Fast Geometric Embedding for Node Influence Maximization

2025-06-09

AI Summary

To address the high computational cost of traditional centrality measures (e.g., betweenness, closeness) on large-scale graphs, this paper proposes Force-Oriented Geometric Embedding (FOGE): a method that embeds graphs into a low-dimensional Euclidean space, where the radial distance of each node from the origin serves as a unified geometric proxy for degree centrality, PageRank, and path-based centralities. FOGE is the first approach to establish a principled geometric correspondence between radial distance and multiple centrality classes, enabling sublinear-time influence-node discovery. On diverse real-world graphs, FOGE achieves Spearman correlation ρ > 0.85 with ground-truth centralities. Compared to standard greedy algorithms, it accelerates computation by three to four orders of magnitude and supports real-time ranking on graphs with up to ten million nodes.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

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.

Problem

• Efficiently compute centrality measures on large-scale graphs
• Embed graphs into low-dimensional space for centrality approximation
• Find high-influence nodes faster than standard greedy algorithms

Innovation

• Force layout algorithm for graph embedding
• Radial distance as centrality proxy
• Fast scalable alternative to greedy algorithm