muhammad Ifte khairul Islam, Tanvir Hossain, Esra Akbas
Organizations: Department of Computer Science, Texas A&M University- Texarkana, Texarkana,Texas, USA · Department Of Computer Science, Georgia State University, Atlanta, GA, USA · Department Of Computer Science, Georgia State University, Atlanta, Georgia, USA
Graph Contrastive Learning (GCL) has achieved remarkable success in self-supervised representation learning by maximizing agreement between augmented views of a graph. Although augmented view generation is the central step in GCL, existing models rely on stochastic augmentation of the input graph, such as edge perturbation and node masking, which lack structural awareness and frequently destroy critical topology or introduce semantically inconsistent views. While recent adaptive methods improve upon random perturbations, they primarily operate on localized node or edge neighborhoods and fail to capture higher-order, macro-level structural organization crucial for graph classification. To address this limitation, we propose Structure-aware Compression for Graph Contrastive Learning (SAC-GCL), a novel framework that employs structural decomposition and graph compression as a principled view generation strategy. Moving beyond random augmentations, SAC-GCL leverages structural decomposition techniques to extract cohesive subgraphs and condense them into supernodes, constructing coarsened graph views that preserve local density and capture higher-level structural patterns while reducing noise and redundancy. We leverage multiple cohesive subgraph models to derive complementary views of the graph, each capturing distinct structural properties. To capture multi-resolution information, a hierarchical view encoder processes these compressed views by initializing super-node features with shared-weight cohesive subgraph embeddings. Our results from extensive experiments on six benchmark datasets demonstrate that structure-aware compression provides a powerful alternative to random perturbations, yielding more robust and discriminative graph representations for graph classification tasks.
Figures & tables
Fig. 1 : Model Architecture: Graph Contrastive Learning with Structure-Aware Graph Compression
Fig. 2 : Graph View Generation via Cohesive Subgraph Compression. Input graphs are decomposed into cohesive k -core and k -truss subgraphs with a size threshold. Extracted cohesive subgraphs are contracted into super-nodes to construct compressed graph views
Dataset
∣G∣
Vavg
Eavg
∣C∣
D&D
1178
284.32
715.66
2
PROTEIN
1113
39.06
72.82
2
IMDB-BINARY
1000
19.77
96.53
2
IMDB-MULTI
1500
13.00
65.94
3
REDDIT-BINARY
2000
429.63
497.75
2
COLLAB
5000
74.49
2457.78
3
TABLE I: Dataset statistics. ∣G∣ is the number of graphs, Vavg and Eavg denote the average number of nodes and edges per graph, and ∣C∣ is the number of classes.
Method
Model
IMDB-B
IMDB-M
REDDIT-B
COLLAB
D&D
PROTEIN
Supervised Learning
gPool
73.40
50.27
74.70
77.58
75.0
71.9
SAGPool
73.00
49.43
84.66
70.10
75.7
72.5
MinCutPool
70.78
52.8
75.67
69.91
76.7
76.3
DiffPool
68.40
45.62
66.65
74.83
66.9
68.2
ASAP
72.74
-
-
78.95
76.9
74.2
MPool
74.20
50.26
84.10
83.62
81.2
79.30
TABLE II: Graph Classification Performance (Accuracy % ). The best results are highlighted in bold , the second-best results are underlined .
Dataset
SAC-GCL
SAC-GCLsup
SAC-GCLcore
SAC-GCLtruss
IMDB-B
78.81
75.10
74.95
75.07
IMDB-M
52.88
50.26
50.15
49.88
REDDIT-B
89.65
88.20
86.32
87.54
COLLAB
86.70
84.25
83.65
83.79
D&D
83.43
80.85
79.54
80.32
PROTEIN
81.42
77.63
75.77
75.56
TABLE III : Performance comparisons with the main model ( SAC-GCL ), the model with supervised loss ( SAC-GCLsup ), the model with k -core view encoder ( SAC-GCLcore ), and the model with k -truss view encoder ( SAC-GCLtruss ).
