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arXiv · 2508.08954

GRAVITY: A Controversial Graph Representation Learning for Vertex Classification

Etienne Gael Tajeuna·Jean Marie Tshimula
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Abstract

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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BibTeXRIS

Etienne Gael Tajeuna, Jean Marie Tshimula. 2025-08-12. GRAVITY: A Controversial Graph Representation Learning for Vertex Classification. https://arxiv.org/abs/2508.08954

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