III-COR+RI: Novel Statistical Models and Algorithms for Network Modeling, Mining and Reverse Engineering

在生物学、社会科学和各种其他领域中出现的许多问题中，经常需要分析由网络互连的实体(例如，分子或个人)的群体。这一建议旨在发展新的统计形式和计算方法，用于建模和推断网络实体的语义基础，并调查这些方面如何影响网络拓扑及其在生物学和社会学过程中的时间演化。它还将研究一些尚未探索的主题，如网络结构的区别学习，恢复时间演变的网络序列，以及相关的理论问题。这项拟议的研究旨在帮助解决宏观问题，例如：1)隐藏的身份/功能诱导，例如，当个人在不同条件下与不同的同龄人互动时，他们扮演什么角色(S)？2)结构/组织预测，例如，分子功能的变化是否以及如何导致生物途径的改变？3)系统稳健性，例如，网络如何适应外源入侵造成的扰动？这项研究横跨统计学习、社会/生物科学和数据挖掘。拟议工作的智力价值在于方法学发展的算法和理论上的新颖性，以及对特定社会和生物网络的分析以及由所提议的方法实现的各种其他应用。主要创新点包括：(1)用于节点功能和网络连接的潜在空间建模的新的贝叶斯形式，它捕捉了网络实体的功能/行为上下文；(2)用于网络演化的指数随机图模型的新的时间扩展，以及推理/学习算法；(3)从纵向节点属性数据对网络进行时间重新布线的逆向工程算法；(4)用于从网络的部分样本学习超大型网络的新的判别性学习算法和相关学习理论。这些方法将被应用于安然电子邮件网络，以探索不同业务运营条件下的行为模式，并分析从乳腺癌细胞测量的纵向分子丰度分布，以推断致癌或肿瘤抑制环境下网络的(变化)。研究结果有望推动网络分析的原理和技术的发展，并使其在更广泛的领域得到广泛的应用。作为一项跨学科的研究工作，该项目将为本科生和研究生层面的多学科教育和研究培训提供丰富的机会。对人类群体中的社会网络结构的透彻理解可以对政策制定或技术采用等重要问题产生重大影响。了解细胞网络及其在外源性干预下的变化有助于推断疾病原因和设计治疗方案。我们的方法论和软件交付成果可能会促进此类研究，提高网络数据收集的成本效益，并促进这一领域的未来发展。有关该项目的更多详细信息，请访问http://www.cs.cmu.edu/~epxing/projects/network.htm

In many problems arising in biology, social sciences and various other fields, it is often necessary to analyze populations of entities (e.g., molecules or individuals) interconnected by a network. This proposal intends to develop new statistical formalisms and computational methodologiesfor modeling and inference the semantic underpinnings ofnetwork entities, and investigate how these aspects influence the network topology and its temporal evolution during biological and sociological processes. It will also study a number of yet unexplored topics such as discriminative learning of network structures,recovering temporally evolving network sequences, and related theoretical issues. The proposed research is envisaged to help address big-picture problems such as: 1) Hidden Identity/Function Induction, e.g., what role(s)do individuals play when they interact with different peers under differentconditions? 2) Structural/Organizational Forecast, e.g., whether and how changes of molecular functions lead to alterations of biological pathways? 3) System Robustness, e.g., how a network adjusts to perturbations caused by exogenous intrusions? This research straddles statistical learning, social/biological sciencesand data mining. The intellectual merit of the proposed work lies in both the algorithmic and theoretical novelties of the methodological developments, and the analysis of specific social and biological networks and various other applications enabled by the proposed methods. The main novelties include: (1) new Bayesian formalisms for latent space modeling of node functions and network linkages, which capture the functional/behavioral context of network entities; (2) novel temporal extensions of exponential random graph model for network evolution, and inference/learning algorithms; (3) algorithms for reverse-engineering temporally rewiring networks from longitudinal node attribute data; and (4) novel discriminative learning algorithms for learning very-large networks from partial samples of the network and relevant learning theory. These methods will be applied tothe ENRON email network to explore the behavioral patterns under various business operation conditions, and to analyze a longitudinal molecular abundance profile measured from breast cancer cells to infer (alterations of) networks under carcinogenic or tumor-suppressing environments. The results are expected to advance the principles and technologies for network analysis, and enable a wide-range of applications of broader interests.The proposed research is also expected to have broad educational and societal impact. As an interdisciplinary research effort, this project will provide rich opportunities for multi-disciplinary educational and research training, at both undergraduate and graduate levels. A thorough understanding of social network structures in human populations can have significant impact on important issues such as policy making or technology adoption. Knowledge of cellular networks and its changes in response to exogenous interventions can help reasoning disease causes and designing therapeutic schemes. Our methodologicaland software deliverables can potentially facilitate such studies, improve thecost-effectiveness of network data collection, and foster future developmentin this area. More details of this project can be found at http://www.cs.cmu.edu/~epxing/projects/network.htm