Stigmergic Swarming Agents for Fast Subgraph Isomorphism

TL;DR

This paper introduces ASSIST, a stigmergy-inspired heuristic for subgraph isomorphism that significantly improves efficiency and supports complex matching scenarios, outperforming traditional methods.

Contribution

The paper presents ASSIST, a novel stigmergic swarming algorithm for subgraph isomorphism with linear query complexity and extended matching capabilities.

Findings

Peering step runs in O(q·log(d)) time.

Iterative search complexity is linear in query size.

Supports inexact and temporally ordered matching.

Abstract

Maximum partial subgraph isomorphism compares two graphs (nodes joined by edges) to find a largest common subgraph. A common use case, for graphs with labeled nodes, seeks to find instances of a \textit{query} graph with $q$ nodes in a (typically larger) \textit{data} graph with $d$ nodes. The problem is NP-complete, and na\"ive solutions are exponential in $q + d$. The fastest current heuristic has complexity $O(d^2)$. This paper outlines ASSIST (Approximate Swarming Subgraph Isomorphism through Stigmergy), inspired by the ant colony optimization approach to the traveling salesperson. After peering (identifying matching individual nodes in query and data) in time $O(q\cdot log(d))$, the time required for ASSIST's iterative subgraph search, the combinatorially complex part of the problem, is linear in query size and constant in data size. ASSIST can be extended to support matching problems (such as temporally ordered edges, inexact matches, and missing nodes or edges in the data graph) that frustrate other heuristics.