Graph Neural Networks: Advances in Representation Learning for Structured Data Analysis
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Abstract
Graph Neural Networks (GNNs) have emerged as a powerful framework for representation learning in structured data analysis. recent advances in GNNs, highlighting their capabilities, applications, and challenges. GNNs extend traditional neural network architectures to handle graph-structured data, enabling the modeling of complex relationships and dependencies present in various domains such as social networks, biological networks, and recommendation systems. By recursively aggregating information from neighbouring nodes, GNNs can learn expressive node and graph embeddings that capture both local and global structural properties. the key components of GNNs, including message passing schemes, graph convolutional layers, and graph attention mechanisms. Additionally, it discusses applications of GNNs in node classification, link prediction, graph classification, and graph generation tasks. Despite their effectiveness, GNNs also face challenges such as scalability, generalization to unseen graphs, and interpretability. Addressing these challenges requires further research and development to unlock the full potential of GNNs in structured data analysis.
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