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New geometric embedding method speeds up node influence maximization in networks

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel force layout algorithm that embeds large-scale graphs into a low-dimensional space. This embedding allows the radial distance from the origin to act as a proxy for various centrality measures, correlating strongly with metrics like degree and PageRank. The method offers a faster and more scalable alternative for identifying influential nodes in networks compared to traditional greedy algorithms. AI

IMPACT Provides a more efficient method for analyzing network structures and identifying key nodes, potentially impacting graph-based AI applications.

RANK_REASON This is a research paper detailing a new algorithm for graph embedding and node influence maximization.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New geometric embedding method speeds up node influence maximization in networks

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This is a research paper detailing a new algorithm for graph embedding and node influence maximization.
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Alexander Kolpakov, Igor Rivin ·

    Fast Geometric Embedding for Node Influence Maximization

    arXiv:2506.07435v3 Announce Type: replace-cross 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-di…

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