Cohesive Multipartite Subgraph Discovery in Large Heterogeneous Networks

大型异构网络中的内聚多部分子图发现

• 批准号: DE240100200
• 负责人: Dr Lu Chen
• 金额: $ 29.7万
• 依托单位: Swinburne University of Technology
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Early Career Researcher Award
• 财政年份: 2024
• 资助国家: 澳大利亚
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DE240100200
• 关键词: Cohesive; Multipartite; Subgraph; Discovery; Large

This project aims to devise novel cohesive multipartite subgraph models and corresponding efficient search algorithms based on various applications. Significant advances in understanding big data will be enabled by the proposed novel theories and algorithms, which can leverage the value of heterogeneous network data and serve as the foundation of network analytics. Expected outcomes of this project include novel cohesive multipartite subgraph models, efficient searching algorithms and platforms for heterogeneous networks. This should provide significant benefits for different organisations and a myriad of applications dealing with heterogeneous network data, including but not limited to e-commerce, cybersecurity, health and social networks.

本计画的目的是设计新颖的内聚多部子图模型，并针对不同的应用，提出相应的有效搜寻演算法。提出的新理论和算法将使理解大数据的重大进展成为可能，这些理论和算法可以利用异构网络数据的价值，并作为网络分析的基础。该项目的预期成果包括新颖的内聚多部子图模型，高效的搜索算法和异构网络平台。这将为不同的组织和处理异构网络数据的无数应用程序提供显著的好处，包括但不限于电子商务，网络安全，健康和社交网络。