Virtually every human-technology interaction, or sensor network, generates observations that are in some relation with each other. As a result, many data science problems can be viewed as a study of some properties of complex networks in which nodes represent the entities that are being studied and edges represent relations between these entities. Such networks are often large-scale, decentralized, and evolve dynamically over time. Modeling and mining complex networks in order to understand the principles governing the organization and the behaviour of such networks is crucial for a broad range of fields of study, including information and social sciences, economics, biology, and neuroscience.

The aim of the 21st Workshop on Modelling and Mining Networks (WAW 2026) is to further the understanding of networks that arise in theoretical as well as applied domains. The goal is also to stimulate the development of high-performance and scalable algorithms that exploit these networks. The workshop welcomes the researchers who are working on graph-theoretic and algorithmic aspects of networks represented as graphs or hypergraphs and other higher order structures.