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Proceedings of the 14th ACM International Conference on Web Search and Data Mining by Zheng, Da (author);Wang, Minjie (author);Gan, Quan (author);Song, Xiang (author);Zhang, Zheng (author);Karypis, George (author) is a nonfiction available to read on EtoBox.

What is Proceedings of the 14th ACM International Conference on Web Search and Data Mining about?

Learning from graph and relational data plays a major role in many applications including social network analysis, marketing, ecommerce, information retrieval, knowledge modeling, medical and biological sciences, engineering, and others. Recently, Graph Neural Networks (GNNs) have emerged as a promising new learning framework capable of bringing the power of deep representation learning to graph and relational data. This ever-growing body of research has shown that GNNs achieve state-of-the-art performance for problems such as link prediction, fraud detection, target-ligand binding activity prediction, knowledge-graph completion, and product recommendations. In practice, many of the real-world graphs are very large. It is urgent to have scalable solutions to train GNN on large graphs efficiently. The objective of this tutorial is twofold. First, it will provide an overview of the theory behind GNNs, discuss the types of problems that GNNs are well suited for, and introduce some of the most widely used GNN model architectures and problems/applications that are designed to solve. Second, it will introduce the Deep Graph Library (DGL), a scalable GNN framework that simplifies the deve

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Author

Zheng, Da (author);Wang, Minjie (author);Gan, Quan (author);Song, Xiang (author);Zhang, Zheng (author);Karypis, George (author)

Publisher

ACM

Published

2021

Language

EN

Category

nonfiction