Decoupling Homophily and Reciprocity with Latent Space Network Models
Decoupling Homophily and Reciprocity with Latent Space Network Models
复制标题
用潜在空间网络模型解耦同质性和互惠性
DOI：
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发表时间：
2017
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通讯作者：
Jennifer Neville
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作者：
Jiasen Yang;Vinayak A. Rao;Jennifer Neville
网络形成了各种物理和社会领域中产生的数据的有用表示。在这项工作中，我们考虑动态网络，如通信网络，其中连接节点对的链路在连续时间内出现。我们采用基于点过程的方法，研究了将节点嵌入到欧氏空间的潜在空间模型。我们提出了两种模型来捕捉动态网络数据的两个不同方面：(I)具有相似特征的个体之间的通信以更高的速率发生(同形)，以及(Ii)个体倾向于交互来自其他节点的通信，但方式因个体而异。我们的框架融合了点过程模型的思想，包括泊松和霍克斯过程，以及静态网络的潜在空间模型的思想。我们在真实数据集上对我们的模型进行了一系列任务的评估，结果表明，双重潜在空间模型可以同时提高静态和动态链接预测的性能，该模型解释了互易性和同质性的异质性，sig-nifi。
Networks form useful representations of data arising in various physical and social domains. In this work, we consider dynamic networks such as communication networks in which links connecting pairs of nodes appear over continuous time. We adopt a point process-based approach, and study latent space models which embed the nodes into Euclidean space. We propose models to capture two different aspects of dynamic network data: ( i ) communication occurs at a higher rate between individuals with similar features (homophily), and ( ii ) individuals tend to reciprocate communications from other nodes, but in a manner that varies across individuals. Our framework mar-ries ideas from point process models, including Poisson and Hawkes processes, with ideas from latent space models of static networks. We evaluate our models over a range of tasks on real-world datasets and show that a dual latent space model, which accounts for heterogeneity in both reciprocity and homophily, sig-nificantly improves performance for both static and dynamic link prediction.