Fast Geometric Embedding for Node Influence Maximization

Alexander Kolpakov; Igor Rivin

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arXiv · 2506.07435

Fast Geometric Embedding for Node Influence Maximization

Alexander Kolpakov·Igor Rivin

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Abstract

Computing classical centrality measures such as betweenness and closeness is computationally expensive on large-scale graphs. In this work, we introduce an efficient force layout algorithm that embeds a graph into a low-dimensional space, where the radial distance from the origin serves as a proxy for various centrality measures. We evaluate our method on multiple graph families and demonstrate strong correlations with degree, PageRank, and paths-based centralities. As an application, it turns out that the proposed embedding allows one to find high-influence nodes in a network, and provides a fast and scalable alternative to the standard greedy algorithm.

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Alexander Kolpakov, Igor Rivin. 2025-06-09. Fast Geometric Embedding for Node Influence Maximization. https://arxiv.org/abs/2506.07435

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