Random Walk in the Age of GNNs: Unveiling Its Continued Relevance and Applications
Keywords:
random walk, graph neural networks, feature learning, node embeddings, scalability, drug discovery, fraud detection, node2vec, message passing, social network analysis, interpretability, sampling techniques, explainable AI, real-life applicationsAbstract
This article emphasizes the ongoing importance of Random Walk in improving Graph Neural Networks (GNNs). We illustrate how Random Walk enhances GNNs by offering a deeper structural understanding, better feature learning, and increased efficiency in handling large-scale graphs. The incorporation of Random Walk strategies significantly enhances performance in practical applications like drug discovery and fraud detection. Our results indicate that Random Walk continues to be an essential tool for enhancing the interpretability, scalability, and dynamic modeling of graph-based systems, highlighting its enduring significance in contemporary AI methods.
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