Extracting the Backbone of Bipartite Projections

该项目将研究一种收集网络数据的新方法，使回答涉及社交网络的社会科学问题更快，更具成本效益。 网络在多个学科中被广泛认为对于理解信息如何传播（通信）、疾病如何传播（公共卫生）、立法如何通过（政治学）、规范和习俗如何形成（社会学）、公司和政府如何协调经济活动（经济学&地理学）、人们如何形成和维持关系至关重要。（发展心理学），以及如何破坏对国家安全的威胁（情报）。 在这些类型的网络上收集数据通常成本高昂且不切实际，这限制了回答这些问题的进展。 与其试图直接测量人（或公司，或细胞等）之间的互动，本项目开发的新方法将通过观察类似行为的模式来间接测量相互作用。例如，在一个大城市里，问每个人都和谁说话是不切实际的，但是我们可以推断出，如果两个人经常参加同样的活动，他们会互相交谈。这个项目将比较几种不同的方法来进行这些推断，以确定哪种方法（如果有的话）是最准确的。识别间接测量网络的精确方法是重要的，因为它将减少回答研究问题的时间和经济成本，例如上面提到的那些，具有科学和社会效益。给定一个二分投影网络，其中两个代理（例如，人）共享一些共同的人工产物（例如，它们参加相同的事件），这些方法确定它们是否共享足够的共同伪像以保证它们被链接的推断（例如，他们互相影响）。进行这种推断的方法的不同之处主要在于它们是否不控制任何东西（例如，无条件阈值），对于每个代理具有的伪像的数量（例如，超几何阈值），或者对于每个代理具有的人工产物的数量和每个人工产物具有的代理的数量两者（例如，固定和随机度序列模型）。在公开可用的R软件包中实现了几种现有方法并评估了它们的计算复杂性之后，它们准确恢复已知（即，将比较具有来自包含不同量噪声的合成二分数据的不同结构属性的地面实况（groundtruth）网络。此外，这些方法的性能，使用两个公开的基准经验的二分数据集，有已知的地面truths.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

This project will investigate a new way to collect network data that would make answering social scientific questions involving social networks faster and more cost-effective. Networks are widely recognized across multiple disciplines as critically important for understanding how information spreads (communications), how illnesses spread (public health), how legislation is passed (political science), how norms and customs form (sociology), how companies and governments coordinate economic activities (economics & geography), how people form and maintain relationships (developmental psychology), and how threats to national security can be disrupted (intelligence). Collecting data on these types of networks is often costly and impractical, which has limited progress in answering these questions. Rather than attempt to measure directly the interactions between people (or companies, or cells, etc.), the new methods developed in this project will measure interactions indirectly by looking at patterns of similar behavior. For example, it would be impractical to ask everyone in a large city who they talk with, but we may be able to infer that two people talk with one another if they routinely attend the same events. This project will compare several different methods for making these inferences to determine which method (if any) is the most accurate. Identifying accurate methods for indirectly measuring networks is important because it will reduce the time and economic cost of answering research questions, such as those noted above, that have scientific and societal benefits.The inferential methods investigated in this project are known as methods of backbone extraction. Given a bipartite projection network in which two agents (e.g., people) share some artifacts in common (e.g., they attend the same events), these methods determine whether they share enough artifacts in common to warrant the inference that they are linked (e.g., they interact with each other). Methods for making such inferences differ primarily by whether they control for nothing (e.g., an unconditional threshold), for the number of artifacts each agent has (e.g., a hypergeometric threshold), or for both the number of artifacts each agent has and the number of agents each artifact has (e.g., the fixed and stochastic degree sequence models). After implementation of several existing methods in a publicly available package for the R software and evaluation of their computational complexity, their ability to accurately recover known (i.e., ground truth) networks with varying structural properties from synthetic bipartite data that contain varying amounts of noise will be compared. In addition, the performance of these methods using two publicly available benchmark empirical bipartite datasets that have known ground truths will be tested.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.