Big Data and Multi-modal Computing Group, CRIPAC (CRIPAC-DIG)

Big Data and Multi-modal Computing Group, CRIPAC

CRIPAC-DIG

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Big Data and Multi-modal Computing Group, Center for Research on Intelligent Perception and Computing

Location:Beijing, China

Home Page:http://www.cripac.ia.ac.cn/CN/model/index.htm

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Big Data and Multi-modal Computing Group, CRIPAC's repositories

GRACE

[GRL+ @ ICML 2020] PyTorch implementation for "Deep Graph Contrastive Representation Learning" (https://arxiv.org/abs/2006.04131v2)

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TextING

[ACL 2020] Tensorflow implementation for "Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks"

GCA

[WWW 2021] Source code for "Graph Contrastive Learning with Adaptive Augmentation"

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H-GCN

[IJCAI 2019] Source code and datasets for "Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification"

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NGNN

[WWW 2019] Code and dataset for "Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks"

A-PGNN

[TKDE 2021] Source code and datasets for the paper "Personalizing Graph Neural Networks with Attention Mechanism for Session-based Recommendation"

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Fi_GNN

[CIKM 2019] Code and dataset for "Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction"

TAGNN

[SIGIR 2020] Python implementation for "TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation"

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GET

[WWW 2022] The source code of "Evidence-aware Fake News Detection with Graph Neural Networks"

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DGCF

[ICDM 2020] Python implementation for "Dynamic Graph Collaborative Filtering."

LATTICE

[ACMMM 2021] PyTorch implementation for "Mining Latent Structures for Multimedia Recommendation"

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DyGCN

Code for "DyGCN: Dynamic Graph Embedding with Graph Convolutional Network"

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GETRAL

The source code of "Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networks

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tgm-dlm

Code for AAAI24 paper Text-Guided Molecule Generation with Diffusion Language Model

LogicCheckGPT

[ACL 2024] Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models. Detect and mitigate object hallucinations in LVLMs by itself through logical closed loops.

AUG-MAE

Code for AAAI'24 paper "Rethinking Graph Masked Autoencoders through Alignment and Uniformity”.

SCGAN

[ICME 2019] Source code and datasets for "Semi-supervised Compatibility Learning Across Categories for Clothing Matching"

GHRM

[WWW 2021] Source code and datasets for the paper "Graph-based Hierarchical Relevance Matching Signals for Ad-hoc Retrieval".

CF-FEND

[SIGIR 2022] Source code and datasets for "Bias Mitigation for Evidence-aware Fake News Detection by Causal Intervention".

DESTINE

[CIKM 2021] Implementations for Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction

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RHGN

Source code for CIKM 2021 paper for Relation-aware Heterogeneous Graph for User Profiling

GRMM

[AAAI 2021] PyTorch implementation for "A Graph-based Relevance Matching Model for Ad-hoc Retrieval"

RecTextAttack

[ACL 2024] PyTorch implementation for "Stealthy Attack on Large Language Model based Recommendation"

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LGCF

[CIKM 2021] The source code of "Fully Hyperbolic Graph Convolution Network for Recommendation"

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hetgsl

[CIKM 2021] Code and dataset for "Label-informed Graph Structure Learning for Node Classification"

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MV-RNN

[TKDE 2018] Code for "MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation"

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Distance2Pre

[PAKDD 2019] Code for "Distance2Pre: Personalized Spatial Preference for Next Point-of-Interest Prediction"

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HCA

[Neurocomputing 2019] Code for "A Hierarchical Contextual Attention-based Network for Sequential Recommendation"

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website

Code for generate the publication page.

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