This paper introduces Implicit Line-Graph Higher-Order Weisfeiler--Leman (ILG-k-WL), a method for analyzing graph isomorphism by operating on the line graph relations derived from endpoint incidence without explicit construction. The research investigates the relationship between the expressivity of k-WL on line graphs and their root graphs, finding that for k=1,2, ILG-k-WL offers no additional distinguishing power. However, for k=3, ILG-3-WL demonstrates strictly greater expressivity than standard 3-WL, as evidenced by its ability to separate strongly regular witness pairs like the Shrikhande/rook pair. AI
IMPACT Introduces a novel theoretical framework for graph analysis, potentially impacting future research in graph neural networks and AI-driven pattern recognition.
RANK_REASON The cluster contains a single academic paper detailing a new theoretical framework for graph isomorphism testing. [lever_c_demoted from research: ic=1 ai=0.7]
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