Title: Non-Local Graph Neural Networks for Semantic Representation

Abstract

This paper explores the application of non-local graph neural networks to enhance semantic representation learning. Traditional graph neural networks primarily focus on node features, often struggling to capture the nuanced relationships within a graph. We propose a novel architecture that explicitly models the *meaning* of the graph, achieved through a mechanism that learns representations sensitive to the graph's structure and content. This approach aims to improve generalization and robustness compared to existing methods, particularly in scenarios with complex, high-dimensional graphs. The core of this work involves a modified attention mechanism integrated within the graph neural network to facilitate the propagation of information across the graph, thereby strengthening the representation of semantic connections. We present a series of experiments evaluating the performance of this architecture on various graph datasets, demonstrating significant improvements in semantic understanding and overall accuracy.