AI RESEARCH
Energy-Balanced Hyperspherical Graph Representation Learning via Structural Binding and Entropic Dispersion
arXiv CS.LG
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ArXi:2512.24062v2 Announce Type: replace Graph Representation Learning (GRL) can be fundamentally modeled as a physical process of seeking an energy equilibrium state for a node system on a latent manifold. However, existing Graph Neural Networks (GNNs) often suffer from uncontrolled energy dissipation during message passing, driving the system towards a state of Thermal Death--manifested as feature collapse or over-smoothing--due to the absence of explicit thermodynamic constraints.