论文标题
共同的中心度度量，以改善社交网络影响力传播的表征
Combined Centrality Measures for an Improved Characterization of Influence Spread in Social Networks
论文作者
论文摘要
影响最大化（IM）旨在在社交网络中找到最有影响力的用户。例如，在特定传播模型中最大化意见传播的用户。先前的工作调查了影响扩散与节点中心度度量之间的相关性，以绕过更昂贵的IM模拟。结果是有希望的，但不完整，因为这些研究研究了仅在受限设置中的中心度度量的性能（即，识别有影响力的用户的能力），e。 g。，在未向/未加权的网络和/或在IM中不太常见的传播模型中。在本文中，我们首先表明，在流行的独立级联繁殖模型下，未一定会转移到未加权和未指向网络的易感性感染（SIR）传播模型中的良好结果。然后，我们确定一组中心度度量，具有良好的性能，用于IC模型中的加权和有向网络。我们的主要贡献是将中心度度量相结合的新方法，以产生更好的结果。此外，我们还通过提出的联合中心度度量扩展了重力中心（GC）。我们对50个真实数据集的实验表明，我们提出的中心度测量值优于众所周知的中心度度量和最先进的GC措施。社交网络，影响最大化，中心度度量，IC繁殖模型，有影响力的播放器
Influence Maximization (IM) aims at finding the most influential users in a social network, i. e., users who maximize the spread of an opinion within a certain propagation model. Previous work investigated the correlation between influence spread and nodal centrality measures to bypass more expensive IM simulations. The results were promising but incomplete, since these studies investigated the performance (i. e., the ability to identify influential users) of centrality measures only in restricted settings, e. g., in undirected/unweighted networks and/or within a propagation model less common for IM. In this paper, we first show that good results within the Susceptible- Infected-Removed (SIR) propagation model for unweighted and undirected networks do not necessarily transfer to directed or weighted networks under the popular Independent Cascade (IC) propagation model. Then, we identify a set of centrality measures with good performance for weighted and directed networks within the IC model. Our main contribution is a new way to combine the centrality measures in a closed formula to yield even better results. Additionally, we also extend gravitational centrality (GC) with the proposed combined centrality measures. Our experiments on 50 real-world data sets show that our proposed centrality measures outperform well-known centrality measures and the state-of-the art GC measure significantly. social networks, influence maximization, centrality measures, IC propagation model, influential spreaders
