diff --git a/.github/citation/citation.json b/.github/citation/citation.json
index f4d1491..ec44de2 100644
--- a/.github/citation/citation.json
+++ b/.github/citation/citation.json
@@ -1 +1 @@
-{"$P^3$: Distributed Deep Graph Learning at Scale": {"citation": 82, "last update": "2023-10-01"}, "G$^3$: When Graph Neural Networks Meet Parallel Graph Processing Systems on GPUs": {"citation": 36, "last update": "2023-10-01"}, "PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication": {"citation": 30, "last update": "2023-10-01"}, "EPQuant: A Graph Neural Network Compression Approach Based on Product Quantization": {"citation": 5, "last update": "2023-10-01"}, "Graph Neural Networks in TensorFlow and Keras with Spektral": {"citation": 211, "last update": "2023-10-02"}, "Efficient Scaling of Dynamic Graph Neural Networks": {"citation": 9, "last update": "2023-10-02"}, "Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining": {"citation": 22, "last update": "2023-10-02"}, "QGTC: Accelerating Quantized Graph Neural Networks via GPU Tensor Core": {"citation": 25, "last update": "2023-10-02"}, "BlockGNN: Towards Efficient GNN Acceleration Using Block-Circulant Weight Matrices": {"citation": 24, "last update": "2023-10-02"}, "Learned Low Precision Graph Neural Networks": {"citation": 24, "last update": "2023-10-02"}, "2PGraph: Accelerating GNN Training over Large Graphs on GPU Clusters": {"citation": 11, "last update": "2023-10-02"}, "Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc": {"citation": 165, "last update": "2023-10-02"}, "Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture": {"citation": 42, "last update": "2023-10-02"}, "GraphFM: Improving Large-Scale GNN Training via Feature Momentum": {"citation": 11, "last update": "2023-10-02"}, "Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads": {"citation": 81, "last update": "2023-10-02"}, "PaGraph: Scaling GNN Training on Large Graphs via Computation-aware Caching": {"citation": 85, "last update": "2023-10-02"}, "NeuGraph: Parallel Deep Neural Network Computation on Large Graphs": {"citation": 208, "last update": "2023-10-02"}, "Computing Graph Neural Networks: A Survey from Algorithms to Accelerators": {"citation": 147, "last update": "2023-10-02"}, "ZIPPER: Exploiting Tile- and Operator-level Parallelism for General and Scalable Graph Neural Network Acceleration": {"citation": 4, "last update": "2023-10-02"}, "GCNAX: A Flexible and Energy-efficient Accelerator for Graph Convolutional Neural Networks": {"citation": 89, "last update": "2023-10-02"}, "DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs": {"citation": 84, "last update": "2023-10-02"}, "SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization": {"citation": 28, "last update": "2023-10-02"}, "CogDL: A Toolkit for Deep Learning on Graphs": {"citation": 14, "last update": "2023-10-02"}, "AliGraph: A Comprehensive Graph Neural Network Platform": {"citation": 228, "last update": "2023-10-02"}, "Hardware Acceleration of Large Scale GCN Inference": {"citation": 61, "last update": "2023-10-03"}, "Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks": {"citation": 20, "last update": "2023-10-03"}, "TARe: Task-Adaptive in-situ ReRAM Computing for Graph Learning": {"citation": 10, "last update": "2023-10-03"}, "GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design": {"citation": 22, "last update": "2023-10-03"}, "Hyperscale FPGA-as-a-service architecture for large-scale distributed graph neural network": {"citation": 12, "last update": "2023-10-03"}, "Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective": {"citation": 24, "last update": "2023-10-03"}, "DRGN: a dynamically reconfigurable accelerator for graph neural networks": {"citation": 1, "last update": "2023-10-03"}, "Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs": {"citation": 24, "last update": "2023-10-03"}, "Efficient Data Loader for Fast Sampling-Based GNN Training on Large Graphs": {"citation": 20, "last update": "2023-10-03"}, "Graphite: Optimizing Graph Neural Networks on CPUs Through Cooperative Software-Hardware Techniques": {"citation": 10, "last update": "2023-10-03"}, "Rubik: A Hierarchical Architecture for Efficient Graph Learning": {"citation": 11, "last update": "2023-10-03"}, "HyGCN: A GCN Accelerator with Hybrid Architecture": {"citation": 248, "last update": "2023-10-03"}, "GNNIE: GNN Inference Engine with Load-balancing and Graph-specific Caching": {"citation": 8, "last update": "2023-10-03"}, "FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems": {"citation": 62, "last update": "2023-10-03"}, "EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression": {"citation": 38, "last update": "2023-10-03"}, "G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency": {"citation": 18, "last