
Discretetime Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space
Representation learning over temporal networks has drawn considerable at...
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BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation
Many representative graph neural networks, e.g., GPRGNN and ChebyNet, a...
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Learning Based Proximity Matrix Factorization for Node Embedding
Node embedding learns a lowdimensional representation for each node in ...
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Scaling Up Graph Neural Networks Via Graph Coarsening
Scalability of graph neural networks remains one of the major challenges...
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Understanding Bandits with Graph Feedback
The bandit problem with graph feedback, proposed in [Mannor and Shamir, ...
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Graph Neural Networks Inspired by Classical Iterative Algorithms
Despite the recent success of graph neural networks (GNN), common archit...
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Compressive Privatization: Sparse Distribution Estimation under Locally Differentially Privacy
We consider the problem of discrete distribution estimation under locall...
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Simple and Deep Graph Convolutional Networks
Graph convolutional networks (GCNs) are a powerful deep learning approac...
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SCE: Scalable Network Embedding from Sparsest Cut
Largescale network embedding is to learn a latent representation for ea...
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Personalized PageRank to a Target Node, Revisited
Personalized PageRank (PPR) is a widely used node proximity measure in g...
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Dynamic Selftraining Framework for Graph Convolutional Networks
Graph neural networks (GNN) such as GCN, GAT, MoNet have achieved state...
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Efficient and HighQuality 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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GBKMV: An Augmented KMV Sketch for Approximate Containment Similarity Search
In this paper, we study the problem of approximate containment similarit...
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Zengfeng Huang
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