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内聚子图有许多应用，包括网络安全，犯罪检测，社会营销和公共卫生。本计画旨在研究有向图上具方向性的内聚子图搜寻，设计有效的模型，并发展有效且可扩充的演算法。该项目旨在解决关键挑战，并为搜索大有向图奠定科学基础。预期的成果包括新的模型，计算范式，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，还将有利于澳大利亚重点行业的发展