Constrained multilinear detection for faster functional motif discovery

TL;DR

This paper introduces a constrained multilinear detection method that improves the efficiency of algorithms for discovering functional motifs in graphs, enhancing the speed of solving the GRAPH MOTIF problem.

Contribution

It defines a new constrained version of multilinear detection (k-CMLD) and provides a faster algorithm for it, leading to improved algorithms for the GRAPH MOTIF problem.

Findings

Faster algorithms for GRAPH MOTIF and its variants.

Introduction of the k-CMLD problem and its efficient solution.

Enhanced parameterized complexity results for motif detection.

Abstract

The GRAPH MOTIF problem asks whether a given multiset of colors appears on a connected subgraph of a vertex-colored graph. The fastest known parameterized algorithm for this problem is based on a reduction to the $k$-Multilinear Detection (k-MlD) problem: the detection of multilinear terms of total degree k in polynomials presented as circuits. We revisit k-MLD and define k-CMLD, a constrained version of it which reflects GRAPH MOTIF more faithfully. We then give a fast algorithm for k-CMLD. As a result we obtain faster parameterized algorithms for GRAPH MOTIF and variants of it.