Fast Geometric Embedding for Node Influence Maximization

Key facts

• The algorithm is designed for large-scale graphs where classical centrality measures are computationally expensive.
• The embedding uses radial distance from the origin as a proxy for various centrality measures.
• The method was evaluated on multiple graph families.
• Strong correlations with degree, PageRank, and paths-based centralities were demonstrated.
• The embedding allows finding high-influence nodes in a network.
• It provides a fast and scalable alternative to the standard greedy algorithm.
• The paper is categorized under Computer Science > Social and Information Networks.
• The submission history is available on arXiv.