Jian Tang

Biographie

Jian Tang est professeur agrégé au département de sciences de la décision de HEC. Il est aussi professeur associé au département informatique et recherche opérationnelle (DIRO) de l'Université de Montréal et un membre académique principal à Mila – Institut québécois d’intelligence artificielle. Il est titulaire d'une chaire de recherche en IA Canada-CIFAR et le fondateur de BioGeometry, une entreprise en démarrage spécialisée dans l'IA générative pour la découverte d'anticorps. Ses principaux domaines de recherche sont les modèles génératifs profonds, l'apprentissage automatique des graphes et leurs applications à la découverte de médicaments. Il est un leader international dans le domaine de l'apprentissage automatique des graphes, et son travail représentatif sur l'apprentissage de la représentation des nœuds, LINE, a été largement reconnu et cité plus de 5 000 fois. Il a également réalisé de nombreux travaux pionniers sur l'IA pour la découverte de médicaments, notamment le premier cadre d'apprentissage automatique à source ouverte pour la découverte de médicaments, TorchDrug et TorchProtein.

Étudiants actuels

Huiyu Cai

Doctorat - UdeM

Collaborateur·rice de recherche

Farzana Heidari

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Xixian Liu

Doctorat - Université de Montréal

Jiarui Lu

Doctorat - UdeM

Github

Chence Shi

Doctorat - UdeM

Yu Tianshu

Visiteur de recherche indépendant - Chinese University of Hong Kong

Sophie Xhonneux

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Gauthier Gidel

Xinyu Yuan

Doctorat - UdeM

Github

Zhihao Zhan

Doctorat - UdeM

Doctorat - UdeM

Doctorat - HEC

Jianan Zhao

Doctorat - UdeM

Github

Publications

Flaky Performances when Pre-Training on Relational Databases with a Plan for Future Characterization Efforts

Shengchao Liu

David Vazquez

Pierre-Andre Noel

We explore the downstream task performances for graph neural network (GNN) self-supervised learning (SSL) methods trained on subgraphs extra… (voir plus)cted from relational databases (RDBs). Intu-itively, this joint use of SSL and GNNs allows us to leverage more of the available data, which could translate to better results. However, while we observe positive transfer in some cases, others showed systematic performance degradation, including some spectacular ones. We hypothesize a mechanism that could explain this behaviour and draft the plan for future work testing it by characterizing how much relevant information different strategies can (theoretically and/or empirically) extract from (synthetic and/or real) RDBs.

2022-07-22

ICML.cc/2022/Workshop/Pre-Training (accepté)

Neural-Symbolic Models for Logical Queries on Knowledge Graphs

Answering complex first-order logic (FOL) queries on knowledge graphs is a fundamental task for multi-hop reasoning. Traditional symbolic me… (voir plus)thods traverse a complete knowledge graph to extract the answers, which provides good interpretation for each step. Recent neural methods learn geometric embeddings for complex queries. These methods can generalize to incomplete knowledge graphs, but their reasoning process is hard to interpret. In this paper, we propose Graph Neural Network Query Executor (GNN-QE), a neural-symbolic model that enjoys the advantages of both worlds. GNN-QE decomposes a complex FOL query into relation projections and logical operations over fuzzy sets, which provides interpretability for intermediate variables. To reason about the missing links, GNN-QE adapts a graph neural network from knowledge graph completion to execute the relation projections, and models the logical operations with product fuzzy logic. Experiments on 3 datasets show that GNN-QE significantly improves over previous state-of-the-art models in answering FOL queries. Meanwhile, GNN-QE can predict the number of answers without explicit supervision, and provide visualizations for intermediate variables.

2022-06-28

Proceedings of the 39th International Conference on Machine Learning (publié)

