论文标题
通过利用社区基于社区的驱动节点作为种子，最大化影响力传播在复杂的网络中
Maximising Influence Spread in Complex Networks by Utilising Community-based Driver Nodes as Seeds
论文作者
论文摘要
找到一小部分有影响力的节点以最大程度地提高影响在复杂网络中的影响是一个积极的研究领域。过去，已经提出了不同的方法来确定一组可以帮助在网络中实现影响力更快的种子节点。本文结合了网络控制领域的驱动程序节点选择方法，以及使用社区结构指导社区驱动程序节点的候选种子节点的选择。 在社区中用作种子节点的驱动节点是一个相对较新的想法。我们确定合成（即随机，小世界和无标度）网络以及22个现实世界社交网络的社区。然后，这些社区的驾驶员节点根据一系列共同的中心度措施进行排名。我们将这些种子集的影响力传播能力与在全球层面选择驱动器节点的结果进行比较。我们表明，在合成网络和真实网络中，利用社区结构可以增强所得种子集的功能。
Finding a small subset of influential nodes to maximise influence spread in a complex network is an active area of research. Different methods have been proposed in the past to identify a set of seed nodes that can help achieve a faster spread of influence in the network. This paper combines driver node selection methods from the field of network control, with the divide-and-conquer approach of using community structure to guide the selection of candidate seed nodes from the driver nodes of the communities. The use of driver nodes in communities as seed nodes is a comparatively new idea. We identify communities of synthetic (i.e., Random, Small-World and Scale-Free) networks as well as twenty-two real-world social networks. Driver nodes from those communities are then ranked according to a range of common centrality measures. We compare the influence spreading power of these seed sets to the results of selecting driver nodes at a global level. We show that in both synthetic and real networks, exploiting community structure enhances the power of the resulting seed sets.
