论文标题
不等扩散的网络结构
The Network Structure of Unequal Diffusion
论文作者
论文摘要
社交网络会影响信息的传播,因此有可能减少或放大机会的不平等。我们从经验上表明,社交网络通常比我们的随机图模型所预期的沿着长度2和3的路径沿着长度2和3的路径表现出不平等扩散的潜力。我们认为,单独的同质性无法完全解释不平等扩散的程度,并将这种不匹配归因于节点之间跨组链接的不等分布。基于这种见解,我们开发了随机块模型的变体,该模型将异质性融合在跨组链接中。该模型在长度2的路径上提供了无偏和一致的估计值或同质性的估计值,并且比现有模型提供了长度3路径的更准确的估计。我们表征其对数可能比率测试的无效分布,并认为仅在网络密集时,拟合测试的好处才有效。根据我们的经验观察和建模结果,我们得出结论,从扩散过程中链接与源节点的任何差异分布的影响不限于其一阶效应,因为某些节点的直接链接与来源的直接链接更少。更重要的是,这种不平等的分布也将导致二阶效应,因为整个组的扩散路径将更少。
Social networks affect the diffusion of information, and thus have the potential to reduce or amplify inequality in access to opportunity. We show empirically that social networks often exhibit a much larger potential for unequal diffusion across groups along paths of length 2 and 3 than expected by our random graph models. We argue that homophily alone cannot not fully explain the extent of unequal diffusion and attribute this mismatch to unequal distribution of cross-group links among the nodes. Based on this insight, we develop a variant of the stochastic block model that incorporates the heterogeneity in cross-group linking. The model provides an unbiased and consistent estimate of assortativity or homophily on paths of length 2 and provide a more accurate estimate along paths of length 3 than existing models. We characterize the null distribution of its log-likelihood ratio test and argue that the goodness of fit test is valid only when the network is dense. Based on our empirical observations and modeling results, we conclude that the impact of any departure from equal distribution of links to source nodes in the diffusion process is not limited to its first order effects as some nodes will have fewer direct links to the sources. More importantly, this unequal distribution will also lead to second order effects as the whole group will have fewer diffusion paths to the sources.