异构且带属性多层网络中的灵活推断
Flexible inference in heterogeneous and attributed multilayer networks
AI总结：
本文提出一种结合贝叶斯框架、Laplace matching 与自动微分的概率生成模型，以支持任意属性类型的异构多层网络推断，并验证其社区检测、预测和模式发现能力。
摘要：
网络化数据集可以通过关于单个节点或边的不同类型信息而得到丰富。然而，大多数用于分析此类数据集的现有方法都难以处理异构数据的复杂性，通常需要大量针对特定模型的分析。在本文中，我们开发了一个概率生成模型，用于在具有任意类型信息的多层网络中执行推断。我们的方法采用贝叶斯框架，并结合 Laplace matching 技术，以简化对推断参数的解释。此外，算法实现依赖于自动微分，从而避免了显式推导的需要。这使我们的模型具有可扩展性，并且能够灵活适应输入数据的任意组合。我们在异构多层数据上展示了该方法在检测重叠社区结构和执行各类预测任务方面的有效性；在这些数据中，节点和边具有不同类型的属性。此外，我们还通过以有意义的方式有效利用所有输入信息，展示了其揭示印度农村村民之间社会支持网络中多种模式的能力。
英文摘要：
Networked datasets can be enriched by different types of information about individual nodes or edges. However, most existing methods for analyzing such datasets struggle to handle the complexity of heterogeneous data, often requiring substantial model-specific analysis. In this paper, we develop a probabilistic generative model to perform inference in multilayer networks with arbitrary types of information. Our approach employs a Bayesian framework combined with the Laplace matching technique to ease interpretation of inferred parameters. Furthermore, the algorithmic implementation relies on automatic differentiation, avoiding the need for explicit derivations. This makes our model scalable and flexible to adapt to any combination of input data. We demonstrate the effectiveness of our method in detecting overlapping community structures and performing various prediction tasks on heterogeneous multilayer data, where nodes and edges have different types of attributes. Additionally, we showcase its ability to unveil a variety of patterns in a social support network among villagers in rural India by effectively utilizing all input information in a meaningful way.
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。
