在前向线性阈值秩上玩双点游戏
Playing with Thresholds on the Forward Linear Threshold Rank
摘要：
社交网络是传播信息和影响力的自然空间，本身已成为一种媒体。已经提出了几种捕获扩散过程的模型，其中大多数基于独立级联（IC）模型或线性阈值（LT）模型。IC模型是概率性的，而LT模型依赖于要被说服的行动者的知识，反映在相关联的个体阈值中。虽然基于LT的模型为网络中的每个参与者考虑了一个单独的阈值，但到目前为止，现有的研究一直认为所有参与者的阈值都是0.5（即，简单多数激活标准）。我们在这项工作中的主要目标是开始研究如何在网络上传播信息的行为，当我们考虑其他选项设置这些阈值，有多少网络行为者最终受到这种传播的影响。为了做到这一点，我们考虑最近推出的中心性措施的基础上的LT模型，前向线性阈值秩（FLTR）。我们实验分析了几个网络的排名性能，其中的影响阻力阈值遵循不同的计划。在这里，我们考虑三种不同的方案：（1）均匀，其中所有参与者具有相同的值;（2）随机，其中每个参与者被分配一个阈值u.a.r.。在一个规定的时间间隔;（3）由另一个中心性措施对演员的价值。我们的研究结果表明，选择对排名有明显的影响，甚至在某些情况下非常显着和突然。我们的结论是，社交网络的排名，提供了最好的分配个人阈值的PageRank和FLTR。
英文摘要：
Social networks are the natural space for the spreading of information and influence and have become a media themselves. Several models capturing that diffusion process have been proposed, most of them based on the Independent Cascade (IC) model or on the Linear Threshold (LT) model. The IC model is probabilistic while the LT model relies on the knowledge of an actor to be convinced, reflected in an associated individual threshold. Although the LT-based models contemplate an individual threshold for each actor in the network, the existing studies so far have always considered a threshold of 0.5 equal in all actors (i.e., a simple majority activation criterion). Our main objective in this work is to start the study on how the dissemination of information on networks behaves when we consider other options for setting those thresholds and how many network actors end up being influenced by this dissemination. For doing so, we consider a recently introduced centrality measure based on the LT model, the Forward Linear Threshold Rank (FLTR). We analyze experimentally the ranking properties for several networks in which the influence resistance threshold follows different schemes. Here we consider three different schemes: (1) uniform, in which all players have the same value; (2) random, where each player is assigned a threshold u.a.r. in a prescribed interval; and (3) determined by the value of another centrality measure on the actor. Our results show that the selection has a clear impact on the ranking, even quite significant and abrupt in some cases. We conclude that the social networks ranks that provide the best assignments for the individual thresholds are PageRank and FLTR.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
