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On the Power of the Weisfeiler-Leman Test for Graph Motif Parameters

Matthias Lanzinger, Pablo Barceló

TL;DR

A polynomial time algorithm is given for determining the WL-dimension of the subgraph counting problem for given pattern $P$, answering an open question from previous work.

Abstract

Seminal research in the field of graph neural networks (GNNs) has revealed a direct correspondence between the expressive capabilities of GNNs and the $k$-dimensional Weisfeiler-Leman ($k$WL) test, a widely-recognized method for verifying graph isomorphism. This connection has reignited interest in comprehending the specific graph properties effectively distinguishable by the $k$WL test. A central focus of research in this field revolves around determining the least dimensionality $k$, for which $k$WL can discern graphs with different number of occurrences of a pattern graph $P$. We refer to such a least $k$ as the WL-dimension of this pattern counting problem. This inquiry traditionally delves into two distinct counting problems related to patterns: subgraph counting and induced subgraph counting. Intriguingly, despite their initial appearance as separate challenges with seemingly divergent approaches, both of these problems are interconnected components of a more comprehensive problem: "graph motif parameters". In this paper, we provide a precise characterization of the WL-dimension of labeled graph motif parameters. As specific instances of this result, we obtain characterizations of the WL-dimension of the subgraph counting and induced subgraph counting problem for every labeled pattern $P$. We additionally demonstrate that in cases where the $k$WL test distinguishes between graphs with varying occurrences of a pattern $P$, the exact number of occurrences of $P$ can be computed uniformly using only local information of the last layer of a corresponding GNN. We finally delve into the challenge of recognizing the WL-dimension of various graph parameters. We give a polynomial time algorithm for determining the WL-dimension of the subgraph counting problem for given pattern $P$, answering an open question from previous work.

On the Power of the Weisfeiler-Leman Test for Graph Motif Parameters

TL;DR

A polynomial time algorithm is given for determining the WL-dimension of the subgraph counting problem for given pattern , answering an open question from previous work.

Abstract

Seminal research in the field of graph neural networks (GNNs) has revealed a direct correspondence between the expressive capabilities of GNNs and the -dimensional Weisfeiler-Leman (WL) test, a widely-recognized method for verifying graph isomorphism. This connection has reignited interest in comprehending the specific graph properties effectively distinguishable by the WL test. A central focus of research in this field revolves around determining the least dimensionality , for which WL can discern graphs with different number of occurrences of a pattern graph . We refer to such a least as the WL-dimension of this pattern counting problem. This inquiry traditionally delves into two distinct counting problems related to patterns: subgraph counting and induced subgraph counting. Intriguingly, despite their initial appearance as separate challenges with seemingly divergent approaches, both of these problems are interconnected components of a more comprehensive problem: "graph motif parameters". In this paper, we provide a precise characterization of the WL-dimension of labeled graph motif parameters. As specific instances of this result, we obtain characterizations of the WL-dimension of the subgraph counting and induced subgraph counting problem for every labeled pattern . We additionally demonstrate that in cases where the WL test distinguishes between graphs with varying occurrences of a pattern , the exact number of occurrences of can be computed uniformly using only local information of the last layer of a corresponding GNN. We finally delve into the challenge of recognizing the WL-dimension of various graph parameters. We give a polynomial time algorithm for determining the WL-dimension of the subgraph counting problem for given pattern , answering an open question from previous work.
Paper Structure (18 sections, 30 theorems, 28 equations)

This paper contains 18 sections, 30 theorems, 28 equations.

Key Result

Proposition 1

Let $\varphi$ be a quantifier-free positive formula with free constraints. Then the function $\#\varphi$ is a labeled graph motif parameterDBLP:conf/icalp/DellRW19 additionally require $\psi$ to be in CNF/DNF. For our purposes the potential blowup incurred by transformation into CNF is of no consequ

Theorems & Definitions (53)

  • Proposition 1: DBLP:conf/pods/ChenM16,DBLP:conf/icalp/DellRW19
  • Example 1
  • Theorem 2
  • Example 2
  • Lemma 3
  • proof : Proof Sketch
  • Lemma 4: Lemma 4 in DBLP:journals/corr/abs-2302-11290
  • Lemma 4
  • proof : Proof of \ref{['mainmotif']}
  • Theorem 5
  • ...and 43 more