Directionality-Aware Cohesive Subgraph Search over Directed Graphs

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。本项目旨在通过设计有效的模型和开发高效且可扩展的算法来研究有向图上内聚子图的方向性感知搜索。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。预期的结果包括新的模型、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带来技术突破，而且将有利于澳大利亚重点产业的发展

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