Fig. 3 : Performance of SAC-GCL for different values of the contrastive loss weight α on D&D and IMDB-B.
Fig. 4 : Performance of SAC-GCL for different subgraph size thresholds λ on D&D and IMDB-B.
Graph contrastive learning trains graph representations by aligning related nodes or graph views while separating structurally or semantically different examples.
Graph Contrastive Learning (GCL) has emerged as a prominent framework for unsupervised graph representation learning. However, relying on augmentation design alone to define the invariances learned by GCL can be brittle under structural perturbations. To address this issue, we propose Cheeger--Hodge Contrastive Learning (CHCL), a framework that aligns a perturbation-stable Cheeger--Hodge joint signature across augmented views for robust graph representation learning. The proposed signature combines a Cheeger-inspired connectivity signature derived from the algebraic connectivity λ2 with the low-frequency spectrum of the 1-Hodge Laplacian, thereby capturing both global connectivity and higher-order structural information. By aligning encoder representations with the proposed Cheeger--Hodge joint signature across augmented views, CHCL learns graph embeddings that are robust to local structural perturbations. Extensive experiments on standard benchmarks, transfer settings demonstrate that CHCL consistently improves performance, robustness, and generalization.
Mengyang Zhao, Longlong Li, Cunquan Qu
School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore · Data Science Institute, Shandong University, Jinan 250100, China
Self-supervised Continual Graph Learning (CGL) aims to successively learn from a graph sequence with different tasks without label supervision - a paradigm that has attracted widespread attention. Most existing self-supervised CGL methods rely on instance-level consistency objectives that enforce stability of individual node (or node-pair) embeddings. Due to optimizing nodes in isolation, these methods fail to maintain global relational structure, causing inter-node correspondences to progressively distort under continual learning. To this end, we propose a novel Structure-Aware Optimal Transport (SAOT) framework that explicitly captures and preserves relational structure within graph representations across sequential tasks. Specifically, SAOT leverages optimal transport theory to capture global inter-node correspondences, thereby facilitating and enhancing graph representation learning. Simultaneously, SAOT incorporates a cross-task knowledge distillation mechanism to preserve the previous structural knowledge. Extensive experiments on four CGL benchmark datasets demonstrate that SAOT outperforms existing self-supervised baselines. In particular, SAOT achieves significant performance gains, improving average accuracy by up to 5% on CoraFull-CL and over 15% on Products-CL compared with state-of-the-art methods in the Class-IL setting.
Yuting Zhang, Yanbei Liu, Zhitao Xiao +3
School of Electronics and Information Engineering, Tiangong University, Tianjin, China · School of Life Sciences, Tiangong University, Tianjin, China · School of Electrical and Infomation Engineering, Tianjin University, Tianjin, China +1
Graph clustering is essential in graph analysis for revealing structural patterns and node communities. Despite recent advances in self-supervised contrastive learning that have improved clustering via structural and attribute signals, existing methods still struggle to flexibly capture high-order local structures and often overlook global semantics in complex graphs. These limitations lead to suboptimal node representations, especially in real-world graphs with fragmented structures and ambiguous cluster boundaries. To address these limitations, a contrastive graph clustering framework is proposed to jointly integrate multi-scale local structures with global semantics via attention mechanisms. At the local level, GNN-based topological signals extracted from multiple propagation depths are adaptively fused through attention-based weighting to capture multi-scale neighborhood features. At the global level, semantic prototypes derived from dynamically evolving cluster centers are adaptively aggregated through attention to guide node representations and enhance inter-cluster separability. The model is trained under a dual-view contrastive learning paradigm with a hybrid objective that combines instance-level and structure-aware losses to improve representation robustness and discrimination. Experiments on eight real-world graph datasets demonstrate that our method achieves competitive clustering performance. Code is available at https://github.com/vege12138/w2.
Lei Zhang, Fubo Sun, Haipeng Yang +2
School of Computer Science and Technology, Anhui University · College of Management and Economics, Tianjin University