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03/18/2023
UNREAL:Unlabeled Nodes Retrieval and Labeling for Heavily-imbalanced Node Classification
Extremely skewed label distributions are common in real-world node class...
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02/27/2023
On Coresets for Clustering in Small Dimensional Euclidean Spaces
We consider the problem of constructing small coresets for k-Median in E...
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01/18/2023
ReFresh: Reducing Memory Access from Exploiting Stable Historical Embeddings for Graph Neural Network Training
A key performance bottleneck when training graph neural network (GNN) mo...
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10/03/2022
ASGNN: Graph Neural Networks with Adaptive Structure
The graph neural network (GNN) models have presented impressive achievem...
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07/12/2022
Optimal Clustering with Noisy Queries via Multi-Armed Bandit
Motivated by many applications, we study clustering with a faulty oracle...
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05/27/2022
Transformers from an Optimization Perspective
Deep learning models such as the Transformer are often constructed by he...
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04/18/2022
BSAL: A Framework of Bi-component Structure and Attribute Learning for Link Prediction
Given the ubiquitous existence of graph-structured data, learning the re...
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03/19/2022
Exploiting Neighbor Effect: Conv-Agnostic GNNs Framework for Graphs with Heterophily
Due to the homophily assumption of graph convolution networks, a common ...
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11/12/2021
Implicit vs Unfolded Graph Neural Networks
It has been observed that graph neural networks (GNN) sometimes struggle...
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10/19/2021
Batched Lipschitz Bandits
In this paper, we study the batched Lipschitz bandit problem, where the ...
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07/08/2021
Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space
Representation learning over temporal networks has drawn considerable at...
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06/21/2021
BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation
Many representative graph neural networks, e.g., GPR-GNN and ChebyNet, a...
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06/10/2021
Learning Based Proximity Matrix Factorization for Node Embedding
Node embedding learns a low-dimensional representation for each node in ...
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06/09/2021
Scaling Up Graph Neural Networks Via Graph Coarsening
Scalability of graph neural networks remains one of the major challenges...
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05/29/2021
Understanding Bandits with Graph Feedback
The bandit problem with graph feedback, proposed in [Mannor and Shamir, ...
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03/10/2021
Graph Neural Networks Inspired by Classical Iterative Algorithms
Despite the recent success of graph neural networks (GNN), common archit...
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12/03/2020
Compressive Privatization: Sparse Distribution Estimation under Locally Differentially Privacy
We consider the problem of discrete distribution estimation under locall...
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07/04/2020
Simple and Deep Graph Convolutional Networks
Graph convolutional networks (GCNs) are a powerful deep learning approac...
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06/30/2020
SCE: Scalable Network Embedding from Sparsest Cut
Large-scale network embedding is to learn a latent representation for ea...
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06/21/2020
Personalized PageRank to a Target Node, Revisited
Personalized PageRank (PPR) is a widely used node proximity measure in g...
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10/07/2019
Dynamic Self-training Framework for Graph Convolutional Networks
Graph neural networks (GNN) such as GCN, GAT, MoNet have achieved state-...
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10/26/2018
Efficient and High-Quality Seeded Graph Matching: Employing High Order Structural Information
Driven by many real applications, we study the problem of seeded graph m...
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09/03/2018