update": "2023-10-03"}, "Marius++: Large-Scale Training of Graph Neural Networks on a Single Machine": {"citation": 0, "last update": "2023-10-03"}, "GRIP: A Graph Neural Network Accelerator Architecture": {"citation": 67, "last update": "2023-10-03"}, "GCNear: A Hybrid Architecture for Efficient GCN Training with Near-Memory Processing": {"citation": 8, "last update": "2023-10-03"}, "FlowGNN: A Dataflow Architecture for Universal Graph Neural Network Inference via Multi-Queue Streaming": {"citation": 7, "last update": "2023-10-03"}, "ReGraphX: NoC-enabled 3D Heterogeneous ReRAM Architecture for Training Graph Neural Networks": {"citation": 25, "last update": "2023-10-03"}, "GIST: Distributed Training for Large-Scale Graph Convolutional Networks": {"citation": 7, "last update": "2023-10-03"}, "Binary Graph Neural Networks": {"citation": 40, "last update": "2023-10-03"}, "Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis": {"citation": 18, "last update": "2023-10-03"}, "GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms": {"citation": 118, "last update": "2023-10-09"}, "Seastar: Vertex-Centric Programming for Graph Neural Networks": {"citation": 35, "last update": "2023-10-09"}, "PaSca: A Graph Neural Architecture Search System under the Scalable Paradigm": {"citation": 27, "last update": "2023-10-09"}, "AGL: A Scalable System for Industrial-purpose Graph Machine Learning": {"citation": 83, "last update": "2023-10-09"}, "GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks": {"citation": 78, "last update": "2023-10-09"}, "Graphiler: Optimizing Graph Neural Networks with Message Passing Data Flow Graph": {"citation": 16, "last update": "2023-10-09"}, "DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks": {"citation": 74, "last update": "2023-10-09"}, "DIG: A Turnkey Library for Diving into Graph Deep Learning Research": {"citation": 65, "last update": "2023-10-09"}, "Accelerating Graph Convolutional Networks Using Crossbar-based Processing-In-Memory Architectures": {"citation": 17, "last update": "2023-10-09"}, "Understanding the Design Space of Sparse/Dense Multiphase Dataflows for Mapping Graph Neural Networks on Spatial Accelerators": {"citation": 5, "last update": "2023-10-09"}, "Understanding and Bridging the Gaps in Current GNN Performance Optimizations": {"citation": 52, "last update": "2023-10-09"}, "MGG: Accelerating Graph Neural Networks with Fine-Grained Intra-Kernel Communication-Computation Pipelining on Multi-GPU Platforms": {"citation": 2, "last update": "2023-10-09"}, "GNNPipe: Accelerating Distributed Full-Graph GNN Training with Pipelined Model Parallelism": {"citation": 0, "last update": "2023-10-09"}, "GNN-PIM: A Processing-in-Memory Architecture for Graph Neural Networks": {"citation": 20, "last update": "2023-10-09"}, "Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks": {"citation": 24, "last update": "2023-10-09"}, "Distributed Graph Neural Network Training: A Survey": {"citation": 10, "last update": "2023-10-09"}, "Bi-GCN: Binary Graph Convolutional Network": {"citation": 33, "last update": "2023-10-09"}, "Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs": {"citation": 14, "last update": "2023-10-09"}, "PCGCN: Partition-Centric Processing for Accelerating Graph Convolutional Network": {"citation": 40, "last update": "2023-10-09"}, "Hardware Acceleration of Graph Neural Networks": {"citation": 102, "last update": "2023-10-09"}, "Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Graphs": {"citation": 13, "last update": "2023-10-09"}, "DGCL: An Efficient Communication Library for Distributed GNN Training": {"citation": 46, "last update": "2023-10-09"}, "TLPGNN: A Lightweight Two-Level Parallelism Paradigm for Graph Neural Network Computation on GPU": {"citation": 7, "last update": "2023-10-12"}, "GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings": {"citation": 88, "last update": "2023-10-12"}, "I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization": {"citation": 60, "last update": "2023-10-12"}, "ByteGNN: Efficient Graph Neural Network Training at Large Scale": {"citation": 31, "last update": "2023-10-12"}, "AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing": {"citation": 206, "last update": "2023-10-12"}, "GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs": {"citation": 103, "last update": "2023-10-12"}, "DyGNN: Algorithm and Architecture Support of vertex Dynamic Pruning for Graph Neural Networks": {"citation": 12, "last update": "2023-10-12"}, "BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing": {"citation": 23, "last update": "2023-10-12"}, "EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks": {"citation": 136, "last update": "2023-10-12"}, "Reducing Communication in Graph Neural Network Training": {"citation": 76, "last