Jure Leskovec

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Stanford University ∙ Pinterest, Inc.

Professor at Stanford University, Chief Scientist at Pinterest.

Featured Co-authors

Sergey Levine
267 publications
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110 publications
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Christopher Ré
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Combiner: Full Attention Transformer with Sparse Computation Cost

Transformers provide a class of expressive architectures that are extrem...
07/12/2021 ∙ by Hongyu Ren, et al. ∙ 2 ∙ share

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GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings

We present GNNAutoScale (GAS), a framework for scaling arbitrary message...
06/10/2021 ∙ by Matthias Fey, et al. ∙ 9 ∙ share

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QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering

The problem of answering questions using knowledge from pre-trained lang...
04/13/2021 ∙ by Michihiro Yasunaga, et al. ∙ 15 ∙ share

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Language-Agnostic Representation Learning of Source Code from Structure and Context

Source code (Context) and its parsed abstract syntax tree (AST; Structur...
03/21/2021 ∙ by Daniel Zügner, et al. ∙ 5 ∙ share

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OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs

Enabling effective and efficient machine learning (ML) over large-scale ...
03/17/2021 ∙ by Weihua Hu, et al. ∙ 61 ∙ share

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ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations

With massive amounts of atomic simulation data available, there is a hug...
03/02/2021 ∙ by Weihua Hu, et al. ∙ 54 ∙ share

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Model-Agnostic Graph Regularization for Few-Shot Learning

In many domains, relationships between categories are encoded in the kno...
02/14/2021 ∙ by Ethan Shen, et al. ∙ 15 ∙ share

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Driver2vec: Driver Identification from Automotive Data

With increasing focus on privacy protection, alternative methods to iden...
02/10/2021 ∙ by Jingbo Yang, et al. ∙ 62 ∙ share

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Open-World Semi-Supervised Learning

Supervised and semi-supervised learning methods have been traditionally ...
02/06/2021 ∙ by Kaidi Cao, et al. ∙ 3 ∙ share

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Identity-aware Graph Neural Networks

Message passing Graph Neural Networks (GNNs) provide a powerful modeling...
01/25/2021 ∙ by Jiaxuan You, et al. ∙ 9 ∙ share

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Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks

Temporal networks serve as abstractions of many real-world dynamic syste...
01/15/2021 ∙ by Yanbang Wang, et al. ∙ 6 ∙ share

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WILDS: A Benchmark of in-the-Wild Distribution Shifts

Distribution shifts can cause significant degradation in a broad range o...
12/14/2020 ∙ by Pang Wei Koh, et al. ∙ 11 ∙ share

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Design Space for Graph Neural Networks

The rapid evolution of Graph Neural Networks (GNNs) has led to a growing...
11/17/2020 ∙ by Jiaxuan You, et al. ∙ 24 ∙ share

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Coresets for Robust Training of Neural Networks against Noisy Labels

Modern neural networks have the capacity to overfit noisy labels frequen...
11/15/2020 ∙ by Baharan Mirzasoleiman, et al. ∙ 3 ∙ share

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F-FADE: Frequency Factorization for Anomaly Detection in Edge Streams

Edge streams are commonly used to capture interactions in dynamic networ...
11/09/2020 ∙ by Yen-Yu Chang, et al. ∙ 8 ∙ share

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Handling Missing Data with Graph Representation Learning

Machine learning with missing data has been approached in two different ...
10/30/2020 ∙ by Jiaxuan You, et al. ∙ 12 ∙ share

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Graph Information Bottleneck

Representation learning of graph-structured data is challenging because ...
10/24/2020 ∙ by Tailin Wu, et al. ∙ 20 ∙ share

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Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs

One of the fundamental problems in Artificial Intelligence is to perform...
10/22/2020 ∙ by Hongyu Ren, et al. ∙ 23 ∙ share

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Direct Multi-hop Attention based Graph Neural Network

Introducing self-attention mechanism in graph neural networks (GNNs) ach...
09/29/2020 ∙ by Guangtao Wang, et al. ∙ 13 ∙ share

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Inductive Learning on Commonsense Knowledge Graph Completion

Commonsense knowledge graph (CKG) is a special type of knowledge graph (...
09/19/2020 ∙ by Bin Wang, et al. ∙ 29 ∙ share

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Distance Encoding – Design Provably More Powerful Graph Neural Networks for Structural Representation Learning

Learning structural representations of node sets from graph-structured d...
08/31/2020 ∙ by Pan Li, et al. ∙ 42 ∙ share

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OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation

Real-world tasks often exhibit a compositional structure that contains a...
08/17/2020 ∙ by Hongyu Ren, et al. ∙ 1 ∙ share

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Concept Learners for Generalizable Few-Shot Learning

Developing algorithms that are able to generalize to a novel task given ...
07/14/2020 ∙ by Kaidi Cao, et al. ∙ 9 ∙ share

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Graph Structure of Neural Networks

Neural networks are often represented as graphs of connections between n...
07/13/2020 ∙ by Jiaxuan You, et al. ∙ 23 ∙ share

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PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest

Latent user representations are widely adopted in the tech industry for ...
07/07/2020 ∙ by Aditya Pal, et al. ∙ 7 ∙ share

