Directionality-Aware Cohesive Subgraph Search over Directed Graphs

有向图上的方向感知内聚子图搜索

• 批准号: DP220103731
• 负责人: A/Prof Lijun Chang
• 金额: $ 34.25万
• 依托单位: The University of Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2022
• 资助国家: 澳大利亚
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP220103731
• 关键词: Directionality; Aware; Cohesive; Subgraph; Search

Searching cohesive subgraphs around a set of user-specified seed vertices in big graphs has many applications including cybersecurity, crime detection, social marketing and public health. This project aims to investigate directionality-aware search of cohesive subgraphs over directed graphs by designing effective models and developing efficient and scalable algorithms. This project expects to address key challenges and lay scientific foundations for searching big directed graphs. The expected outcomes include novel models, computing paradigms, algorithms, indexing techniques, and distributed solutions. The success of the project will not only provide technological breakthroughs but also benefit the development of key industries in Australia

在大图中围绕一组用户指定的种子顶点搜索凝聚力子图具有许多应用程序，包括网络安全，犯罪检测，社会营销和公共卫生。该项目旨在通过设计有效的模型并开发有效且可扩展的算法来研究对导向图的方向性搜索对定向图的搜索。该项目期望应对关键挑战，并为搜索大型图形搜索科学基础。预期的结果包括新型模型，计算范例，算法，索引技术和分布式解决方案。该项目的成功不仅将提供技术突破，而且还有益于澳大利亚关键行业的发展