论文标题
通过可用观察结果为大量人群生成描述性社交网络：一种新颖的方法和生成器
Generate Descriptive Social Networks for Large Populations from Available Observations: A Novel Methodology and a Generator
论文作者
论文摘要
在用面向代理的方法对社会动态进行建模时，研究人员必须描述人群中相互作用的结构。考虑到广泛的网络收集的棘手性，他们依赖于应该探索合理网络空间的随机网络生成器。我们首先确定建模者的需求，包括将异质代理放置在网络上，并区分导致不同相互作用的各种社会联系。我们以散射统计和定性观察的形式指出数据的存在，应用于参数发电机。我们提出了一种基于代理的建模特有的新方法，其中我们将从个人观察到的属性中生成社交联系，并将其返回为多重网络。社会经济属性与生成规则之间的相互依存关系被编码为贝叶斯网络。一种方法论通过这些参数的形式化指导建模者。通过描述支持肯尼亚农村避孕解决方案扩散的相互作用的结构来说明这种方法。
When modeling a social dynamics with an agent-oriented approach, researchers have to describe the structure of interactions within the population. Given the intractability of extensive network collecting, they rely on random network generators that are supposed to explore the space of plausible networks. We first identify the needs of modelers, including placing heterogeneous agents on the network given their attributes and differentiating the various types of social links that lead to different interactions. We point out the existence of data in the form of scattered statistics and qualitative observations, that should be used to parameter the generator. We propose a new approach peculiar to agent-based modeling, in which we will generate social links from individuals' observed attributes, and return them as a multiplex network. Interdependencies between socioeconomic attributes, and generative rules, are encoded as Bayesian networks. A methodology guides modelers through the formalization of these parameters. This approach is illustrated by describing the structure of interactions that supports diffusion of contraceptive solutions in rural Kenya.
