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08/16/2023
DeSCo: Towards Generalizable and Scalable Deep Subgraph Counting
Subgraph counting is the problem of counting the occurrences of a given ...
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07/08/2023
Learning to Group Auxiliary Datasets for Molecule
The limited availability of annotations in small molecule datasets prese...
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06/15/2023
Hyperbolic Representation Learning: Revisiting and Advancing
The non-Euclidean geometry of hyperbolic spaces has recently garnered co...
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06/06/2023
BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and Graphs
In-Batch contrastive learning is a state-of-the-art self-supervised meth...
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05/05/2023
HiPool: Modeling Long Documents Using Graph Neural Networks
Encoding long sequences in Natural Language Processing (NLP) is a challe...
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04/28/2023
MUDiff: Unified Diffusion for Complete Molecule Generation
We present a new model for generating molecular data by combining discre...
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04/09/2023
Adversarially Robust Neural Architecture Search for Graph Neural Networks
Graph Neural Networks (GNNs) obtain tremendous success in modeling relat...
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12/07/2022
Learning Graph Search Heuristics
Searching for a path between two nodes in a graph is one of the most wel...
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10/21/2022
Efficient Automatic Machine Learning via Design Graphs
Despite the success of automated machine learning (AutoML), which aims t...
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10/21/2022
Diffuser: Efficient Transformers with Multi-hop Attention Diffusion for Long Sequences
Efficient Transformers have been developed for long sequence modeling, d...
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09/30/2022
Metro: Memory-Enhanced Transformer for Retrosynthetic Planning via Reaction Tree
Retrosynthetic planning plays a critical role in drug discovery and orga...
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09/29/2022
How Powerful is Implicit Denoising in Graph Neural Networks
Graph Neural Networks (GNNs), which aggregate features from neighbors, a...
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06/20/2022
GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks
As one of the most popular machine learning models today, graph neural n...
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06/15/2022
Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator
Subsurface simulations use computational models to predict the flow of f...
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04/06/2022
Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020
Graph structured data is ubiquitous in daily life and scientific areas a...
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10/28/2021
Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones
Hierarchical relations are prevalent and indispensable for organizing hu...
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09/20/2021
Neural Distance Embeddings for Biological Sequences
The development of data-dependent heuristics and representations for bio...
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06/24/2021
Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networks
Structural features are important features in graph datasets. However, a...
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03/12/2021
Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification
Recent work on aspect-level sentiment classification has demonstrated th...
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01/25/2021
Identity-aware Graph Neural Networks
Message passing Graph Neural Networks (GNNs) provide a powerful modeling...
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11/17/2020
Design Space for Graph Neural Networks
The rapid evolution of Graph Neural Networks (GNNs) has led to a growing...
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09/29/2020
Direct Multi-hop Attention based Graph Neural Network
Introducing self-attention mechanism in graph neural networks (GNNs) ach...
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02/21/2020
Learning to Simulate Complex Physics with Graph Networks
Here we present a general framework for learning simulation, and provide...
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10/28/2019
Hyperbolic Graph Convolutional Neural Networks
Graph convolutional neural networks (GCNs) embed nodes in a graph into E...
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10/25/2019
Improving Graph Attention Networks with Large Margin-based Constraints
Graph Attention Networks (GATs) are the state-of-the-art neural architec...
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10/23/2019
Neural Execution of Graph Algorithms
Graph Neural Networks (GNNs) are a powerful representational tool for so...
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06/11/2019
Position-aware Graph Neural Networks
Learning node embeddings that capture a node's position within the broad...
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06/09/2019
Redundancy-Free Computation Graphs for Graph Neural Networks
Graph Neural Networks (GNNs) are based on repeated aggregations of infor...
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03/10/2019
GNN Explainer: A Tool for Post-hoc Explanation of Graph Neural Networks
Graph Neural Networks (GNNs) are a powerful tool for machine learning on...
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06/22/2018
Hierarchical Graph Representation Learning with Differentiable Pooling
Recently, graph neural networks (GNNs) have revolutionized the field of ...
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06/22/2018
Hierarchical Graph Representation Learning withDifferentiable Pooling
Recently, graph neural networks (GNNs) have revolutionized the field of ...
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06/07/2018
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
Generating novel graph structures that optimize given objectives while o...
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06/06/2018
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
Recent advancements in deep neural networks for graph-structured data ha...
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02/24/2018
GraphRNN: A Deep Generative Model for Graphs
Modeling and generating graphs is fundamental for studying networks in b...
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06/07/2017