Hybrid modelling of particulate systems and compaction processes

图和网络在建模和分析复杂的互联系统中起着重要的作用，例如交通网络，集成电路，电网，引用图以及生物和人工神经网络。图聚类算法可用于检测强连接顶点的组并识别网络内的社区，特别是谱聚类是一种众所周知的强大技术。这个项目的目的是开发新的扩展到传统的谱聚类算法，使社区检测在有向和时间演化图。

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.