Models and Inferences for Heterogeneous Interaction Patterns in Social Networks

社交网络中异构交互模式的模型和推论

• 批准号: 2210735
• 负责人: Jun Yan
• 金额: $ 36万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210735&HistoricalAwards=false
• 关键词: Models; Inferences; Heterogeneous; Interaction; Patterns

The rapid growth of social network platforms brings opportunities as well as challenges to identify, extract, and investigate the users’ sentiment on subjects of scientific and public interest. This research project aims to identify the sources of social network heterogeneity. The project will study the likelihood of replies to a posting as well as the propagation of sentiments through a social network. Results of the study are expected to help identify sentiment exchange patterns through interactions between users; detect sub-networks of posts with emotional clusters or exhibiting sentiment polarization; and understand how contagious sentiments propagate through the network of Twitter posts. The project will be integrated into multilevel education through graduate student advising, modernized graduate course components, undergraduate research on carefully devised small problems, and outreach to high school students. Broad participation will be encouraged through partnerships with organizations promoting STEM among under-represented groups. Results from the project will be shared via public repositories for data/codes, software packages, online tutorials, and social media. Dynamic networks are formed among Twitter users by evolving tweets on a certain theme (e.g., depression) through retweets, replies, and mentions, which are critical in sentiment analysis. Existing dynamic network models not accounting for heterogeneous behavioral patterns cannot provide adequate fits for large, real networks. This project aims to tackle two sources of heterogeneity in dynamic network modeling: reciprocity in emotion sharing and similarity clustering in terms of behavioral features. The investigators plan to develop two novel models that modify the preferential attachment (PA) model. The first one retains the scale-free property and allows personalized heterogeneous tendencies to generate reciprocal edges. The second model is a practical spatial superstar PA model that allows a higher level of interaction among nodes of closer social ties measured in a feature space. Their theoretical properties will be studied via rigorous asymptotic analyses. Inferences about the model parameters when fitting observed networks will be developed through likelihood-based, Bayesian, moment-based, and extreme value approaches. Implementations of the models and inferences will be made publicly available via user-friendly, open-source software packages. All the methods under development will be validated through simulation studies, and the validated methods will then be applied to dynamic networks from Tweets on mental-health-related tags.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.

社交网络平台的快速发展为识别、提取和调查用户对科学和公共利益主题的情绪带来了机遇和挑战。本研究项目旨在找出社会网络异质性的来源。该项目将研究帖子回复的可能性，以及社交网络上情绪的传播。研究结果有望通过用户之间的互动来帮助识别情感交换模式；检测具有情感集群或表现出情感极化的帖子子网络；并了解情绪是如何通过推特帖子网络传播的。该项目将通过研究生指导、现代化研究生课程组成、精心设计的小问题的本科生研究以及向高中学生的推广，融入多层次教育。通过与在代表性不足的群体中推广STEM的组织建立伙伴关系，鼓励广泛参与。该项目的结果将通过数据/代码、软件包、在线教程和社交媒体的公共存储库共享。动态网络是在Twitter用户之间形成的，通过转发、回复和提及，围绕某个主题（例如抑郁症）不断演变的推文，这在情绪分析中至关重要。没有考虑异构行为模式的现有动态网络模型不能为大型真实网络提供充分的拟合。本项目旨在解决动态网络建模中的两个异质性来源：情感共享中的互惠性和行为特征方面的相似性聚类。研究人员计划开发两个新的模型来修改优先依恋（PA）模型。第一种方法保留了无标度特性，并允许个性化的异构趋势产生互反边。第二个模型是一个实用的空间超级明星PA模型，它允许在特征空间中测量的具有更紧密社会关系的节点之间进行更高水平的交互。它们的理论性质将通过严格的渐近分析来研究。当拟合观察到的网络时，关于模型参数的推断将通过基于似然、贝叶斯、基于矩和极值方法来发展。模型和推理的实现将通过用户友好的开源软件包公开提供。所有正在开发的方法都将通过模拟研究进行验证，然后将验证的方法应用于与心理健康相关标签的推文动态网络。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Generating General Preferential Attachment Networks with R Package wdnet

• DOI: 10.6339/23-jds1110
• 发表时间: 2023-01
• 期刊: Journal of Data Science
• 作者: Yelie Yuan;Tiandong Wang;Jun Yan;Panpan Zhang