Fast Geometric Embedding for Node Influence Maximization

• URL: http://arxiv.org/abs/2506.07435v1
• Date: Mon, 09 Jun 2025 05:21:56 GMT
• Title: Fast Geometric Embedding for Node Influence Maximization
• Authors: Alexander Kolpakov, Igor Rivin,
• Abstract summary: 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 centrality measures.<n>As an application, it turns out that the proposed embedding allows to find high-influence nodes in a network, and provides a fast and scalable alternative to the standard greedy algorithm.
• Score: 49.84018914962972
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 to find high-influence nodes in a network, and provides a fast and scalable alternative to the standard greedy algorithm.

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