His research focuses on NRL as well as large-scale computational social science applications. Hamilton is a PhD Candidate in Computer Science at Stanford University.
#HYPERSPIN TUTORIAL 2018 CODE#
In recent years, the SNAP group has performed extensive research in the area of network representation learning (NRL) by publishing new methods, releasing open source code and datasets, and writing a review paper on the topic. The group is one of the leading centers of research on new network analytics methods. Applications of network representation learning for recommender systems and computational biology.Īll the organizers are members of the SNAP group under Prof.Techniques for deep learning on network/graph structed data (e.g., graph convolutional networks and GraphSAGE).Part 2: Graph neural networks (pdf) (ppt).Learning low-dimensional embeddings of nodes in complex networks (e.g., DeepWalk and node2vec).What is network representation learning and why is it important?.The tutorial will be held at The Web Conference, 2018 (WWW) in Lyon, France, April 24th, 2018. We will cover methods to embed individual nodes as well as approaches to embed entire (sub)graphs, and in doing so, we will present a unified framework for NRL. We will discuss classic matrix factorization-based methods, random-walk based algorithms (e.g., DeepWalk and node2vec), as well as very recent advancements in graph neural networks. In this tutorial, we will cover key advancements in NRL over the last decade, with an emphasis on fundamental advancements made in the last two years. These network representation learning (NRL) approaches remove the need for painstaking feature engineering and have led to state-of-the-art results in network-based tasks, such as node classification, node clustering, and link prediction. However, recent years have seen a surge in approaches that automatically learn to encode network structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. Researchers in network science have traditionally relied on user-defined heuristics to extract features from complex networks (e.g., degree statistics or kernel functions).
0 kommentar(er)
