New paper reveals fundamental expressivity limits in MP-GNNs

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 →

• Eran Rosenbluth
• Message-Passing Graph Neural Networks

• paper
• other

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