Jure Leskovec

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

2019 · arXiv (Cornell University)

Knowledge graphs capture structured information and relations between a set of entities or items. As such knowledge graphs represent an attractive source of information that could help improve recommender systems. However, existing approaches in this …

GNNExplainer: Generating Explanations for Graph Neural Networks.

2019 · PubMed

Graph Neural Networks (GNNs) are a powerful tool for machine learning on graphs. GNNs combine node feature information with the graph structure by recursively passing neural messages along edges of the input graph. However, incorporating …

Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks

2021 · International Conference on Learning Representations

Temporal networks serve as abstractions of many real-world dynamic systems. These networks typically evolve according to certain laws, such as the law of triadic closure, which is universal in social networks. Inductive representation learning of …

AdaGrid: Adaptive Grid Search for Link Prediction Training Objective

2022 · arXiv (Cornell University)

One of the most important factors that contribute to the success of a machine learning model is a good training objective. Training objective crucially influences the model's performance and generalization capabilities. This paper specifically focuses …

PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning

2024 · arXiv (Cornell University)

We present PyTorch Frame, a PyTorch-based framework for deep learning over multi-modal tabular data. PyTorch Frame makes tabular deep learning easy by providing a PyTorch-based data structure to handle complex tabular data, introducing a model …

Inferring Networks of Substitutable and Complementary Products

2015

To design a useful recommender system, it is important to understand how products relate to each other. For example, while a user is browsing mobile phones, it might make sense to recommend other phones, but …

Learning Structural Node Embeddings via Diffusion Wavelets

2018

Nodes residing in different parts of a graph can have similar structural roles within their local network topology. The identification of such roles provides key insight into the organization of networks and can be used …

Graph Convolutional Neural Networks for Web-Scale Recommender Systems

2018

Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. However, making these methods practical and scalable to web-scale recommendation tasks with billions of items and hundreds …

Hierarchical Graph Representation Learning with Differentiable Pooling

2018 · arXiv (Cornell University)

Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction. However, current GNN methods …

Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks

2019

Modeling sequential interactions between users and items/products is crucial in domains such as e-commerce, social networking, and education. Representation learning presents an attractive opportunity to model the dynamic evolution of users and items, where each …

Handling Missing Data with Graph Representation Learning

2020 · arXiv (Cornell University)

Machine learning with missing data has been approached in two different ways, including feature imputation where missing feature values are estimated based on observed values, and label prediction where downstream labels are learned directly from …

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

2021

Michihiro Yasunaga, Hongyu Ren, Antoine Bosselut, Percy Liang, Jure Leskovec. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

LinkBERT: Pretraining Language Models with Document Links

2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Language model (LM) pretraining captures various knowledge from text corpora, helping downstream NLP tasks. However, existing methods such as BERT model a single document, failing to capture document dependencies and knowledge that spans across documents. …