2025/06/09 by Alexander Kolpakov, Kolpakov, Alexander, Igor Rivin +1
Social Sciences · Computer Science · #Advanced Computing and Algorithms #Video Analysis and Summarization #Advanced Image and Video Retrieval Techniques
paper · pdf · doi:10.48550/arxiv.2506.07435
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.