Efficient Cohesive-Subgraph Search over Large Graphs

大图上的高效内聚子图搜索

• 批准号: DE150100563
• 负责人: A/Prof Lijun Chang
• 金额: $ 23.85万
• 依托单位: The University of Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Early Career Researcher Award
• 财政年份: 2015
• 资助国家: 澳大利亚
• 起止时间: 2015-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DE150100563
• 关键词: Efficient; Cohesive; Subgraph; Search; over

Cohesive-subgraph searching is highly demanded by many applications that deal with large graphs, such as community searching, social marketing, crime detection, and collaborative tagging systems. This project aims to develop effective and efficient techniques to search cohesive subgraphs of particular interest to end-users that are defined by a set of query vertices or a set of query labels (keywords). This project aims to develop, analyse, implement, and evaluate novel indexing and query processing techniques to effectively and efficiently support a set of primitive cohesive-subgraph search queries. The project aims to provide technological breakthroughs and be beneficial for both science and society in Australia.

凝聚子图搜索在许多处理大型图的应用中有很高的要求，例如社区搜索、社会营销、犯罪检测和协作标记系统。该项目旨在开发有效和高效的技术来搜索最终用户特别感兴趣的内聚子图，这些子图由一组查询顶点或一组查询标签（关键字）定义。该项目旨在开发，分析，实现和评估新的索引和查询处理技术，以有效和高效地支持一组原始的内聚子图搜索查询。该项目旨在提供技术突破，并对澳大利亚的科学和社会都有益。