EAGER: Collaborative Research: Correspondence Discovery in Disparate Networks

EAGER：协作研究：不同网络中的对应发现

• 批准号: 1743088
• 负责人: Danai Koutra
• 金额: $ 5万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1743088&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Correspondence; Discovery

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.

在许多重要的数据挖掘应用中，输入网络可能是从不同的源、在不同的时间、以不同的粒度、具有部分或完全不同的节点集合收集的，并且因此产生差异问题。网络对应问题旨在找到不同输入网络之间的节点或网络对齐，是各种高影响力应用程序背后的重要垫脚石。例如，在生物信息学中，网络对应通常是发现哪些疾病与哪些蛋白质相关的第一步，以帮助设计新药或重新使用现有药物;在脑信息学中，它可以帮助检测哪些大脑线路与某些疾病和人格特征相关;在管理中，找到不同团队网络之间的对应关系往往是确定企业内高绩效团队与功能失调团队的关键。除了极少数例外，绝大多数关于网络对应的现有工作都集中在静态和同质（即，单部）图，尽管许多新兴的应用经常产生多个（多于两个）、动态和异构的图。该项目的总体目标是发现不同网络中的对应关系，以便能够对它们进行集体挖掘。 本项目将研究三个主要研究任务，其中包括现实场景和应用的限制：（1）具有多种类型节点和边缘的异构网络的链接，（2）动态网络的链接，以及（3）集体网络链接，而不是成对比较和网络对齐。不同类型网络的准确和有效的链接将使现有的图挖掘工具的应用程序，以不同的网络的集合，并导致在各种重要的应用领域的新见解。该项目将在多个维度上推进挖掘不同网络的最新技术，包括其通用性，适用性，有效性和可扩展性。该项目开发的算法将适用于广泛的高影响力领域，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、开源软件以及潜在的技术转让进行传播。

项目成果

GeoAlign: Interpolating Aggregates over Unaligned Partitions

GeoAlign：在未对齐的分区上插入聚合

• DOI: 10.5441/002/edbt.2018.32
• 发表时间: 2018
• 期刊: 21st International Conference on Extending Database Technology (EDBT
• 作者: Song, Jie;Koutra, Danai;Mani, Murali;Jagadish V., H.
• 通讯作者: Jagadish V., H.

HashAlign: Hash-Based Alignment of Multiple Graphs

HashAlign：基于哈希的多个图的对齐

• DOI: 10.1007/9783319.930404
• 发表时间: 2018
• 期刊: Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD
• 作者: Heimann, Mark;Lee, Wei;Pan, Shengjie;Chen, Kuan-Yu;Koutra, Danai
• 通讯作者: Koutra, Danai

Exploratory Analysis of Graph Data by Leveraging Domain Knowledge

• DOI: 10.1109/icdm.2017.28
• 发表时间: 2017-11
• 期刊: 2017 IEEE International Conference on Data Mining (ICDM)
• 作者: Di Jin;Danai Koutra

Career Transitions and Trajectories: A Case Study in Computing

• DOI: 10.1145/3219819.3219863
• 发表时间: 2018-05
• 期刊: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
• 作者: Tara Safavi;Maryam Davoodi;Danai Koutra