发表机构
School of Computer Science and Technology / School of Artificial Intelligence, China University of Mining and Technology; Mine Digitization Engineering Research Center of the Ministry of Education; Jiangsu Provincial Industrial Technology Engineering Center for Intelligent Sensing and Emergency IoT in Underground Space; School of Electrical Engineering and Computer Science, University of Ottawa（中国矿业大学计算机科学与技术学院/人工智能学院; 教育部矿山数字化工程研究中心; 江苏省地下空间智能传感与应急物联网产业技术工程中心; 渥太华大学电气工程与计算机科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究无监督图域适应问题，提出源节点影响剪枝（SNIP）方法，通过量化结构差异为源节点打分并过滤低影响分数节点，构建精炼“子源”图，实验证明该方法优于基线，验证了选择性节点利用的优越性。
AI 中文摘要
无监督图域适应旨在通过减轻跨域分布偏移，促进从有标签源图到无标签目标图的知识转移。现有方法主要关注潜在空间中的节点级特征对齐，默认所有源节点都对对齐有积极贡献。但因节点语义与拓扑结构内在耦合，该假设常不成立，结构偏差大的源节点会引入噪声导致负迁移。为此提出源节点影响剪枝（SNIP），通过整合多种中心性度量量化源节点与目标域结构差异，为节点分配影响分数，利用基于排名的归一化机制过滤低影响分数节点，构建精炼“子源”图。在五个真实世界数据集的八个转移场景上的综合实验表明，SNIP优于竞争基线，验证了选择性节点利用的优越性。
英文摘要
Unsupervised Graph Domain Adaptation (UGDA) aims to facilitate knowledge transfer from a labeled source graph to an unlabeled target graph by mitigating cross-domain distribution shifts. Existing methods primarily focus on node-level feature alignment in latent spaces, relying on the implicit assumption that all source nodes contribute positively to the alignment. However, this assumption often fails because a node's semantic information is intrinsically coupled with its topological graph structure. Due to structural shifts, source nodes with severe structural deviations (e.g., structural outliers) lack semantic counterparts in the target graph, and forcing alignment on them introduces severe noise and causes negative transfer. To bridge this gap, we argue that selective source node utilization is superior to full-graph training, thereby shifting the research paradigm from feature-level alignment to data-level refinement. To this end, we propose Source Node Influence Pruning (SNIP), a novel model-agnostic, data-centric refinement framework. Specifically, SNIP quantifies the structural discrepancy between individual source nodes and the target domain by integrating multiple centrality measures, assigning each source node an influence score. A rank-based normalization mechanism is further employed to eliminate scale variations across different measures, allowing SNIP to effectively identify and filter out structurally incompatible nodes with low influence scores. As a plug-and-play method, SNIP constructs a refined "sub-source" graph that is inherently more beneficial for subsequent alignment. Comprehensive experiments across eight transfer scenarios on five real-world datasets demonstrate that SNIP consistently outperforms competitive baselines and significantly enhances adaptation performance, validating the superiority of selective node utilization over full-graph training.
Comments12 pages,7 Figures
