Querying and Mining Dynamics in Evolving Graphs and Networks

演化图和网络中的查询和挖掘动态

• 批准号: RGPIN-2020-04506
• 负责人: Pei, Jian
• 金额: $ 4.66万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717043
• 关键词: Querying; Mining; Dynamics; Evolving; Graphs

In many applications, huge amounts of complex data are modeled as graphs or networks. More often than not, such graphs and networks are evolving all the time, such as interactions in social networks and traffics in geographical networks/communication networks. Analyzing and mining evolving graphs and networks enable us to understand sophisticated behaviors that cannot be captured comprehensively in the past. Dynamics are a beauty in evolving graphs and networks. Analyzing and making good use of dynamics in evolving graphs and networks provide us unprecedented power to conquer big data, and, at the same time, post grand technical challenges. This proposed research program is to embrace the opportunities and address the technical challenges in a timely and practical manner. We will design practical and principled analytics tasks and develop efficient and effective methods. Specifically, we will identify a series of novel tasks that are practically useful to capture dynamics in evolving graphs and networks and, at the same time, are computationally feasible or approximate-able. Those tasks include both data statistics queries and machine learning tasks. Second, we will develop principled algorithmic approaches and data structures that are effective and efficient for those target tasks. Third, we will build a big graph and network data system as a platform to integrate our algorithmic inventions, and conduct case studies in real application scenarios to verify and evaluate our research development and produce practical impact. This proposed research program continues my long-term endeavor to conquer massive sophisticated data. The ultimate objective is to develop business intelligence based on dynamic graph and network data, and train HQP equipped with the up-to-date knowledge and skills and capable of producing innovations in industry and academia. We divide the proposed research program into three projects. First, we will study how to model dynamics in not-evolving networks with various constraints and preferences. Second, we will investigate methods analyzing and mining dynamics in evolving networks. Last, we will build a distributed big graph data system for analyzing and mining dynamics in evolving networks. We will build a distributed graph big data system as a platform to integrate our algorithmic inventions and focus on scalability using cloud computing. The proposed research program will systematically investigate a series of novel research problems and lead to fruitful publications in premier academic venues. The research outcome will advance the frontier in this fast-growing area and produce substantial impact in academia. HQP will be trained in the program to meet the deadly demand from both academia and industry in this area. We will invite our industry partners to test drive the outcome in this proposed research. Some techniques and some components of the graph database system may likely be adopted by some partners.

在许多应用程序中，大量的复杂数据被建模为图形或网络。这些图形和网络通常一直在不断发展，例如社交网络中的交互和地理网络/通信网络中的交互。分析和采矿的图表和网络使我们能够理解过去无法全面捕获的复杂行为。 动态是不断发展的图形和网络的美。在不断发展的图形和网络中分析和充分利用动态，为我们提供了前所未有的能力来征服大数据，同时也出现了巨大的技术挑战。该建议的研究计划是及时，实用的方式拥抱机会并应对技术挑战。 我们将设计实用和有原则的分析任务，并开发有效的方法。具体而言，我们将确定一系列新任务，这些任务实际上可用于捕获不断发展的图和网络中的动态，同时在计算上是可行的或近似的。这些任务包括数据统计查询和机器学习任务。其次，我们将开发有原则的算法方法和数据结构，这些方法对这些目标任务有效且有效。第三，我们将构建一个大图和网络数据系统，作为一个平台，以整合我们的算法发明，并在实际应用方案中进行案例研究，以验证和评估我们的研究开发并产生实际影响。 该提出的研究计划继续我的长期努力来征服大量复杂数据。最终目标是根据动态图和网络数据开发商业智能，并培训配备了最新知识和技能的HQP，并能够在行业和学术界产生创新。 我们将拟议的研究计划分为三个项目。首先，我们将研究如何在不变的网络中建模具有各种约束和偏好的动态。其次，我们将调查分析不断发展的网络中的方法和采矿动态。最后，我们将构建一个分布式的大图数据系统，用于分析不断发展的网络中的动态。我们将构建一个分布式图形数据系统作为一个平台，以集成我们的算法发明并使用云计算专注于可扩展性。 拟议的研究计划将系统地研究一系列新的研究问题，并在主要学术场地中导致富有成果的出版物。研究结果将在这个快速发展的地区推进前沿，并在学术界产生重大影响。 HQP将在该计划中接受培训，以满足该领域的学术界和行业的致命需求。我们将邀请我们的行业合作伙伴在这项拟议的研究中测试结果。某些合作伙伴可能会采用某些技术和图形数据库系统的某些组件。