
Design Space for Graph Neural Networks
The rapid evolution of Graph Neural Networks (GNNs) has led to a growing...
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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...
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FFADE: Frequency Factorization for Anomaly Detection in Edge Streams
Edge streams are commonly used to capture interactions in dynamic networ...
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Handling Missing Data with Graph Representation Learning
Machine learning with missing data has been approached in two different ...
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Graph Information Bottleneck
Representation learning of graphstructured data is challenging because ...
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Beta Embeddings for MultiHop Logical Reasoning in Knowledge Graphs
One of the fundamental problems in Artificial Intelligence is to perform...
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Direct Multihop Attention based Graph Neural Network
Introducing selfattention mechanism in graph neural networks (GNNs) ach...
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Inductive Learning on Commonsense Knowledge Graph Completion
Commonsense knowledge graph (CKG) is a special type of knowledge graph (...
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Distance Encoding – Design Provably More Powerful Graph Neural Networks for Structural Representation Learning
Learning structural representations of node sets from graphstructured d...
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OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation
Realworld tasks often exhibit a compositional structure that contains a...
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Concept Learners for Generalizable FewShot Learning
Developing algorithms that are able to generalize to a novel task given ...
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Graph Structure of Neural Networks
Neural networks are often represented as graphs of connections between n...
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PinnerSage: MultiModal User Embedding Framework for Recommendations at Pinterest
Latent user representations are widely adopted in the tech industry for ...
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Neural Subgraph Matching
Subgraph matching is the problem of determining the presence and locatio...
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Improving Query Safety at Pinterest
Query recommendations in search engines is a double edged sword, with un...
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M2P2: Multimodal Persuasion Prediction using Adaptive Fusion
Identifying persuasive speakers in an adversarial environment is a criti...
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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 ...
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Learning to Simulate Complex Physics with Graph Networks
Here we present a general framework for learning simulation, and provide...
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Entity Context and Relational Paths for Knowledge Graph Completion
Knowledge graph completion aims to predict missing relations between ent...
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Unifying Graph Convolutional Neural Networks and Label Propagation
Label Propagation (LPA) and Graph Convolutional Neural Networks (GCN) ar...
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Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings
Answering complex logical queries on largescale incomplete knowledge gr...
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G2SAT: Learning to Generate SAT Formulas
The Boolean Satisfiability (SAT) problem is the canonical NPcomplete pr...
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Hyperbolic Graph Convolutional Neural Networks
Graph convolutional neural networks (GCNs) embed nodes in a graph into E...
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Improving Graph Attention Networks with Large Marginbased Constraints
Graph Attention Networks (GATs) are the stateoftheart neural architec...
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Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks
Modeling sequential interactions between users and items/products is cru...
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Selection Via Proxy: Efficient Data Selection For Deep Learning
Data selection methods such as active learning and coreset selection ar...
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Positionaware Graph Neural Networks
Learning node embeddings that capture a node's position within the broad...
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RedundancyFree Computation Graphs for Graph Neural Networks
Graph Neural Networks (GNNs) are based on repeated aggregations of infor...
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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...
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Pretraining Graph Neural Networks
Many applications of machine learning in science and medicine, including...
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Knowledge Graph Convolutional Networks for Recommender Systems with Label Smoothness Regularization
Knowledge graphs capture interlinked information between entities and th...
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Knowledgeaware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems
Knowledge graphs capture structured information and relations between a ...
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Knowledgeaware Graph Neural Networks with Label Smoothness Regularization for Recommendation
Knowledge graphs capture structured information and relations between a ...
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Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems
Recommender systems that can learn from crosssession data to dynamicall...
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Goalsetting 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...
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GNN Explainer: A Tool for Posthoc Explanation of Graph Neural Networks
Graph Neural Networks (GNNs) are a powerful tool for machine learning on...
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Learning Dynamic Embeddings from Temporal Interactions
Modeling a sequence of interactions between users and items (e.g., produ...
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Predicting pregnancy using largescale data from a women's health tracking mobile application
Predicting pregnancy has been a fundamental problem in women's health fo...
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Complete the Look: Scenebased Complementary Product Recommendation
Modeling fashion compatibility is challenging due to its complexity and ...
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Offline Behaviors of Online Friends
In this work we analyze traces of mobility and colocation among a group...
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How Powerful are Graph Neural Networks?
Graph Neural Networks (GNNs) for representation learning of graphs broad...
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CASC: ContextAware Segmentation and Clustering for Motif Discovery in Noisy Time Series Data
Complex systems, such as airplanes, cars, or financial markets, produce ...
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Inferring MultiDimensional Rates of Aging from CrossSectional Data
Modeling how individuals evolve over time is a fundamental problem in th...
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Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities
New technologies have enabled the investigation of biology and human hea...
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Hierarchical Graph Representation Learning with Differentiable Pooling
Recently, graph neural networks (GNNs) have revolutionized the field of ...
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Hierarchical Graph Representation Learning withDifferentiable Pooling
Recently, graph neural networks (GNNs) have revolutionized the field of ...
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Drive2Vec: Multiscale StateSpace Embedding of Vehicular Sensor Data
With automobiles becoming increasingly reliant on sensors to perform var...
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Graph Convolutional Policy Network for GoalDirected Molecular Graph Generation
Generating novel graph structures that optimize given objectives while o...
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Graph Convolutional Neural Networks for WebScale Recommender Systems
Recent advancements in deep neural networks for graphstructured data ha...
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Querying Complex Networks in Vector Space
Learning vector embeddings of complex networks is a powerful approach us...
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Jure Leskovec
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Professor at Stanford University, Chief Scientist at Pinterest.