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Neural Subgraph Matching

Subgraph matching is the problem of determining the presence and locatio...
07/06/2020 ∙ by Zhaoyu Lou, et al. ∙ 6 ∙ share

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Improving Query Safety at Pinterest

Query recommendations in search engines is a double edged sword, with un...
06/20/2020 ∙ by Abhijit Mahabal, et al. ∙ 0 ∙ share

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M2P2: Multimodal Persuasion Prediction using Adaptive Fusion

Identifying persuasive speakers in an adversarial environment is a criti...
06/03/2020 ∙ by Chongyang Bai, et al. ∙ 0 ∙ share

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Open Graph Benchmark: Datasets for Machine Learning on Graphs

We present the Open Graph Benchmark (OGB), a diverse set of challenging ...
05/02/2020 ∙ by Weihua Hu, et al. ∙ 64 ∙ share

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Learning to Simulate Complex Physics with Graph Networks

Here we present a general framework for learning simulation, and provide...
02/21/2020 ∙ by Alvaro Sanchez-Gonzalez, et al. ∙ 26 ∙ share

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Entity Context and Relational Paths for Knowledge Graph Completion

Knowledge graph completion aims to predict missing relations between ent...
02/17/2020 ∙ by Hongwei Wang, et al. ∙ 7 ∙ share

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Unifying Graph Convolutional Neural Networks and Label Propagation

Label Propagation (LPA) and Graph Convolutional Neural Networks (GCN) ar...
02/17/2020 ∙ by Hongwei Wang, et al. ∙ 9 ∙ share

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Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings

Answering complex logical queries on large-scale incomplete knowledge gr...
02/14/2020 ∙ by Hongyu Ren, et al. ∙ 12 ∙ share

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G2SAT: Learning to Generate SAT Formulas

The Boolean Satisfiability (SAT) problem is the canonical NP-complete pr...
10/29/2019 ∙ by Jiaxuan You, et al. ∙ 6 ∙ share

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Hyperbolic Graph Convolutional Neural Networks

Graph convolutional neural networks (GCNs) embed nodes in a graph into E...
10/28/2019 ∙ by Ines Chami, et al. ∙ 23 ∙ share

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Improving Graph Attention Networks with Large Margin-based Constraints

Graph Attention Networks (GATs) are the state-of-the-art neural architec...
10/25/2019 ∙ by Guangtao Wang, et al. ∙ 18 ∙ share

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Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks

Modeling sequential interactions between users and items/products is cru...
08/03/2019 ∙ by Srijan Kumar, et al. ∙ 0 ∙ share

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Selection Via Proxy: Efficient Data Selection For Deep Learning

Data selection methods such as active learning and core-set selection ar...
06/26/2019 ∙ by Cody Coleman, et al. ∙ 12 ∙ share

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Position-aware Graph Neural Networks

Learning node embeddings that capture a node's position within the broad...
06/11/2019 ∙ by Jiaxuan You, et al. ∙ 2 ∙ share

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Redundancy-Free Computation Graphs for Graph Neural Networks

Graph Neural Networks (GNNs) are based on repeated aggregations of infor...
06/09/2019 ∙ by Zhihao Jia, et al. ∙ 1 ∙ share

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Data Sketching for Faster Training of Machine Learning Models

Many machine learning problems reduce to the problem of minimizing an ex...
06/05/2019 ∙ by Baharan Mirzasoleiman, et al. ∙ 8 ∙ share

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Pre-training Graph Neural Networks

Many applications of machine learning in science and medicine, including...
05/29/2019 ∙ by Weihua Hu, et al. ∙ 24 ∙ share

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Knowledge Graph Convolutional Networks for Recommender Systems with Label Smoothness Regularization

Knowledge graphs capture interlinked information between entities and th...
05/11/2019 ∙ by Hongwei Wang, et al. ∙ 9 ∙ share

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Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems

Knowledge graphs capture structured information and relations between a ...
05/11/2019 ∙ by Hongwei Wang, et al. ∙ 0 ∙ share

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Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommendation

Knowledge graphs capture structured information and relations between a ...
05/11/2019 ∙ by Hongwei Wang, et al. ∙ 0 ∙ share

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Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems

Recommender systems that can learn from cross-session data to dynamicall...
04/08/2019 ∙ by Jiaxuan You, et al. ∙ 0 ∙ share

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Goal-setting And Achievement In Activity Tracking Apps: A Case Study Of MyFitnessPal

Activity tracking apps often make use of goals as one of their core moti...
04/04/2019 ∙ by Mitchell L. Gordon, et al. ∙ 0 ∙ share

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GNN Explainer: A Tool for Post-hoc Explanation of Graph Neural Networks

Graph Neural Networks (GNNs) are a powerful tool for machine learning on...
03/10/2019 ∙ by Rex Ying, et al. ∙ 10 ∙ share

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Learning Dynamic Embeddings from Temporal Interactions

Modeling a sequence of interactions between users and items (e.g., produ...
12/06/2018 ∙ by Srijan Kumar, et al. ∙ 8 ∙ share

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Predicting pregnancy using large-scale data from a women's health tracking mobile application

Predicting pregnancy has been a fundamental problem in women's health fo...
12/05/2018 ∙ by Bo Liu, et al. ∙ 0 ∙ share

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