Propinquity drives the emergence of network structure and density
Propinquity drives the emergence of network structure and density
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DOI：
10.1073/pnas.1900219116
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
L. Gallos;S. Havlin;H. Stanley;N. Fefferman
中科院分区：
文献类型：
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作者：
L. Gallos;S. Havlin;H. Stanley;N. Fefferman
虽然许多研究都集中在新节点如何在进入网络时建立连接，但我们考虑的是在最初引入之后，额外邻居的选择如何塑造新兴网络结构中的模式。我们发现这种类型的出现在现实世界中的网络的足迹，并讨论如何可以估计的过程中驱动拓扑结构的检查静态快照的网络通过链接密度的透镜。我们的方法产生洞察网络形成的应用范围从社会行为到药物发现。由于缺乏大规模的、持续发展的经验数据，网络研究通常局限于对时间快照的分析。这种方法已被用于验证网络的进化机制，如优先连接。然而，这些研究大多局限于分析网络中新节点建立的第一个链接，通常忽略了每个节点最初引入后建立的连接。在这里，我们表明，个人的后续行动，如他们的第二个网络链接，不是随机的，可以从第一个网络链接背后的机制解耦。我们表明，这个功能有很强的影响，网络拓扑结构。此外，及时的快照现在可以提供关于用于建立第二连接的机制的信息。我们解释这些经验的结果，通过引入“邻近模型”，在该模型中，我们控制和改变的距离建立的第二个链接的一个新的节点，并发现，这可以导致网络与可调密度缩放，发现在真实的网络。我们的工作表明，社会学意义的机制正在影响网络的演变，并提供了测量连续连接之间的距离的重要性的迹象。
Significance While many studies have focused on how new nodes make connections as they enter a network, we instead consider how choices of additional neighbors, after initial introduction, can shape patterns in emergent network structure. We find the footprints of this type of emergence in real-world networks and discuss how one could estimate the processes driving topology by examination of static snapshots of networks through the lens of link density. Our approach yields insight into network formation in applications ranging from social behavior to drug discovery. The lack of large-scale, continuously evolving empirical data usually limits the study of networks to the analysis of snapshots in time. This approach has been used for verification of network evolution mechanisms, such as preferential attachment. However, these studies are mostly restricted to the analysis of the first links established by a new node in the network and typically ignore connections made after each node’s initial introduction. Here, we show that the subsequent actions of individuals, such as their second network link, are not random and can be decoupled from the mechanism behind the first network link. We show that this feature has strong influence on the network topology. Moreover, snapshots in time can now provide information on the mechanism used to establish the second connection. We interpret these empirical results by introducing the “propinquity model,” in which we control and vary the distance of the second link established by a new node and find that this can lead to networks with tunable density scaling, as found in real networks. Our work shows that sociologically meaningful mechanisms are influencing network evolution and provides indications of the importance of measuring the distance between successive connections.