| 摘要: |
| 研究目标是从未标记的图数据中学习健壮的图表示. 开发了一种结构关系建模 (structural relation modeling, SRM)框架, 用于自监督图表示学习, 缓解了由未标记数据和图拓扑不平衡引起的固有限制. 首先, 与大多数现有方法专注于局部结构或节点嵌入不同, 通过在统一框架内对节点、子图和整个图之间的复杂关系(即局部-全局关系和节点相关性)进行建模来捕捉图结构. 这有助于更好地理解图的拓扑结构, 并利用结构自监督信号. 其次, 引入了一种基于分区的子图采样机制, 通过小批量训练缓解了由图拓扑不平衡引起的过度聚合和拓扑衰减. 该机制确保更均匀的信息传播. 第三, 施加了一种节点正则化策略, 以提高训练的稳定性和效率, 产生更精确的结构表示. 对12个公共数据集进行的节点和图分类的广泛实验证明了所提方法的有效性和普适性. |
| 关键词: 自监督图表示学习 图拓扑不平衡 结构关系建模 图结构 子图 |
| DOI：10.13328/j.cnki.jos.007439 |
| 分类号:TP18 |
| 基金项目:国家自然科学基金(U23A20389, 62176139, 62106128, 62176141) |
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| Self-supervised Graph Representation Learning via Structural Relation Modeling |
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QIN Zhe-Yun1, LU Xian-Kai1, NIE Xiu-Shan2, YIN Yi-Long1
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1.School of Software, Shandong University, Jinan 250101, China;2.School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101, China
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| Abstract: |
| This study aims to learn robust graph representations from unlabeled graph data. A novel framework, termed structural relation modeling (SRM), is proposed for self-supervised graph representation learning to alleviate inherent limitations caused by unlabeled data and graph topological imbalances. First, rather than focusing solely on local structures or node embeddings as in most existing methods, this study models complex structural relations, such as local-global relations and node correlations, among nodes, subgraphs, and entire graphs within a unified framework to better capture graph topology and utilize structural self-supervision. Second, a partition-based subgraph sampling mechanism is introduced to mitigate over-aggregation and topological decay induced by graph topological imbalance, enabling more uniform information propagation through mini-batch training. Third, a node regularization strategy is applied to improve training stability and efficiency, resulting in more accurate structural representations. Extensive experiments on node and graph classification across 12 public datasets demonstrate the effectiveness and generalizability of the proposed method. |
| Key words: self-supervised graph representation learning graph topological imbalance structural relation modeling (SRM) graph structure subgraph |
