Law of Connectivity in Machine Learning

TL;DR

This paper introduces the Law of Connectivity in machine learning, asserting that all entities are interconnected, influencing each other directly or indirectly, which impacts how models and scenarios are understood and designed.

Contribution

The paper proposes a new fundamental principle called the Law of Connectivity, emphasizing the universal interconnectedness of entities in machine learning contexts.

Findings

Entities are always connected, directly or indirectly.

Self-connections are present at each node.

Connectivity influences the impact of entities on each other.

Abstract

We present in this paper our law that there is always a connection present between two entities, with a selfconnection being present at least in each node. An entity is an object, physical or imaginary, that is connected by a path (or connection) and which is important for achieving the desired result of the scenario. In machine learning, we state that for any scenario, a subject entity is always, directly or indirectly, connected and affected by single or multiple independent / dependent entities, and their impact on the subject entity is dependent on various factors falling into the categories such as the existenc