My research, rooted in network science and control systems theory, focuses on mathematical problems involving multiple dynamical systems interacting through a network. These systems, or agents, can represent various entities like robots, vehicles, or social individuals, with interactions encoded by a graph's nodes and edges. My main research questions revolve around understanding and designing collective behaviors from these interactions, which have led me to explore several application domains. For sensor networks, I studied distributed algorithms for global estimation from local measurements. For robotic teams, I developed algorithms ensuring optimal deployment and cooperation despite communication challenges. For connected and autonomous vehicles, my work involved designing control methods for stable platooning under unreliable communication. In studying animal groups, I highlighted how communication patterns shape their flocks. For epidemics on networks, I studied their evolution and strategies for vaccinations and non-pharmaceutical interventions. My research on social networks has involved original models of opinion formation and social influence, and I recently explored attention dynamics on social media platforms like YouTube. Five major themes weave through my research: distributed control, communication constraints, minimal and gossiping interactions, heterogeneity, and scalability to large networks. Distributed control requires algorithms that comply with the network's communication constraints. Communication in these systems often faces limitations like delays, noise, and quantization, impacting coordination. To address communication costs, I explored gossiping interactions where agents communicate pairwise randomly. Heterogeneity among agents, though often neglected, poses significant challenges, especially for synchronization. Lastly, I focused on the scalability of control methods to large networks, ensuring their feasibility regardless of network size. In my research, I employed diverse mathematical tools, primarily from systems and control theory, graph theory, nonsmooth and hybrid systems, and probability theory, to tackle various research challenges. Graph theory is essential for understanding network topology's role in performance, while nonsmooth systems arise from quantization and from changes in the topology of the interaction graph. Hybrid systems allow handling both continuous flows and discontinuous jumps in dynamics. Random processes describe asynchronous interactions and failures, with stochastic models often preferred for their probabilistic guarantees over more conservative deterministic analysis. Through these methodological lenses, I have striven to contribute effectively to both the theoretical and practical understanding of networked dynamical systems.