4.5k citations · 32.3k across the 207 of their papers we have counts for
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Analytical Models for Motifs in Temporal Networks: Discovering Trends and Anomalies
Alexandra Porter, Baharan Mirzasoleiman, Jure Leskovec
Dynamic evolving networks capture temporal relations in domains such as social networks, communication networks, and financial transaction networks. In such networks, temporal moti…
Extending the WILDS Benchmark for Unsupervised Adaptation
Shiori Sagawa, Pang Wei Koh, Tony Lee +17
Machine learning systems deployed in the wild are often trained on a source distribution but deployed on a different target distribution. Unlabeled data can be a powerful point of…
SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs
Hongyu Ren, Hanjun Dai, Bo Dai +4
Knowledge graphs (KGs) capture knowledge in the form of head--relation--tail triples and are a crucial component in many AI systems. There are two important reasoning tasks on KGs:…
Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones
Yushi Bai, Rex Ying, Hongyu Ren +1
Hierarchical relations are prevalent and indispensable for organizing human knowledge captured by a knowledge graph (KG). The key property of hierarchical relations is that they in…
Neural Distance Embeddings for Biological Sequences
Gabriele Corso, Rex Ying, Michal Pándy +3
The development of data-dependent heuristics and representations for biological sequences that reflect their evolutionary distance is critical for large-scale biological research.…
LM-Critic: Language Models for Unsupervised Grammatical Error Correction
Michihiro Yasunaga, Jure Leskovec, Percy Liang
Training a model for grammatical error correction (GEC) requires a set of labeled ungrammatical / grammatical sentence pairs, but manually annotating such pairs can be expensive. R…
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.…
Combiner: Full Attention Transformer with Sparse Computation Cost
Hongyu Ren, Hanjun Dai, Zihang Dai +4
Transformers provide a class of expressive architectures that are extremely effective for sequence modeling. However, the key limitation of transformers is their quadratic memory a…
Deception Detection in Group Video Conversations using Dynamic Interaction Networks
Srijan Kumar, Chongyang Bai, V. S. Subrahmanian +1
Detecting groups of people who are jointly deceptive in video conversations is crucial in settings such as meetings, sales pitches, and negotiations. Past work on deception in vide…
GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings
Matthias Fey, Jan E. Lenssen, Frank Weichert +1
We present GNNAutoScale (GAS), a framework for scaling arbitrary message-passing GNNs to large graphs. GAS prunes entire sub-trees of the computation graph by utilizing historical…
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
Michihiro Yasunaga, Hongyu Ren, Antoine Bosselut +2
The problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question and answe…
Language-Agnostic Representation Learning of Source Code from Structure and Context
Daniel Zügner, Tobias Kirschstein, Michele Catasta +2
Source code (Context) and its parsed abstract syntax tree (AST; Structure) are two complementary representations of the same computer program. Traditionally, designers of machine l…
ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations
Weihua Hu, Muhammed Shuaibi, Abhishek Das +5
With massive amounts of atomic simulation data available, there is a huge opportunity to develop fast and accurate machine learning models to approximate expensive physics-based ca…
Postmortem memory of public figures in news and social media
Robert West, Jure Leskovec, Christopher Potts
Deceased public figures are often said to live on in collective memory. We quantify this phenomenon by tracking mentions of 2,362 public figures in English-language online news and…
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
Weihua Hu, Matthias Fey, Hongyu Ren +3
Enabling effective and efficient machine learning (ML) over large-scale graph data (e.g., graphs with billions of edges) can have a great impact on both industrial and scientific a…
Model-Agnostic Graph Regularization for Few-Shot Learning
Ethan Shen, Maria Brbic, Nicholas Monath +3
In many domains, relationships between categories are encoded in the knowledge graph. Recently, promising results have been achieved by incorporating knowledge graph as side inform…
Driver2vec: Driver Identification from Automotive Data
Jingbo Yang, Ruge Zhao, Meixian Zhu +3
With increasing focus on privacy protection, alternative methods to identify vehicle operator without the use of biometric identifiers have gained traction for automotive data anal…
Identity-aware Graph Neural Networks
Jiaxuan You, Jonathan Gomes-Selman, Rex Ying +1
Message passing Graph Neural Networks (GNNs) provide a powerful modeling framework for relational data. However, the expressive power of existing GNNs is upper-bounded by the 1-Wei…
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development
Kexin Huang, Tianfan Fu, Wenhao Gao +7
Therapeutics machine learning is an emerging field with incredible opportunities for innovatiaon and impact. However, advancement in this field requires formulation of meaningful l…
Open-World Semi-Supervised Learning
Kaidi Cao, Maria Brbic, Jure Leskovec
A fundamental limitation of applying semi-supervised learning in real-world settings is the assumption that unlabeled test data contains only classes previously encountered in the…
Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks
Yanbang Wang, Yen-Yu Chang, Yunyu Liu +2
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…