{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,11]],"date-time":"2024-08-11T07:48:01Z","timestamp":1723362481197},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:p>In this paper, we show that a simple coloring scheme can improve, both theoretically and empirically, the expressive power of Message Passing Neural Networks (MPNNs). More specifically, we introduce a graph neural network called Colored Local Iterative Procedure (CLIP) that uses colors to disambiguate identical node attributes, and show that this representation is a universal approximator of continuous functions on graphs with node attributes. Our method relies on separability, a key topological characteristic that allows to extend well-chosen neural networks into universal representations. Finally, we show experimentally that CLIP is capable of capturing structural characteristics that traditional MPNNs fail to distinguish, while being state-of-the-art on benchmark graph classification datasets.<\/jats:p>","DOI":"10.24963\/ijcai.2020\/294","type":"proceedings-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T12:12:10Z","timestamp":1594210330000},"page":"2126-2132","source":"Crossref","is-referenced-by-count":11,"title":["Coloring Graph Neural Networks for Node Disambiguation"],"prefix":"10.24963","author":[{"given":"George","family":"Dasoulas","sequence":"first","affiliation":[{"name":"Huawei Noah's Ark Lab"},{"name":"\u00c9cole Polytechnique, Paris, France"}]},{"given":"Ludovic","family":"Dos Santos","sequence":"additional","affiliation":[{"name":"Huawei Noah\u2019s Ark Lab"}]},{"given":"Kevin","family":"Scaman","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab"}]},{"given":"Aladin","family":"Virmaux","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab"}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-PRICAI-2020","name":"Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}","start":{"date-parts":[[2020,7,11]]},"theme":"Artificial Intelligence","location":"Yokohama, Japan","end":{"date-parts":[[2020,7,17]]}},"container-title":["Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T02:14:19Z","timestamp":1594260859000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2020\/294"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2020\/294","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}