{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/how-powerful-are-graph-neural-networks","title":"How Powerful are Graph Neural Networks?","arxiv_id":"1810.00826","date":"2018-10-01","proceeding":"ICLR 2019 5","authors":["Keyulu Xu","Weihua Hu","Jure Leskovec","Stefanie Jegelka"],"abstract":"Graph Neural Networks (GNNs) are an effective framework for representation\nlearning of graphs. GNNs follow a neighborhood aggregation scheme, where the\nrepresentation vector of a node is computed by recursively aggregating and\ntransforming representation vectors of its neighboring nodes. Many GNN variants\nhave been proposed and have achieved state-of-the-art results on both node and\ngraph classification tasks. However, despite GNNs revolutionizing graph\nrepresentation learning, there is limited understanding of their\nrepresentational properties and limitations. Here, we present a theoretical\nframework for analyzing the expressive power of GNNs to capture different graph\nstructures. Our results characterize the discriminative power of popular GNN\nvariants, such as Graph Convolutional Networks and GraphSAGE, and show that\nthey cannot learn to distinguish certain simple graph structures. We then\ndevelop a simple architecture that is provably the most expressive among the\nclass of GNNs and is as powerful as the Weisfeiler-Lehman graph isomorphism\ntest. We empirically validate our theoretical findings on a number of graph\nclassification benchmarks, and demonstrate that our model achieves\nstate-of-the-art performance.","url_abs":"http://arxiv.org/abs/1810.00826v3","url_pdf":"http://arxiv.org/pdf/1810.00826v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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