srch

Michael Bronstein is a professor at Imperial College London and the Head of Graph Learning Research at Twitter. Recently, he has started posting ...

Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks Christoper Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe. Proceedings of AAAI. 2019. pdf (arxiv)

07.02.2020 · Graph Neural Networks (GNNs), which generalize the deep neural network models to graph structured data, pave a new way to effectively learn representations for graph-structured data either from the node level or the graph level. Thanks to their strong representation learning capability, GNNs have gained practical significance in various ...

I am an assistant professor at UC San Diego department of Computer Science ... Understanding the Representation Power of Graph Neural Networks in Learning ...

He is an associate professor at Beijing University of Posts and Telecommunications, China. His research interests are graph neural networks, data mining and machine learning. He was a postdoc in the Department of Computer Science and Technology at Tsinghua University working with Professor Shiqiang Yang and Professor Peng Cui.

Current focus is on Graph Neural Network modeling. I have a MA from Cambridge, a PhD in Complex Systems and Non Linear Dynamics (School of Informatics, dept of Engineering of the University of Firenze , Italy) and a PhD in (Theoretical) Genetics ( University of Pavia , Italy).

Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction, to appear at NeurIPS’21. New!! Shitong Luo, Chence Shi, Minkai Xu, Jian Tang.

“Graph Neural Networks are one of the hottest areas of machine learning and this book is a wonderful in-depth resource covering a broad range of topics and applications of graph representation learning.”---Jure Leskovec (Associate Professor at Stanford University, and investigator at Chan Zuckerberg Biohub)

Debiased graph neural networks with agnostic label selection bias. IEEE TNNLS. # 2021 [C1] Jianan Zhao, Xiao Wang*, Chuan Shi, Binbin Hu, Guojie Song, Yanfang Ye. Heterogeneous graph structure learning for graph neural networks. AAAI 2021. (CCF-A) [C2] Deyu Bo, Xiao Wang, Chuan Shi, Huawei Shen.

I am currently an assistant professor at Mila-Quebec AI Institute and HEC Montreal. ... Geometric Deep Learning, Graph Neural Networks for Drug Design ...

“Graph Neural Networks are one of the hottest areas of machine learning and this book is a wonderful in-depth resource covering a broad range of topics and applications of graph representation learning.”---Jure Leskovec (Associate Professor at Stanford University, and investigator at Chan Zuckerberg Biohub)

Jan 03, 2022 · A new graph neural network was created to reduce these possible causes of bias. It was designed to work differently by focusing on non-sensitive details about an individual. This model was trained ...

I am Associate Professor of Computer Science at Stanford University, and investigator ... We released PyG: The ultimate library for Graph Neural Networks.

Yao Ma is an assistant professor in the Department of Computer Science at New ... 06/2021 New preprint Is Homophily a Necessity for Graph Neural Networks?

Professor Alejandro Ribeiro ... This class is been taught by me, Alejandro Ribeiro. I am very happy to have a captive audience to listen attentively while I talk ...

Graph Structure Estimation Neural Networks. WWW 2021. [code & data] Deyu Bo, Xiao Wang, Chuan Shi, Huawei Shen.Beyond Low-frequency Information in Graph Convolutional Networks. AAAI 2021. [code & data] Yuanfu Lu, Xunqiang Jiang, Yuan Fang, Chuan Shi.Learning to Pre-train Graph Neural Networks. AAAI 2021. [code & data]

Research to automate the process of neural network design has been going on for few decades with early work being related to automating Multi-Layer Perceptron (MLP) based neural network design [2, 3, 10, 13].

Stanford Data Science Initiative / AI for Health Fall 2019 Annual MeetingNovember 21-22, 2019.

Zhiqian Chen, 陈枳扦, computer science, research, graph mining, deep learning.

Sep 02, 2021 · A graph is the input, and each component (V,E,U) gets updated by a MLP to produce a new graph. Each function subscript indicates a separate function for a different graph attribute at the n-th layer of a GNN model. As is common with neural networks modules or layers, we can stack these GNN layers together.

Xavier Bresson (PhD 2005, EPFL, Switzerland) is Associate Professor in Computer Science at NUS, Singapore. He is a leading researcher in the field of Graph ...

Hi, I am a MSc student at Mila, supervised by professor Jian Tang. My research interests focus on graph neural networks (GNNs).

Mar 04, 2020 · Luana Ruiz: Graph Neural Networks and PhD Life. Luana Ruiz received the Best Student Paper Award at the 27th European Signal Processing Conference in La Coruna, Spain. Luana Ruiz, a Ph.D. student in the Department of Electrical and Systems Engineering in the School of Engineering and Applied Science, recently presented her work on graph ...

Talk “Towards Combining Statistical Relational Learning and Graph Neural Networks for Reasoning” at the annual Mila-Microsoft Workshop. 16th, October, 2019. Invited Talk “Graph Representation Learning and Applications to Drug Discovery” at the first annual conference of Canada Chapter of Chinese Biopharmaceutical Association, 5th, October, 2019

(Here is a technical perspective of this work, written by Prof. Yufei Tao) Linhao Luo, Yixiang Fang*, Xin Cao, Xiaofeng Zhang, Wenjie Zhang. "Detecting Communities from Heterogeneous Graphs: A Context Path-based Graph Neural Network Model", ACM Conference on Information and Knowledge Management (CIKM), pages 1170-1180, 2021.