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New research unifies relational and temporal GNN expressivity

By PulseAugur Editorial · [2 sources] ·

Researchers have explored the expressivity of sparse graph neural networks (GNNs) within the framework of the Strong Expressive Lottery Ticket Hypothesis (SELTH). The study generalizes this hypothesis to multi-relational and temporal graph domains, introducing the concept of the relational Weisfeiler-Leman (RWL) algorithm. The findings indicate that sufficiently parameterized relational GNNs can contain sparse subnetworks that maintain 1-RWL expressivity, with a derived lower bound on the probability of achieving this through random pruning. The research also demonstrates that common temporal GNNs and cross-graph message passing schemes can be reformulated as RGNNs, inheriting these expressivity guarantees and connecting sparse RGNN expressivity to their optimization behavior. AI

IMPACT This research could lead to more efficient and expressive graph neural networks by better understanding the role of parameter sparsity.

RANK_REASON The cluster contains an academic paper detailing theoretical advancements in graph neural networks.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New research unifies relational and temporal GNN expressivity

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The cluster contains an academic paper detailing theoretical advancements in graph neural networks.
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COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    A Unifying Relational Perspective on Expressive Lottery Tickets

    Graph neural networks (GNNs) are widely used, but how parameter sparsity affects the expressivity of relational (RGNNs) and temporal (TGNNs) variants is poorly understood. The Strong Expressive Lottery Ticket Hypothesis (SELTH) posits the existence of sparse GNNs that preserve We…

  2. arXiv stat.ML TIER_1 English(EN) · Lorenz Kummer, Samir Moustafa, Anatol Ehrlich, Franka Bause, Marco Nennstiel, Przemys{\l}aw Andrzej Wa{\l}\c{e}ga, Nils Morten Kriege ·

    A Unifying Relational Perspective on Expressive Lottery Tickets

    arXiv:2608.18819v1 Announce Type: cross Abstract: Graph neural networks (GNNs) are widely used, but how parameter sparsity affects the expressivity of relational (RGNNs) and temporal (TGNNs) variants is poorly understood. The Strong Expressive Lottery Ticket Hypothesis (SELTH) po…

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