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New paper reveals fundamental expressivity limits in MP-GNNs

By PulseAugur Editorial · [1 sources] ·

A new research paper titled "Lost in Aggregation: On a Fundamental Expressivity Limit of Message-Passing Graph Neural Networks" by Eran Rosenbluth explores a theoretical limitation in Message-Passing Graph Neural Networks (MP-GNNs). The paper defines an information-complexity property for aggregation functions and proves that MP-GNNs using such functions can only distinguish a polynomial number of graph structures, which is significantly less than the super-exponential number of non-isomorphic graphs. This finding suggests that these MP-GNNs are fundamentally less expressive than even simpler methods like Color Refinement when distinguishing between graphs. AI

IMPACT Highlights theoretical limitations in graph neural network expressivity, potentially guiding future research in graph representation learning.

RANK_REASON The cluster contains a single academic paper detailing theoretical research findings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New paper reveals fundamental expressivity limits in MP-GNNs

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The cluster contains a single academic paper detailing theoretical research findings. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Eran Rosenbluth ·

    Lost in Aggregation: On a Fundamental Expressivity Limit of Message-Passing Graph Neural Networks

    arXiv:2603.14846v3 Announce Type: replace Abstract: We define an information-complexity property for aggregation functions, capturing a vast range of practical aggregations, and prove that any Message-Passing Graph Neural Network (MP-GNN) model with such aggregations induces only…

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