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

Maria Inês Silva, David Aparício, Beatriz Malveiro, João Tiago Ascensão, Pedro Bizarro

Published at ECML PKDD 2021 - Graph Embedding and Mining workshop

AI Research


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.

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