<p dir="ltr">This dissertation introduces a novel graph embedding paradigm, leveraging a force-directed scheme for graph embedding. In the field of graph embedding, an "embedding" refers to the process of transforming elements of a graph such as nodes, or edges, or potentially other structural information of the graph into a low-dimensional space, typically a vector space, while preserving the graph's structural properties as much as possible. The dimensions of the space are supposed to be much smaller than the elements of the graph that are to be embedded. This transformation results in a set of vectors, with each vector representing a node (or edge) in the graph. The goal is to capture the essence of the graph's topology, node connectivity, and other relevant features in a way that facilitates easier processing by machine learning algorithms, which often perform better with input data in a continuous vector space.</p><p dir="ltr">The main premise of kinematics-based force-directed graph embedding is that the nodes are considered as massive mobile objects that can be moved around in the embedding space under force. In this PhD thesis, we devised a general theoretical framework for the proposed graph embedding paradigm and provided the mathematical proof of convergence given the required constraints. From this point on, the objective was to explore force functions and parameters and methods of applying them in terms of their efficacy regarding graph embedding applications. We found some force functions that outperformed the state-of-the-art methods.</p><p dir="ltr">The author of this manuscript believes that the proposed paradigm will open a new chapter, specifically in the field of graph embedding and generally in the field of embedding.</p>