NeTS: Small: Inverse Problems from Cascades: Structure, Causation and Opinions

级联，也称为流行过程，是一种网络现象，其中一个节点的激活增加了其邻居激活的可能性；这会导致从一个节点开始的事件最终通过连续传播影响网络的更大部分。级联过程可以灵活而连贯地模拟以下几种现象：手机中的病毒和恶意软件的传播、人类社会中的疾病、在线社交网络中的观点和行动。这个项目的重点是使用级联作为一种推理和学习工具；目的是通过对级联进程的部分和非常嘈杂的观察，确定重要的网络结构和属性。这与绝大多数关于级联的工作背道而驰，后者专注于“向前”问题(预测级联如何在给定网络属性的情况下传播)。该项目将开发一个分析和算法框架，以实现以下三个目标：1.推断图结构：什么图最能解释观察到的级联？从多个级联过程的噪声样本中，该项目将把图学习描述为非参数统计推理，并提出一种算法方法，该方法利用正则化凸优化和迭代(前向-后向)方法的最新突破。相反，该项目将使用统计极小极大理论来开发样本复杂性的下限。应用程序比比皆是--例如，通过观察关注者网络上的级联，学习真实的Twitter兴趣图。2.检测和识别致因网络：是否有可能检测到级联是否在进行；如果是，它在哪个网络上演化？互动发生在许多不同领域的多个可能的网络上(移动取证、流行病学、在线社交网络)，这表明这一推力具有广泛的适用性。3.学习节点意见：用户通常需要积极参与才能进行级联(例如，在社交媒体上转发)。通过将用户决策与用户操作相关联，是否有可能了解个人用户意见？为了验证，该项目将在合成数据和真实数据上测试算法。要利用的公共数据集包括德克萨斯州的医院记录以及在线博客和搜索记录(Spinn3r、Twitter、谷歌流感趋势、Info猩猩)。级联过程在现代网络中广泛流行。该项目的算法和对逆问题的理解将进一步推动包括生物和人类疾病网络、自利代理人的社会网络以及移动和恶意软件网络在内的不同领域的最先进技术。此外，该项目将继续并扩大国际和平协会对招收和辅导来自代表性不足社区的学生的重视。得克萨斯大学奥斯汀分校无线网络和通信集团的工业附属公司计划将促进向工业转移技术。

Cascades, also known as epidemic processes, are network phenomena where the activation of one node increases the likelihood of activation of its neighbors; this results in an event starting at one node eventually affecting a much larger part of the network via successive spread. Cascade processes serve as flexible yet coherent models for several phenomena: spread of viruses and malware in mobile phones, diseases in human society, opinions and actions in online social networks. This project focuses on using cascades as an inference and learning tool; the aim is to ascertain important network structure and properties, from partial and very noisy observations of cascade progressions on it. This runs counter to the vast majority of work on cascades, which is focused on the "forward" problem (of predicting how a cascade will spread given network properties). The project will develop an analytical and algorithmic framework that achieves the following three aims: 1. Inferring Graph Structure: What graph best explains observed cascades? From noisy samples of multiple cascade progressions, the project will formulate graph-learning as non-parametric statistical inference, and propose an algorithmic approach that leverages recent break-throughs in regularized convex optimization and iterative (forward-backward) methods. Conversely, the project will develop lower-bounds on sample complexity using statistical minimax theory. Applications abound - for instance, learning the true Twitter interest graph from observation of cascades over the follower network. 2. Detecting and Identifying the Causative Network: Is it possible to detect if a cascade is progressing; if so which network is it evolving on? Interactions occur over multiple possible networks in many different domains (mobile forensics, epidemiology, online social networks), pointing to the broad applicability of this thrust. 3. Learning Node Opinions: Users often need to be active participate for cascades to progress (e.g., retweet on social media). By correlating user decisions with user actions, is it possible to learn individual user opinions? For validation the project will test the algorithms both on both synthetic data and real data. Public data sets to be leveraged include Texas hospital records along with online blog and search records (Spinn3r, Twitter, Google Flu Trends, infochimps).Cascade processes are widely prevalent in modern networks. The project's algorithms and understanding of inverse problems will further the state of the art in diverse fields including biological and human disease networks, societal networks of self-interested agents, and mobile and malware networks. In addition, this project will continue and broaden the PI's emphasis on recruiting and mentoring students from under-represented communities. The industrial affiliates program of the Wireless Networking and Communications Group at The University of Texas at Austin will facilitate technology transfer to industry.