Data science for graphs

It is with great pleasure that we introduce this special issue of the Journal of Applied and Computational Topology devoted to "Data Science on Graphs."This issue is a confluence of cutting-edge research at the intersection of topology, graph theory, and data science, reflecting the vibrant synergy of these fields.The rapid expansion of interest in developing models and tools that capture the intricate interactions within complex systems is a reflection of the data-driven era we inhabit.Biological, technological, and social networks are all bound by a common thread: they are inherently high-dimensional and irregular, challenging the very paradigms of classical data science methodologies.Graphs, in their essence, are simple constructs: nodes connected by edges.Yet, within this simplicity lies the power to model relationships, flows, and structures that are foundational to numerous domains.From the intricate pathways of biological systems to the vast networks of social media interactions, graphs offer a universal language for describing and analyzing connections.The fields of graph signal processing and geometric deep learning leverage graphs to unlock the power of high-dimensional and irregular data.As such, these approaches have revolutionized our ability to process and learn from complex data.Simultaneously, the field of topological data analysis has been forging its own path, developing a suite of tools that model data sets as geometric objects-graphs, simplicial complexes, and beyond.This discipline is not just about managing complexity; it's about understanding it, about finding the shape of data and the stories it tells.This special issue is a call to those immersed in topological data analysis and in machine learning and signal processing on graphs.It is an invitation to explore the largely untapped intersection between these fields and to illuminate the shared pathways towards a deeper understanding of our interconnected world.This issue also underscores the importance of interdisciplinary collaboration.