EAGER: Collaborative Research: Correspondence Discovery in Disparate Networks

在许多重要的数据挖掘应用中，输入网络可能来自不同的来源、不同的时间、不同的粒度、具有部分或完全不同的节点集，从而产生视差问题。网络通信问题旨在发现不同输入网络之间的节点或网络对齐，是各种高影响应用背后的重要垫脚石。例如，在生物信息学中，网络通信通常是发现哪些疾病与哪些蛋白质有关的第一步，以帮助设计新药或重新利用现有的药物；在脑信息学中，它可以帮助检测哪些大脑连接与某些疾病和个性特征相关；在管理中，找到不同团队网络之间的对应关系通常是确定企业中高绩效团队与功能失调团队的关键。尽管许多新兴应用经常产生多个(多于两个)、动态和异构图，但绝大多数现有的网络通信工作都集中在静态和同构(即单部)图的成对对齐上，只有极少数例外。该项目的总体目标是发现不同网络中的通信，以便能够对它们进行集体挖掘。这个项目将调查三个主要的研究任务，其中包括来自现实场景和应用的约束：(1)具有多种类型的节点和边缘的异质网络的链接，(2)动态网络的链接，以及(3)集体网络链接，而不是网络的成对比较和比对。准确高效地链接不同类型的网络将使现有的图挖掘工具能够应用于一系列不同的网络，并在各种重要的应用领域产生新的见解。该项目将推进最先进的技术，从多个维度挖掘不同的网络，包括其通用性、适用性、有效性和可扩展性。从这个项目开发的算法将适用于广泛的高影响领域，如社会科学、脑信息学和生物信息学。研究成果将通过出版物、会议教程、开放源码软件以及潜在的技术转让来传播。

In many important data mining applications, the input networks may be collected from different sources, at different times, at different granularities, with partially or completely different sets of nodes, and thus create the disparity issue. The network correspondence problem, which aims to find the node or network alignment across different input networks, is a vital stepping stone behind a variety of high-impact applications. For example, in bioinformatics, network correspondence is often the very first step toward discovering which diseases are related to which proteins in order to help design new drugs or re-purpose the existing ones; in brain-informatics, it can help detect which brain wirings are correlated with certain diseases and personality traits; in management, finding the correspondence between different team networks is often the key to characterize high-performing vs. dysfunctional teams within an enterprise. The vast majority, with only very few exceptions, of the existing work on network correspondence focuses on pairwise alignment for static and homogeneous (i.e., uni-partite) graphs, although many emerging applications often produce multiple (more than two), dynamic and heterogeneous graphs. The overall goal of this project is to discover correspondence in disparate networks in order to enable collective mining of them. This project will investigate three main research tasks, which incorporate constraints from realistic scenarios and applications: (1) Linkage of heterogeneous networks with multiple types of nodes and edges, (2) Linkage of dynamic networks, and (3) Collective network linkage, as opposed to pairwise comparison and alignment of networks. Accurate and efficient linkage of different types of networks will enable the applications of the existing graph mining tools to a collection of disparate networks, and lead to new insights in a variety of important application domains. This project will advance the state-of-the-art techniques on mining disparate networks in multiple dimensions, including its generality, applicability, effectiveness, and scalability. The algorithms developed from this project will be applicable to a wide range of high-impact domains, such as social sciences, brain-informatics, and bioinformatics. The research outcomes will be disseminated by publications, conference tutorials, open-source software, as well as potential tech transfer.