Higraphs are a visual formalism for representing complex systems, particularly those involving sets and their relationships. They extend traditional graphs and Euler/Venn diagrams by allowing the representation of Cartesian products and the inclusion of edges or hyperedges to connect different sets. Higraphs are particularly useful for modeling concurrent systems, such as statecharts, which are a higraph-based extension of finite-state machines. Higraphs are based on topological principles, not geometric ones, and use blobs (areas enclosed by curves) to represent sets. The nesting of these blobs indicates set inclusion, not membership. Each blob can be uniquely identified, and the Cartesian product of sets can be represented by partitioning the inner area of a Jordan curve. This allows for the representation of complex relationships between sets, including intersections, unions, and differences. Edges in higraphs can be directed or undirected and can connect blobs to represent relationships between sets. This makes higraphs suitable for a wide range of applications, including database modeling, knowledge representation, and the specification of complex concurrent systems. Higraphs can also be used to represent statecharts, which are a powerful tool for modeling the behavior of systems with multiple states and transitions. Higraphs have been applied in various domains, including database modeling, semantic networks, and data-flow diagrams. They are particularly useful in representing complex relationships between entities and in modeling concurrent systems. The ability to represent Cartesian products and the use of topological principles make higraphs a powerful tool for visualizing and analyzing complex systems. The formal syntax and semantics of higraphs are defined in the appendix, providing a rigorous foundation for their use in various applications.Higraphs are a visual formalism for representing complex systems, particularly those involving sets and their relationships. They extend traditional graphs and Euler/Venn diagrams by allowing the representation of Cartesian products and the inclusion of edges or hyperedges to connect different sets. Higraphs are particularly useful for modeling concurrent systems, such as statecharts, which are a higraph-based extension of finite-state machines. Higraphs are based on topological principles, not geometric ones, and use blobs (areas enclosed by curves) to represent sets. The nesting of these blobs indicates set inclusion, not membership. Each blob can be uniquely identified, and the Cartesian product of sets can be represented by partitioning the inner area of a Jordan curve. This allows for the representation of complex relationships between sets, including intersections, unions, and differences. Edges in higraphs can be directed or undirected and can connect blobs to represent relationships between sets. This makes higraphs suitable for a wide range of applications, including database modeling, knowledge representation, and the specification of complex concurrent systems. Higraphs can also be used to represent statecharts, which are a powerful tool for modeling the behavior of systems with multiple states and transitions. Higraphs have been applied in various domains, including database modeling, semantic networks, and data-flow diagrams. They are particularly useful in representing complex relationships between entities and in modeling concurrent systems. The ability to represent Cartesian products and the use of topological principles make higraphs a powerful tool for visualizing and analyzing complex systems. The formal syntax and semantics of higraphs are defined in the appendix, providing a rigorous foundation for their use in various applications.