用快速力导向嵌入法，用距离原点远近预测节点影响力。
Fast Geometric Embedding for Node Influence Maximization
- 通过低维空间力导向嵌入，用径向距离代表中心性。
- 在多类图上与度、PageRank等中心性高度相关。
- 可快速定位高影响力节点，替代传统贪心算法。
在大规模图上计算经典中心性度量（如介数、接近度）代价高昂。本文提出一种高效的力导向算法，将图嵌入低维空间，以从原点的径向距离作为多种中心性度量的代理。我们在多个图族上评估该方法，结果显示其与度、PageRank及路径基础中心性具有强相关性。作为应用，该嵌入方法可有效识别网络中高影响力节点，为标准贪心算法提供快速且可扩展的替代方案。
原文摘要 · Abstract (English)
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.
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