AI RESEARCH
SWING: Unlocking Implicit Graph Representations for Graph Random Features
arXiv CS.LG
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ArXi:2602.12703v2 Announce Type: replace We propose SWING: Space Walks for Implicit Network Graphs, a new class of algorithms for computations involving Graph Random Features on graphs given by implicit representations (i-graphs), where edge-weights are defined as bi-variate functions of feature vectors in the corresponding nodes. Those classes of graphs include several prominent examples, such as: $\epsilon$-neighborhood graphs, used on regular basis in machine learning.