GUDIE: a flexible, user-defined method to extract subgraphs of interest from large graphs

TL;DR

GUDIE is a flexible, user-defined message-passing algorithm designed to extract meaningful subgraphs from large, labeled networks based on user criteria, enhancing analysis in social, financial, and epidemiological networks.

Contribution

This work introduces GUDIE, a novel method for extracting contextually relevant subgraphs from large, labeled graphs using user-defined criteria, improving analysis efficiency.

Findings

GUDIE effectively expands to insightful network regions.

It avoids unimportant connections, focusing on relevant context.

Preliminary results show improved analysis capabilities.

Abstract

Large, dense, small-world networks often emerge from social phenomena, including financial networks, social media, or epidemiology. As networks grow in importance, it is often necessary to partition them into meaningful units of analysis. In this work, we propose GUDIE, a message-passing algorithm that extracts relevant context around seed nodes based on user-defined criteria. We design GUDIE for rich, labeled graphs, and expansions consider node and edge attributes. Preliminary results indicate that GUDIE expands to insightful areas while avoiding unimportant connections. The resulting subgraphs contain the relevant context for a seed node and can accelerate and extend analysis capabilities in finance and other critical networks.