结构多样性与同质性：对100多个大型网络的研究
Structural Diversity and Homophily: A Study Across More than One Hundred Big Networks
摘要：
一个被广泛认可的网络组织原则是结构同质性，这表明有更多共同邻居的人更有可能相互联系。然而，嵌入在共同邻居中的不同结构对链接形成的影响还不太清楚。为了探索这个问题，我们首先描述了共同社区的结构多样性。使用120个大规模网络的集合，我们证明了共同的邻域多样性对链接存在的影响可以在网络中有很大的不同。我们发现，它对Facebook的积极影响和对LinkedIn的消极影响表明这些网络中存在不同的潜在网络需求。我们还发现了一些令人震惊的例子，其中多样性违反了同质性原则-也就是说，较少的相互联系可能导致更高的相互联系的倾向。然后，我们利用结构多样性来开发一个共同的邻域签名（CNS），我们将其应用于一个大的网络集，以发现独特的网络超家族，而不是通过传统的方法。我们的研究结果揭示了对网络结构组织和形成方式的追求，指出了设计图生成模型和推荐系统的潜在进步。
英文摘要：
A widely recognized organizing principle of networks is structural homophily, which suggests that people with more common neighbors are more likely to connect with each other. However, what influence the diverse structures embedded in common neighbors have on link formation is much less well-understood. To explore this problem, we begin by characterizing the structural diversity of common neighborhoods. Using a collection of 120 large-scale networks, we demonstrate that the impact of the common neighborhood diversity on link existence can vary substantially across networks. We find that its positive effect on Facebook and negative effect on LinkedIn suggest different underlying networking needs in these networks. We also discover striking cases where diversity violates the principle of homophily---that is, where fewer mutual connections may lead to a higher tendency to link with each other. We then leverage structural diversity to develop a common neighborhood signature (CNS), which we apply to a large set of networks to uncover unique network superfamilies not discoverable by conventional methods. Our findings shed light on the pursuit to understand the ways in which network structures are organized and formed, pointing to potential advancement in designing graph generation models and recommender systems.
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。
