776 citations · 5k across the 72 of their papers we have counts for
18 papers · 1 filter
Continuous Graph Neural Networks
Louis-Pascal A. C. Xhonneux, Meng Qu, Jian Tang
This paper builds on the connection between graph neural networks and traditional dynamical systems. We propose continuous graph neural networks (CGNN), which generalise existing g…
Deep geometric knowledge distillation with graphs
Carlos Lassance, Myriam Bontonou, Ghouthi Boukli Hacene +3
In most cases deep learning architectures are trained disregarding the amount of operations and energy consumption. However, some applications, like embedded systems, can be resour…
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
Xiaozhi Wang, Tianyu Gao, Zhaocheng Zhu +4
Pre-trained language representation models (PLMs) cannot well capture factual knowledge from text. In contrast, knowledge embedding (KE) methods can effectively represent the relat…
Structural Robustness for Deep Learning Architectures
Carlos Lassance, Vincent Gripon, Jian Tang +1
Deep Networks have been shown to provide state-of-the-art performance in many machine learning challenges. Unfortunately, they are susceptible to various types of noise, including…
Data-Driven Approach to Encoding and Decoding 3-D Crystal Structures
Jordan Hoffmann, Louis Maestrati, Yoshihide Sawada +3
Generative models have achieved impressive results in many domains including image and text generation. In the natural sciences, generative models have led to rapid progress in aut…
GraphMix: Improved Training of GNNs for Semi-Supervised Learning
Vikas Verma, Meng Qu, Kenji Kawaguchi +4
We present GraphMix, a regularization method for Graph Neural Network based semi-supervised object classification, whereby we propose to train a fully-connected network jointly wit…
An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation
Yanru Qu, Ting Bai, Weinan Zhang +2
This paper studies graph-based recommendation, where an interaction graph is constructed from historical records and is lever-aged to alleviate data sparsity and cold start problem…
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
Fan-Yun Sun, Jordan Hoffmann, Vikas Verma +1
This paper studies learning the representations of whole graphs in both unsupervised and semi-supervised scenarios. Graph-level representations are critical in a variety of real-wo…
Weakly-supervised Knowledge Graph Alignment with Adversarial Learning
Meng Qu, Jian Tang, Yoshua Bengio
This paper studies aligning knowledge graphs from different sources or languages. Most existing methods train supervised methods for the alignment, which usually require a large nu…
Learning Powerful Policies by Using Consistent Dynamics Model
Shagun Sodhani, Anirudh Goyal, Tristan Deleu +3
Model-based Reinforcement Learning approaches have the promise of being sample efficient. Much of the progress in learning dynamics models in RL has been made by learning models vi…
Probabilistic Logic Neural Networks for Reasoning
Meng Qu, Jian Tang
Knowledge graph reasoning, which aims at predicting the missing facts through reasoning with the observed facts, is critical to many applications. Such a problem has been widely ex…
vGraph: A Generative Model for Joint Community Detection and Node Representation Learning
Fan-Yun Sun, Meng Qu, Jordan Hoffmann +2
This paper focuses on two fundamental tasks of graph analysis: community detection and node representation learning, which capture the global and local structures of graphs, respec…
Drug-Drug Adverse Effect Prediction with Graph Co-Attention
Andreea Deac, Yu-Hsiang Huang, Petar Veličković +2
Complex or co-existing diseases are commonly treated using drug combinations, which can lead to higher risk of adverse side effects. The detection of polypharmacy side effects is u…
GMNN: Graph Markov Neural Networks
Meng Qu, Yoshua Bengio, Jian Tang
This paper studies semi-supervised object classification in relational data, which is a fundamental problem in relational data modeling. The problem has been extensively studied in…
Introducing Graph Smoothness Loss for Training Deep Learning Architectures
Myriam Bontonou, Carlos Lassance, Ghouthi Boukli Hacene +3
We introduce a novel loss function for training deep learning architectures to perform classification. It consists in minimizing the smoothness of label signals on similarity graph…
GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding
Zhaocheng Zhu, Shizhen Xu, Meng Qu +1
Learning continuous representations of nodes is attracting growing interest in both academia and industry recently, due to their simplicity and effectiveness in a variety of applic…
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie +1
We study the problem of learning representations of entities and relations in knowledge graphs for predicting missing links. The success of such a task heavily relies on the abilit…
Session-based Social Recommendation via Dynamic Graph Attention Networks
Weiping Song, Zhiping Xiao, Yifan Wang +3
Online communities such as Facebook and Twitter are enormously popular and have become an essential part of the daily life of many of their users. Through these platforms, users ca…