Hybrid modelling of particulate systems and compaction processes

图和网络在交通网络、集成电路、电网、引文图、生物和人工神经网络等复杂互联系统的建模和分析中起着重要的作用。图聚类算法可以用来检测强连通顶点组和识别网络中的社区，尤其是谱聚类是一种众所周知和强大的技术。该项目旨在开发传统谱聚类算法的新扩展，以支持在有向图和时间演化图中进行社区检测。

Graphs and networks play an important role in modeling and analyzing complex interconnected systems such as transportation networks, integrated circuits, power grids, citation graphs, and biological and artificial neural networks. Graph clustering algorithms can be used to detect groups of strongly connected vertices and identify communities within networks, with spectral clustering in particular being a well-known and powerful technique. This project aims to develop novel extensions to traditional spectral clustering algorithms to enable community detection in both directed and time-evolving graphs.