Alternatives and similar repositories for asonam20-monstor

Users that are interested in asonam20-monstor are comparing it to the libraries listed below

• kswoo97 / pcl
  Published as a journal paper at DAMI 2023
  ☆18Updated 11 months ago
• chlehdwon / RDB2G-Bench
  [NeurIPS 2025 Datasets and Benchmarks] Source code for the paper RDB2G-Bench: A Comprehensive Benchmark for Automatic Graph Modeling of R…
  ☆22Updated last month
• young917 / EdgeDependentNodeLabel
  KDD23 - Classification of Edge-Dependent Labels of Nodes in Hypergraphs
  ☆19Updated last year
• NAVER-INTEL-Co-Lab / gaudi-geometric-learning
  Handbook for Geometric Deep Learning with Intel Gaudi-v2 Devices
  ☆21Updated 10 months ago
• Graph-COM / GAD-NR
  [WSDM 2024] GAD-NR : Graph Anomaly Detection via Neighborhood Reconstruction
  ☆53Updated last year
• Yujun-Yan / Heterophily_and_oversmoothing
  Codes for "Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks"
  ☆42Updated 2 years ago
• AndyJZhao / Gophormer
  ☆22Updated 3 years ago
• myflashbarry / LLM-benchmarking
  ☆18Updated 11 months ago
• yushundong / PyGDebias
  Open-source Library PyGDebias: Graph Datasets and Fairness-Aware Graph Mining Algorithms
  ☆65Updated last year
• yule-BUAA / DyGLib_TGB
  An Empirical Evaluation of Temporal Graph Benchmark
  ☆37Updated 2 years ago
• RingBDStack / GC-Bench
  An Open and Unified Benchmark for Graph Condensation (submitted to NeurIPS 2024 Datasets and Benchmarks Track)
  ☆20Updated last year
• TaiLvYuanLiang / HGWaveNet
  ☆13Updated last year
• wooner49 / TriCL
  I'm Me, We're Us, and I'm Us: Tri-directional Contrastive Learning on Hypergraphs (AAAI'23)
  ☆39Updated last year
• yuwvandy / FairVGNN
  ☆27Updated 3 years ago
• SitaoLuan / ACM-GNN
  NeurIPS 2022, Revisiting Heterophily For Graph Neural Networks, official PyTorch implementation for Adaptive Channel Mixing (ACM) GNN fra…
  ☆88Updated last year
• GemsLab / CPGNN
  ☆30Updated 4 years ago
• RecklessRonan / GloGNN
  ICML 2022, Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
  ☆46Updated 3 years ago
• Yiminghh / HiGCN
  ☆22Updated last year
• yixinliu233 / GREET
  [AAAI'23] Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating
  ☆53Updated 3 years ago
• WenZhihao666 / TREND
  TREND: TempoRal Event and Node Dynamics for Graph Representation Learning. WWW-2022.
  ☆37Updated last year
• YuanchenBei / Awesome-Self-Supervised-Learning-on-Graphs-with-Heterophily
  A curated list of Heterophilous Graph Self-Supervised Learning papers.
  ☆15Updated 2 years ago
• LUMIA-Group / OrderedGNN
  The official implementation of the paper "Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothing" (ICLR 2023).
  ☆47Updated last year
• jianhao2016 / AllSet
  This is the GitHub repository for our ICLR22 paper: "You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks"
  ☆103Updated 2 years ago
• EnyanDai / RSGNN
  An official PyTorch implementation of "Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels" (WSDM 2022))
  ☆34Updated 3 years ago
• GemsLab / HeteRobust
  How does Heterophily Impact the Robustness of Graph Neural Networks? Theoretical Connections and Practical Implications (KDD'22)
  ☆13Updated 3 years ago
• CUAI / Non-Homophily-Large-Scale
  [NeurIPS 2021] Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
  ☆123Updated 3 years ago
• K-Kyungho / PET
  Official code for "Towards Better Utilzation of Multiple Views for Bundle Recommendation" (CIKM 24 Short)
  ☆21Updated last year
• Xzh0u / GBK-GNN
  Code for GBK-GNN (paper accepted by WWW2022)
  ☆17Updated 3 years ago
• KimMeen / Neural-Temporal-Walks
  [NeurIPS 2022] The official PyTorch implementation of "Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dyna…
  ☆54Updated 3 years ago
• rollingstonezz / Curriculum_learning_for_GNNs
  This is the official repository for NeurIPS 2023 paper "Curriculum Learning for Graph Neural Networks: Which Edges Should We Learn First"
  ☆17Updated 2 years ago