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Interferometric Graph Transform for Community Labeling

arXiv 2106.05875 ↗First posted 4 Jun 2021cs.LGcited by 0

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Abstract (arXiv)

We present a new approach for learning unsupervised node representations in community graphs. We significantly extend the Interferometric Graph Transform (IGT) to community labeling: this non-linear operator iteratively extracts features that take advantage of the graph topology through demodulation operations. An unsupervised feature extraction step cascades modulus non-linearity with linear operators that aim at building relevant invariants for community labeling. Via a simplified model, we show that the IGT concentrates around the E-IGT: those two representations are related through some ergodicity properties. Experiments on community labeling tasks show that this unsupervised representation achieves performances at the level of the state of the art on the standard and challenging datasets Cora, Citeseer, Pubmed and WikiCS.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 34 of its 47 references that are in the map. First posted on arXiv 4 Jun 2021.

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34 of 47 references.

References · 34

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22 more
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