Tyger: Task-Type-Generic Active Learning for Molecular Property Prediction

Kuangqi Zhou

Kaixin Wang

Jiashi Feng

Tingyang Xu

Xinchao Wang

How to accurately predict the properties of molecules is an essential problem in AI-driven drug discovery, which generally requires a large … (voir plus)amount of annotation for training deep learning models. Annotating molecules, however, is quite costly because it requires lab experiments conducted by experts. To reduce annotation cost, deep Active Learning (AL) methods are developed to select only the most representative and informative data for annotating. However, existing best deep AL methods are mostly developed for a single type of learning task (e.g., single-label classification), and hence may not perform well in molecular property prediction that involves various task types. In this paper, we propose a Task-type-generic active learning framework (termed Tyger) that is able to handle different types of learning tasks in a unified manner. The key is to learn a chemically-meaningful embedding space and perform active selection fully based on the embeddings, instead of relying on task-type-specific heuristics (e.g., class-wise prediction probability) as done in existing works. Specifically, for learning the embedding space, we instantiate a querying module that learns to translate molecule graphs into corresponding SMILES strings. Furthermore, to ensure that samples selected from the space are both representative and informative, we propose to shape the embedding space by two learning objectives, one based on domain knowledge and the other leveraging feedback from the task learner (i.e., model that performs the learning task at hand). We conduct extensive experiments on benchmark datasets of different task types. Experimental results show that Tyger consistently achieves high AL performance on molecular property prediction, outperforming baselines by a large margin. We also perform ablative experiments to verify the effectiveness of each component in Tyger.

2022-05-23

ArXiv (prépublication)

Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering

Jie Zhang

Xiaokang Zhang

Jifan Yu

Jie Tang

Cuiping Li

Hong Chen

2022-05-01

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (publié)

Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering

Jing Zhang

Xiaokang Zhang

Jifan Yu

Jie Tang

Cuiping Li

Hong Chen

Recent works on knowledge base question answering (KBQA) retrieve subgraphs for easier reasoning. The desired subgraph is crucial as a small… (voir plus) one may exclude the answer but a large one might introduce more noises. However, the existing retrieval is either heuristic or interwoven with the reasoning, causing reasoning on the partial subgraphs, which increases the reasoning bias when the intermediate supervision is missing. This paper proposes a trainable subgraph retriever (SR) decoupled from the subsequent reasoning process, which enables a plug-and-play framework to enhance any subgraph-oriented KBQA model. Extensive experiments demonstrate SR achieves significantly better retrieval and QA performance than existing retrieval methods. Via weakly supervised pre-training as well as the end-to-end fine-tuning, SR achieves new state-of-the-art performance when combined with NSM (He et al., 2021), a subgraph-oriented reasoner, for embedding-based KBQA methods. Codes and datasets are available online (https://github.com/RUCKBReasoning/SubgraphRetrievalKBQA)

2022-02-27

ArXiv (preprint)

Pre-training Molecular Graph Representation with 3D Geometry

Shengchao Liu

Hanchen Wang

Weiyang Liu

Joan Lasenby

Hongyu Guo

Molecular graph representation learning is a fundamental problem in modern drug and material discovery. Molecular graphs are typically model… (voir plus)ed by their 2D topological structures, but it has been recently discovered that 3D geometric information plays a more vital role in predicting molecular functionalities. However, the lack of 3D information in real-world scenarios has significantly impeded the learning of geometric graph representation. To cope with this challenge, we propose the Graph Multi-View Pre-training (GraphMVP) framework where self-supervised learning (SSL) is performed by leveraging the correspondence and consistency between 2D topological structures and 3D geometric views. GraphMVP effectively learns a 2D molecular graph encoder that is enhanced by richer and more discriminative 3D geometry. We further provide theoretical insights to justify the effectiveness of GraphMVP. Finally, comprehensive experiments show that GraphMVP can consistently outperform existing graph SSL methods.

2022-01-28

ICLR.cc/2022/Conference (poster)

High-Order Pooling for Graph Neural Networks with Tensor Decomposition

Metro: Memory-Enhanced Transformer for Retrosynthetic Planning via Reaction Tree

Songtao Liu

Zhitao Ying

Rex Ying

Zuobai Zhang

Peilin Zhao

Lu Lin

Dinghao Wu

Retrosynthetic planning plays a critical role in drug discovery and organic chemistry. Starting from a target molecule as the root node, it … (voir plus)aims to find a complete reaction tree subject to the constraint that all leaf nodes belong to a set of starting materials. The multi-step reactions are crucial because they determine the flow chart in the production of the Organic Chemical Industry. However, existing datasets lack curation of tree-structured multi-step reactions, and fail to provide such reaction trees, limiting models’ understanding of organic molecule transformations. In this work, we first develop a benchmark curated for the retrosynthetic planning task, which consists of 124,869 reaction trees retrieved from the public USPTO-full dataset. On top of that, we propose Metro: Memory-Enhanced Transformer for RetrOsynthetic planning. Specifically, the dependency among molecules in the reaction tree is captured as context information for multi-step retrosynthesis predictions through transformers with a memory module. Extensive experiments show that Metro dramatically outperforms existing single-step retrosynthesis models by at least 10.7% in top-1 accuracy. The experiments demonstrate the superiority of exploiting context information in the retrosynthetic planning task. Moreover, the proposed model can be directly used for synthetic accessibility analysis, as it is trained on reaction trees with the shortest depths. Our work is the first step towards a brand new formulation for retrosynthetic planning in the aspects of data construction, model design, and evaluation. Code is available at https://github.com/SongtaoLiu0823/metro.

