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New framework scales higher-order graph learning with clique complexes

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new framework for higher-order graph learning that addresses the scalability limitations of existing methods. The approach introduces simplified and factored cellular Weisfeiler Leman tests to enhance computational efficiency while maintaining expressivity. Additionally, a novel maximal clique complex and a biased random walk method called CliqueWalk are proposed to enable scalable learning with reduced time and memory complexity. AI

IMPACT Enables more expressive and scalable analysis of complex relational data, potentially improving performance in areas like drug discovery and social network analysis.

RANK_REASON The cluster contains a research paper detailing a new method for graph learning.

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

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Antoine Vialle, Aref Einizade, Fragkiskos D. Malliaros, Jhony H. Giraldo ·

    Scaling Higher-Order Graph Learning with Maximal Clique Complexes

    arXiv:2605.31373v1 Announce Type: cross Abstract: Graph neural networks (GNNs) are limited to modeling pairwise interactions, while higher-order models based on cell complexes achieve greater expressivity but often suffer from poor scalability. We introduce simplified and factore…

  2. arXiv cs.AI TIER_1 English(EN) · Jhony H. Giraldo ·

    Scaling Higher-Order Graph Learning with Maximal Clique Complexes

    Graph neural networks (GNNs) are limited to modeling pairwise interactions, while higher-order models based on cell complexes achieve greater expressivity but often suffer from poor scalability. We introduce simplified and factored cellular Weisfeiler Leman tests (sCWL and fCWL),…

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