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Graph Neural Networks improve node centrality approximation speed and transferability

By PulseAugur Editorial · [2 sources] ·

Researchers have developed Graph Neural Networks (GNNs) to approximate node centrality metrics like betweenness and closeness, which are computationally expensive to calculate exactly. The study demonstrates that GNNs can learn transferable structural representations across various graph topologies, improving scalability and speed. While the models show promise in approximating betweenness centrality with significant speedups, closeness centrality remains sensitive to graph structures, presenting an ongoing challenge. AI

IMPACT This research could enable faster analysis of large, complex networks in fields like social science and biology.

RANK_REASON The cluster contains a research paper detailing a new methodology for approximating graph quantities using GNNs.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Graph Neural Networks improve node centrality approximation speed and transferability

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Samra Sana, Giorgio Mantica, Saul Imbrici ·

    Graph Neural Networks for Scalable and Transferable Node Centrality Approximation

    arXiv:2607.09372v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) provide a learning-based framework for approximating graph quantities that are expensive to compute exactly. This paper investigates GNNs for scalable approximation of betweenness and closeness centralit…

  2. arXiv cs.LG TIER_1 English(EN) · Saul Imbrici ·

    Graph Neural Networks for Scalable and Transferable Node Centrality Approximation

    Graph Neural Networks (GNNs) provide a learning-based framework for approximating graph quantities that are expensive to compute exactly. This paper investigates GNNs for scalable approximation of betweenness and closeness centrality, formulated as a node-ranking problem. Exact c…

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