论文标题
达成共识：通过扰动社交网络来减少两极分化
Towards Consensus: Reducing Polarization by Perturbing Social Networks
论文作者
论文摘要
本文研究了集中式规划师如何修改社会或信息网络的结构以减少两极分化。首先，发现极化高度取决于网络的程度和结构特性，包括众所周知的等级数（即，Cheeger常数）。然后，我们在完整的信息下制定了计划者的问题，并激励寻求分歧并协调下降启发式方法。介绍了人们对策划者的新颖设置，其中介绍了人口天生的天生意见，并被引入，并被证明等同于最大化拉普拉斯的光谱差距。我们证明了策略的有效性，该策略在光谱差距的特征向量引起的切割相对侧的顶点之间增加了边缘。最后，在六个现实世界和合成网络上评估了这些策略。在几个网络中，我们发现通过添加少量边缘可以显着降低极化。
This paper studies how a centralized planner can modify the structure of a social or information network to reduce polarization. First, polarization is found to be highly dependent on degree and structural properties of the network -- including the well-known isoperimetric number (i.e., Cheeger constant). We then formulate the planner's problem under full information, and motivate disagreement-seeking and coordinate descent heuristics. A novel setting for the planner in which the population's innate opinions are adversarially chosen is introduced, and shown to be equivalent to maximization of the Laplacian's spectral gap. We prove bounds for the effectiveness of a strategy that adds edges between vertices on opposite sides of the cut induced by the spectral gap's eigenvector. Finally, these strategies are evaluated on six real-world and synthetic networks. In several networks, we find that polarization can be significantly reduced through the addition of a small number of edges.
