GRAVITY: A Controversial Graph Representation Learning for Vertex Classification
This addresses vertex classification for graph learning applications, presenting a novel but incremental approach.
The paper tackles vertex classification in graphs by introducing GRAVITY, a framework that models vertices as interacting under learned forces to self-organize into class-consistent clusters, achieving competitive performance on real-world benchmarks in both transductive and inductive tasks.
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.