用物理引力模型动态优化图节点表示，提升分类精度
GRAVITY: A Controversial Graph Representation Learning for Vertex Classification
- 模拟物理引力场，基于结构与属性动态调整节点影响范围
- 在多个真实数据集上表现优于传统方法，尤其在跨域任务中
- 适合需要动态建模关系的图学习场景，如社交网络分析
为实现精准的顶点分类，我们提出GRAVITY（基于顶点交互拓扑的图表示学习），受物理系统中物体在吸引力作用下自组织的启发。该框架将每个顶点视为通过由结构邻近性和属性相似性决定的可学习交互施加影响。这些交互在潜在空间中形成势场，使顶点向能量效率更高的位置移动，聚集于类别一致的吸引子附近，远离无关群体。与静态邻域的消息传递机制不同，GRAVITY基于学习的力函数自适应调节每个顶点的感受野，实现由上下文驱动的动态聚合。这种场驱动的组织方式强化了类别边界，并提升了潜在簇内的语义一致性。在真实世界基准测试中的实验表明，GRAVITY生成的嵌入具有竞争力，在归纳式和传承式顶点分类任务中均表现优异。
原文摘要 · Abstract (English)
In the quest of accurate vertex classification, we introduce GRAVITY (Graph-based Representation leArning via Vertices Interaction TopologY), a framework inspired by physical systems where objects self-organize under attractive forces. GRAVITY models each vertex as exerting influence through learned interactions shaped by structural proximity and attribute similarity. These interactions induce a latent potential field in which vertices move toward energy efficient positions, coalescing around class-consistent attractors and distancing themselves from unrelated groups. Unlike traditional message-passing schemes with static neighborhoods, GRAVITY adaptively modulates the receptive field of each vertex based on a learned force function, enabling dynamic aggregation driven by context. This field-driven organization sharpens class boundaries and promotes semantic coherence within latent clusters. Experiments on real-world benchmarks show that GRAVITY yields competitive embeddings, excelling in both transductive and inductive vertex classification tasks.
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