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02/17/2022
Transformer for Graphs: An Overview from Architecture Perspective
Recently, Transformer model, which has achieved great success in many ar...
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01/25/2022
Masked Transformer for Neighhourhood-aware Click-Through Rate Prediction
Click-Through Rate (CTR) prediction, is an essential component of online...
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11/23/2021
CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning
Labels are costly and sometimes unreliable. Noisy label learning, semi-s...
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06/14/2021
PI-GNN: A Novel Perspective on Semi-Supervised Node Classification against Noisy Labels
Semi-supervised node classification, as a fundamental problem in graph l...
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05/29/2021
Learning Graphon Autoencoders for Generative Graph Modeling
Graphon is a nonparametric model that generates graphs with arbitrary si...
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04/14/2021
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
Graph Neural Network (GNN) research is rapidly growing thanks to the cap...
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08/22/2020
Tackling Over-Smoothing for General Graph Convolutional Networks
Increasing the depth of Graph Convolutional Networks (GCN), which in pri...
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06/27/2019
Adversarial Representation Learning on Large-Scale Bipartite Graphs
Graph representation on large-scale bipartite graphs is central for a va...
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09/14/2018
