Alternatives and similar repositories for MIG-GT

Users that are interested in MIG-GT are comparing it to the libraries listed below

• CrawlScript / RpHGNN
  Source code and dataset of the paper "Efficient Heterogeneous Graph Learning via Random Projection"
  ☆97Updated last year
• enoche / FREEDOM
  ACM MM'23: A Tale of Two Graphs: Freezing and Denoising Graph Structures for Multimodal Recommendation
  ☆34Updated 2 years ago
• georgeguo-cn / LGMRec
  [AAAI 2024] Official Pytorch Implementation for "LGMRec: Local and Global Graph Learning for Multimodal Recommendation".
  ☆48Updated 8 months ago
• QwQ2000 / TheWebConf24-LTGNN-PyTorch
  TheWebConf'24 full paper - "Linear-Time Graph Neural Networks for Scalable Recommendations"
  ☆21Updated 4 months ago
• HKUDS / LightGCL
  [ICLR'2023] "LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation"
  ☆194Updated 2 years ago
• enoche / BM3
  Pytorch implementation for "Bootstrap Latent Representations for Multi-modal Recommendation"-WWW'23
  ☆61Updated 2 years ago
• liun-online / SpCo
  The source code of SpCo
  ☆34Updated 2 years ago
• CrawlScript / Torch-MGDCF
  Source code (PyTorch) and dataset of the paper "MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering"
  ☆36Updated 7 months ago
• Liuwq-bit / LightGT
  [SIGIR 2023] LightGT: A Light Graph Transformer for Multimedia Recommendation
  ☆27Updated last year
• Yuhan1i / G-Refer
  G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation
  ☆19Updated 9 months ago
• tengxiao1 / Awesome-Graph-Contrastive-Learning-with-Heterophily
  Papers about Graph Contrastive Learning and Graph Self-supervised Learning on Graphs with Heterophily
  ☆38Updated 2 years ago
• zjs123 / DTGB
  Comprehensive Benchmark Dataset for Dynamic Text-Attributed Graphs
  ☆46Updated last year
• WxxShirley / CIKM2023DiRec
  Codes, data, and baselines for CIKM 2023 Long Paper "Dual Intents Graph Modeling for User-centric Group Discovery"
  ☆17Updated 2 years ago
• anananan116 / MBGen
  [CIKM '24] Implementation of "Multi-Behavior Generative Recommendation"
  ☆48Updated last year
• ChangxinTian / RGCF
  [SIGIR'22] Official PyTorch implementation for "Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering".
  ☆18Updated 3 years ago
• HKUDS / AdaGCL
  [KDD'2023] "AdaGCL: Adaptive Graph Contrastive Learning for Recommendation"
  ☆66Updated last year
• THUDM / RecDCL
  RecDCL: Dual Contrastive Learning for Recommendation (WWW'24, Oral)
  ☆29Updated last year
• XYGaoG / Graph-Condensation-Papers
  Awesome Graph Condensation Papers, TKDE'25 paper: Graph Condensation: A Survey.
  ☆48Updated 5 months ago
• nickwzk / InvRL
  The official implementation of InvRL
  ☆13Updated 3 years ago
• HKUDS / DSL
  [IJCAI'2023] "DSL: Denoised Self-Augmented Learning for Social Recommendation"
  ☆30Updated last year
• ivam-he / PSHGCN
  PyTorch implementation of "Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials."
  ☆19Updated 11 months ago
• ptkjw1997 / DGMRec
  Missing Modality Generation for Recommendaton
  ☆32Updated 7 months ago
• BlueGhostYi / BIGCF
  [SIGIR2024] BIGCF: Exploring the Individuality and Collectivity of Intents behind Interactions for Graph Collaborative Filtering
  ☆14Updated last year
• CRIPAC-DIG / LATTICE
  [ACMMM 2021] PyTorch implementation for "Mining Latent Structures for Multimedia Recommendation"
  ☆55Updated last year
• sktsherlock / TAG-Benchmark
  Benchmark
  ☆107Updated last year
• wangz3066 / CorDGT
  ☆16Updated last year
• Peiyance / TransGNN-torch
  TransGNN, SIGIR 2024
  ☆63Updated last year
• zhoushengisnoob / CKD
  Code for The Web Conference 2022 Paper "Collaborative Knowledge Distillation for Heterogeneous Information Network Embedding"
  ☆17Updated 3 years ago
• HKUDS / AutoCF
  [WWW'23] "AutoCF: Automated Self-Supervised Learning for Recommendation"
  ☆49Updated last year
• tsinghua-fib-lab / Graph-Denoising-SocialRec
  The official implementation of "Robust Preference-Guided Denoising for Graph based Social Recommendation" (WWW'23)
  ☆30Updated last year