Waniek Marcin, Holme Petter, Cebrian Manuel, Rahwan Talal
Computer Science, Division of Science, New York University Abu Dhabi, Saadiyat Island, Abu Dhabi 129188, UAE.
Department of Computer Science, Aalto University, Otakaari 1B, 02150 Espoo, Finland.
iScience. 2022 Aug 19;25(9):104956. doi: 10.1016/j.isci.2022.104956. eCollection 2022 Sep 16.
Influencing others through social networks is fundamental to all human societies. Whether this happens through the diffusion of rumors, opinions, or viruses, identifying the diffusion source (i.e., the person that initiated it) is a problem that has attracted much research interest. Nevertheless, existing literature has ignored the possibility that the source might strategically modify the network structure (by rewiring links or introducing fake nodes) to escape detection. Here, without restricting our analysis to any particular diffusion scenario, we close this gap by evaluating two mechanisms that hide the source-one stemming from the source's actions, the other from the network structure itself. This reveals that sources can easily escape detection, and that removing links is far more effective than introducing fake nodes. Thus, efforts should focus on exposing concealed ties rather than planted entities; such exposure would drastically improve our chances of detecting the diffusion source.
通过社交网络影响他人是所有人类社会的基础。无论这是通过谣言、观点还是病毒式传播发生的，识别传播源（即发起传播的人）都是一个吸引了众多研究兴趣的问题。然而，现有文献忽略了传播源可能会策略性地修改网络结构（通过重新连接链接或引入虚假节点）以逃避检测的可能性。在这里，我们不将分析局限于任何特定的传播场景，而是通过评估两种隐藏传播源的机制来弥补这一差距——一种源于传播源的行为，另一种源于网络结构本身。这表明传播源很容易逃避检测，而且删除链接比引入虚假节点更有效。因此，应将努力集中在揭露隐藏的联系而非植入的实体上；这样的揭露将大大提高我们检测传播源的机会。
