Engineer friends often ask me: Graph Deep Learning sounds great, but are there any big commercial success stories? Is it being deployed in practical applications? Besides the obvious ones–recommendation systems at Pinterest, Alibaba and Twitter–a slightly nuanced success story is the Transformer architecture, which has taken the NLP industry by storm. Through this post, I want to establish links between Graph Neural Networks (GNNs) and Transformers. I’ll talk about the intuitions behind model architectures in the NLP and GNN communities, make connections using equations and figures, and discuss how we could work together to drive progress.
Microsoft Research collaborates with computer scientists at academic and scientific institutions to promote advances in computing technologies and research.
Microsoft Research collaborates with computer scientists at academic and scientific institutions to promote advances in computing technologies and research.
This page provides two large hyperlink graph for public download. The graphs have been extracted from the 2012 and 2014 versions of the Common Crawl web corpera. The 2012 graph covers 3.5 billion web pages and 128 billion hyperlinks between these pages. To the best of our knowledge, the graph is the largest hyperlink graph that is available to the public outside companies such as Google, Yahoo, and Microsoft. The2014 graph covers 1.7 billion web pages connected by 64 billion hyperlinks. Below we provide instructions on how to download the graphs as well as basic statistics about their topology.
This page provides a large hyperlink graph for public download. The graph has been extracted from the Common Crawl 2012 web corpus and covers 3.5 billion web pages and 128 billion hyperlinks between these pages. To the best of our knowledge, this graph is the largest hyperlink graph that is available to the public outside companies such as Google, Yahoo, and Microsoft. Below we provide instructions on how to download the graph as well as basic statistics about its topology.
S. Wang, L. Hu, Y. Wang, X. He, Q. Sheng, M. Orgun, L. Cao, F. Ricci, и P. Yu. (2021)cite arxiv:2105.06339Comment: Accepted by IJCAI 2021 Survey Track, copyright is owned to IJCAI. The first systematic survey on graph learning based recommender systems. arXiv admin note: text overlap with arXiv:2004.11718.
C. Xiong, R. Power, и J. Callan. Proceedings of the 26th International Conference on World Wide Web, стр. 1271--1279. Republic and Canton of Geneva, Switzerland, International World Wide Web Conferences Steering Committee, (2017)