The paper explores the self-similar community structure in organizations using an email network of a real organization with about 1,700 employees. The formal structure of an organization is designed for routine tasks, but informal networks emerge for unexpected situations. The study uses email data to map these informal networks, revealing self-similar properties that suggest a universal mechanism for their formation and evolution. Email data provides an objective and quantitative way to analyze informal networks, as it captures real interactions without subjective bias. The email network of the University of Rovira i Virgili (URV) is analyzed, showing a self-similar community structure. The community structure is identified using the GN algorithm, which recursively splits the network into smaller communities. The resulting binary tree shows a complex, self-similar branching pattern, resembling natural systems like river networks. The community size distribution follows a power law, indicating no characteristic community size. The Horton-Strahler index reveals topological self-similarity with a branching ratio of approximately 5.76, suggesting a universal mechanism for the formation of informal networks. The study highlights the importance of understanding informal networks for effective management. The self-similar structure and similarity to river networks suggest that optimization principles, such as minimizing energy expenditure, may underlie the formation of these networks. The findings provide insights into the mechanisms driving interactions within organizations and suggest that self-similarity is a result of the balance between cooperation and physical constraints. The methods used offer a non-intrusive way to map informal networks, opening new avenues for research into organizational structures.The paper explores the self-similar community structure in organizations using an email network of a real organization with about 1,700 employees. The formal structure of an organization is designed for routine tasks, but informal networks emerge for unexpected situations. The study uses email data to map these informal networks, revealing self-similar properties that suggest a universal mechanism for their formation and evolution. Email data provides an objective and quantitative way to analyze informal networks, as it captures real interactions without subjective bias. The email network of the University of Rovira i Virgili (URV) is analyzed, showing a self-similar community structure. The community structure is identified using the GN algorithm, which recursively splits the network into smaller communities. The resulting binary tree shows a complex, self-similar branching pattern, resembling natural systems like river networks. The community size distribution follows a power law, indicating no characteristic community size. The Horton-Strahler index reveals topological self-similarity with a branching ratio of approximately 5.76, suggesting a universal mechanism for the formation of informal networks. The study highlights the importance of understanding informal networks for effective management. The self-similar structure and similarity to river networks suggest that optimization principles, such as minimizing energy expenditure, may underlie the formation of these networks. The findings provide insights into the mechanisms driving interactions within organizations and suggest that self-similarity is a result of the balance between cooperation and physical constraints. The methods used offer a non-intrusive way to map informal networks, opening new avenues for research into organizational structures.