Title: Non-Local Graph Embedding with Adaptive Weighting

Abstract

Graph embeddings are a fundamental tool for representing graph data, offering insights into network structure and relationships. Traditional methods often employ fixed weights, limiting the flexibility and scalability of the representation. This paper introduces a novel non-local graph embedding method that dynamically adjusts the weights of each node based on the global structure of the graph, promoting robust and scalable representation. We propose a neural network architecture that learns node weights based on the graph's connectivity and topology, enabling the method to capture complex relationships and adapt to varying graph characteristics. This approach overcomes the limitations of fixed weight methods, offering a significant advancement in graph embedding techniques. This work aims to provide a more adaptable and robust representation of graph data, facilitating a broader range of applications.