## 1 Introduction: The Use of Simple Graphs in the Description of the Fundamental Entinties  
Apriorics is an attempt to describe the fundamental building block of physical reality in terms of graph theory and information. The philosophical basis of apriorics was dealt with in [1], in which apriorics was connected to structuralism, a philosophical approach denying inherent meaning to any notion, and interprets each and everything solely in terms of its connections to other things. Here, we are more concerned with the mathematical aspects of the theory and less with the philosophical aspects. Hence, we focus our attention on proofs and rigorous mathematical derivations. To be sure, also the General Theory of Information, as a multidisciplinary framework, seeks to understand and quantify information in various contexts. Proposed by physicist [PERSON], it builds on the foundational work of [PERSON]'s information theory. This theory goes beyond traditional concepts of information, extending to complex systems, thermodynamics, biology, and even the quantum realm. It explores the role of information in shaping the behavior and organization of systems, emphasizing the link between entropy, complexity, and information. By doing so, the General Theory of Information provides a comprehensive perspective on how information is a fundamental element in the fabric of our universe, impacting diverse scientific domains [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]. Notice, however, that the General Theory of Information cannot predict the correct number of fundamental fermions and fundamental gauge bosons and the three fundamental interactions in the way that apriorics does, as is described below. Category theory is a branch of abstract mathematics that focuses on studying relationships and structures in a generalized way. It deals with objects and morphisms (arrows) between them, emphasizing their properties and how they interact, rather than specific elements. Categories provide a framework for understanding diverse mathematical and scientific concepts, including algebra, topology, and logic. By abstracting the commonalitiesbetween different mathematical structures, category theory promotes a deeper understanding of mathematical concepts and their universal principles. Its broad applicability extends beyond mathematics, finding use in computer science, physics, and other fields for modeling and reasoning about complex systems [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31]. However, category theory does not predict the correct number of fundamental fermions and fundamental gauge bosons and the three fundamental interactions in the way that apriorics does. Of course, graph theory has a much wider application domain with respect to apriorics. Graph theory, being a branch of mathematics, has numerous real-life applications across various fields. Here are some examples:  
1. Social networks: Graph theory is extensively used in social network analysis to model relationships between individuals or entities. It helps analyze connectivity patterns, identify influential nodes (people), and understand information flow within networks like Facebook, Twitter, or LinkedIn. 2. Transportation networks: Graphs model transportation systems such as road networks, railway lines, and airline routes. Graph algorithms optimize routes, schedule public transportation, and analyze traffic flow, contributing to urban planning and logistics management. 3. Telecommunications: Graph theory aids in designing communication networks, including the internet, mobile networks, and satellite communication systems. It optimizes data routing, analyzes network robustness, and ensures efficient transmission of information. 4. Epidemiology: Graphs model disease spread within populations, with nodes representing individuals and edges representing contacts or interactions. Graph analysis helps track and predict disease outbreaks, assess vaccination strategies, and identify key factors influencing transmission. 5.