Community detection is one of the most relevant tasks in the analysis of graphs as it has been shown that many real-world networks show a community structure. While many community detection algorithms have been developed over the recent years, most of these are designed for standard single-layer graphs. However, this can be an oversimplification of reality. In the first part of the talk, we will deal with the community detection and graph semi-supervised learning issues extended to multiplex networks, i.e., networks with multiple layers having same node sets and no inter-layer connections. The contributions are both in the problems' formulation and in their resolution applying and adapting suited and tailored optimization methods. In the second part of the talk, we will focus on the analysis of collaborations between scholars. Collaboration is crucial for deepening existing knowledge and gaining exposure to new ideas. We will investigate how researchers influence each other with their research topics, and how the COVID-19 pandemic affected researcher collaborations.