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graph-deep-learning

This page summarises the open source code of our group, mostly on graph learning & deep learning. More source code will be released as it is ready for publishing. You can visit your GRAND Lab page on Github for more details.

Graph Learning & Deep Learning

  • Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination (NeurIPS 2022)

  • Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs (NeurIPS 2022)

  • Unifying Graph Contrastive Learning with Flexible Contextual Scopes (ICDM 2022)

  • Towards Unsupervised Deep Graph Structure Learning (WWW 2022)

  • Beyond Low-pass Filtering: Graph Convolutional Networks with Automatic Filtering (TKDE 2022)

  • Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning (IJCAI 2021)

  • Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning (AAAI 2021)

  • ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning (CIKM 2021)

  • Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning (TNNLS 2021)

  • Open-World Graph Learning (ICDM 2020)

  • One-Shot Neural Architecture Search: Maximising Diversity to Overcome Catastrophic Forgetting (TPAMI 2020)

  • Graph Stochastic Neural Networks for Semi-supervised Learning (NeurIPS 2020)

  • Graph Geometry Interaction Learning (NeurIPS 2020)

  • Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement (NeurIPS 2020)

  • Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks (KDD 2020)

  • Overcoming Multi-Model Forgetting in One-Shot NAS with Diversity Maximization (CVPR 2020)

  • Unsupervised Domain Adaptive Graph Convolutional Networks (WWW 2020)

  • GSSNN: Graph Smoothing Splines Neural Network (AAAI 2020)

  • Reinforcement Learning based Meta-path Discovery in Large-scale Heterogeneous Information Networks (AAAI 2020)

  • Relation Structure-Aware Heterogeneous Graph Neural Network (ICDM 2019)

  • Graph WaveNet for Deep Spatial-Temporal Graph Modeling (IJCAI 2019)

  • Adversarially regularized graph autoencoder for graph embedding (IJCAI 2018)

  • Binarized attributed network embedding (ICDM 2018)

  • MGAE: marginalized graph autoencoder for graph clustering (CIKM 2017)

  • Tri-party deep network representation (IJCAI 2016)

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