Cohesive Multipartite Subgraph Discovery in Large Heterogeneous Networks

该项目旨在根据不同的应用设计新的内聚多部子图模型和相应的高效搜索算法。提出的新理论和算法将在理解大数据方面取得重大进展，这些理论和算法可以利用异类网络数据的价值，并作为网络分析的基础。该项目的预期成果包括新的内聚多部子图模型、高效的搜索算法和针对异质网络的平台。这应该会为不同的组织和大量处理不同网络数据的应用程序带来重大好处，包括但不限于电子商务、网络安全、医疗和社交网络。

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.