2022-01-01

arXiv.org (prépublication)

PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding

Yang Zhang

We are now witnessing significant progress of deep learning methods in a variety of tasks (or datasets) of proteins. However, there is a lac… (voir plus)k of a standard benchmark to evaluate the performance of different methods, which hinders the progress of deep learning in this field. In this paper, we propose such a benchmark called PEER, a comprehensive and multi-task benchmark for Protein sEquence undERstanding. PEER provides a set of diverse protein understanding tasks including protein function prediction, protein localization prediction, protein structure prediction, protein-protein interaction prediction, and protein-ligand interaction prediction. We evaluate different types of sequence-based methods for each task including traditional feature engineering approaches, different sequence encoding methods as well as large-scale pre-trained protein language models. In addition, we also investigate the performance of these methods under the multi-task learning setting. Experimental results show that large-scale pre-trained protein language models achieve the best performance for most individual tasks, and jointly training multiple tasks further boosts the performance. The datasets and source codes of this benchmark are all available at https://github.com/DeepGraphLearning/PEER_Benchmark

Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs

Stephen Bonner

Ufuk Kirik

Ola Engkvist

I. Barrett

Adoption of recently developed methods from machine learning has given rise to creation of drug-discovery knowledge graphs (KGs) that utiliz… (voir plus)e the interconnected nature of the domain. Graph-based modelling of the data, combined with KG embedding (KGE) methods, are promising as they provide a more intuitive representation and are suitable for inference tasks such as predicting missing links. One common application is to produce ranked lists of genes for a given disease, where the rank is based on the perceived likelihood of association between the gene and the disease. It is thus critical that these predictions are not only pertinent but also biologically meaningful. However, KGs can be biased either directly due to the underlying data sources that are integrated or due to modelling choices in the construction of the graph, one consequence of which is that certain entities can get topologically overrepresented. We demonstrate the effect of these inherent structural imbalances, resulting in densely connected entities being highly ranked no matter the context. We provide support for this observation across different datasets, models as well as predictive tasks. Further, we present various graph perturbation experiments which yield more support to the observation that KGE models can be more influenced by the frequency of entities rather than any biological information encoded within the relations. Our results highlight the importance of data modelling choices, and emphasizes the need for practitioners to be mindful of these issues when interpreting model outputs and during KG composition.

2021-12-13

ArXiv (preprint)

Full-Scale Information Diffusion Prediction With Reinforced Recurrent Networks

Cheng Yang

Hao Wang

Chuan Shi

Maosong Sun

Ganqu Cui

Zhiyuan Liu

Information diffusion prediction is an important task, which studies how information items spread among users. With the success of deep lear… (voir plus)ning techniques, recurrent neural networks (RNNs) have shown their powerful capability in modeling information diffusion as sequential data. However, previous works focused on either microscopic diffusion prediction, which aims at guessing who will be the next influenced user at what time, or macroscopic diffusion prediction, which estimates the total numbers of influenced users during the diffusion process. To the best of our knowledge, few attempts have been made to suggest a unified model for both microscopic and macroscopic scales. In this article, we propose a novel full-scale diffusion prediction model based on reinforcement learning (RL). RL incorporates the macroscopic diffusion size information into the RNN-based microscopic diffusion model by addressing the nondifferentiable problem. We also employ an effective structural context extraction strategy to utilize the underlying social graph information. Experimental results show that our proposed model outperforms state-of-the-art baseline models on both microscopic and macroscopic diffusion predictions on three real-world datasets.

2021-09-01

IEEE Transactions on Neural Networks and Learning Systems (publié)

Full-Scale Information Diffusion Prediction With Reinforced Recurrent Networks

Cheng Yang

Hao Wang

Chuan Shi

Maosong Sun

Ganqu Cui

Zhiyuan Liu

2021-09-01

IEEE Transactions on Neural Networks and Learning Systems (published)