A Novel Deep Learning-Based Approach for Graph Dimensionality Reduction by Using Fuzzy Logic and Random Walks

Graph structures play a vital role in modeling relationships across various domains, including social networks, knowledge bases, and biological networks. As the dimensions of these networks grow, the efficiency of proximity-based analysis methods declines, necessitating the use of graph embedding techniques to reduce dimensionality while preserving the underlying structure. This process enhances performance in applications such as node classification and link prediction. However, traditional graph embedding methods face challenges with capturing non-linear relationships and scaling to large networks. Additionally, in real-world networks, the essential initial and precise node features which are required by these algorithms are not always available. In this paper, we propose a novel framework called FuzzyRandomNet, which addresses these challenges by integrating fuzzy logic with random walks. FuzzyRandomNet introduces non-linear layers and optimizes node features to provide more efficient and scalable solutions for graph representation learning. The evaluation of the proposed method against existing techniques on standard datasets demonstrates superior performance in node classification and link prediction, exhibiting higher accuracy and flexibility in large and complex networks.