The paper "How Powerful are Graph Neural Networks?" by Keyulu Xu et al. explores the theoretical framework for analyzing the expressive power of Graph Neural Networks (GNNs). GNNs are effective for representing graph structures, but their representational properties and limitations are not well understood. The authors present a theoretical framework to analyze the discriminative power of popular GNN variants, such as Graph Convolutional Networks (GCN) and GraphSAGE, showing that they cannot distinguish certain simple graph structures. They develop a simple architecture called Graph Isomorphism Network (GIN), which is provably the most expressive among GNNs and is as powerful as the Weisfeiler-Lehman (WL) graph isomorphism test. Empirical validation on graph classification benchmarks confirms that GIN achieves state-of-the-art performance, demonstrating its high representational power. The paper also discusses the limitations of less powerful GNN variants, such as GCN and GraphSAGE, and provides insights into their performance on various graph structures.The paper "How Powerful are Graph Neural Networks?" by Keyulu Xu et al. explores the theoretical framework for analyzing the expressive power of Graph Neural Networks (GNNs). GNNs are effective for representing graph structures, but their representational properties and limitations are not well understood. The authors present a theoretical framework to analyze the discriminative power of popular GNN variants, such as Graph Convolutional Networks (GCN) and GraphSAGE, showing that they cannot distinguish certain simple graph structures. They develop a simple architecture called Graph Isomorphism Network (GIN), which is provably the most expressive among GNNs and is as powerful as the Weisfeiler-Lehman (WL) graph isomorphism test. Empirical validation on graph classification benchmarks confirms that GIN achieves state-of-the-art performance, demonstrating its high representational power. The paper also discusses the limitations of less powerful GNN variants, such as GCN and GraphSAGE, and provides insights into their performance on various graph structures.