update": "2023-10-12"}, "fuseGNN: Accelerating Graph Convolutional Neural Network Training on GPGPU": {"citation": 13, "last update": "2023-10-12"}, "Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks": {"citation": 825, "last update": "2023-10-12"}, "Fast Graph Representation Learning with PyTorch Geometric": {"citation": 3110, "last update": "2023-10-12"}, "Relational Inductive Biases, Deep Learning, and Graph Networks": {"citation": 2981, "last update": "2023-10-12"}, "FlexGraph: A Flexible and Efficient Distributed Framework for GNN Training": {"citation": 35, "last update": "2023-10-12"}, "TigerGraph: A Native MPP Graph Database": {"citation": 53, "last update": "2023-10-12"}, "Degree-Quant: Quantization-Aware Training for Graph Neural Networks": {"citation": 111, "last update": "2023-10-12"}, "BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling": {"citation": 29, "last update": "2023-10-12"}, "Binarized Graph Neural Network": {"citation": 22, "last update": "2023-10-12"}, "GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration": {"citation": 7, "last update": "2023-10-12"}, "GNNLab: A Factored System for Sample-based GNN Training over GPUs": {"citation": 29, "last update": "2023-10-12"}, "StreamGCN: Accelerating Graph Convolutional Networks with Streaming Processing": {"citation": 2, "last update": "2023-10-12"}, "FusedMM: A Unified SDDMM-SpMM Kernel for Graph Embedding and Graph Neural Networks": {"citation": 27, "last update": "2023-10-12"}}
\ No newline at end of file
+{"GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design": {"citation": 22, "last update": "2023-10-03"}, "Hyperscale FPGA-as-a-service architecture for large-scale distributed graph neural network": {"citation": 12, "last update": "2023-10-03"}, "Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective": {"citation": 24, "last update": "2023-10-03"}, "DRGN: a dynamically reconfigurable accelerator for graph neural networks": {"citation": 1, "last update": "2023-10-03"}, "Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs": {"citation": 24, "last update": "2023-10-03"}, "Efficient Data Loader for Fast Sampling-Based GNN Training on Large Graphs": {"citation": 20, "last update": "2023-10-03"}, "Graphite: Optimizing Graph Neural Networks on CPUs Through Cooperative Software-Hardware Techniques": {"citation": 10, "last update": "2023-10-03"}, "Rubik: A Hierarchical Architecture for Efficient Graph Learning": {"citation": 11, "last update": "2023-10-03"}, "HyGCN: A GCN Accelerator with Hybrid Architecture": {"citation": 248, "last update": "2023-10-03"}, "GNNIE: GNN Inference Engine with Load-balancing and Graph-specific Caching": {"citation": 8, "last update": "2023-10-03"}, "FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems": {"citation": 62, "last update": "2023-10-03"}, "EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression": {"citation": 38, "last update": "2023-10-03"}, "G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency": {"citation": 18, "last update": "2023-10-03"}, "Marius++: Large-Scale Training of Graph Neural Networks on a Single Machine": {"citation": 0, "last update": "2023-10-03"}, "GRIP: A Graph Neural Network Accelerator Architecture": {"citation": 67, "last update": "2023-10-03"}, "GCNear: A Hybrid Architecture for Efficient GCN Training with Near-Memory Processing": {"citation": 8, "last update": "2023-10-03"}, "FlowGNN: A Dataflow Architecture for Universal Graph Neural Network Inference via Multi-Queue Streaming": {"citation": 7, "last update": "2023-10-03"}, "ReGraphX: NoC-enabled 3D Heterogeneous ReRAM Architecture for Training Graph Neural Networks": {"citation": 25, "last update": "2023-10-03"}, "GIST: Distributed Training for Large-Scale Graph Convolutional Networks": {"citation": 7, "last update": "2023-10-03"}, "Binary Graph Neural Networks": {"citation": 40, "last update": "2023-10-03"}, "Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis": {"citation": 18, "last update": "2023-10-03"}, "GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms": {"citation": 118, "last update": "2023-10-09"}, "Seastar: Vertex-Centric Programming for Graph Neural Networks": {"citation": 35, "last update": "2023-10-09"}, "PaSca: A Graph Neural Architecture Search System under the Scalable Paradigm": {"citation": 27, "last update": "2023-10-09"}, "AGL: A Scalable System for Industrial-purpose Graph Machine Learning": {"citation": 83, "last update": "2023-10-09"}, "GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks": {"citation": 78, "last update": "2023-10-09"}, "Graphiler: Optimizing Graph Neural Networks with Message Passing Data Flow Graph": {"citation": 16, "last update": "2023-10-09"}, "DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks": {"citation": 74, "last update": "2023-10-09"}, "DIG: A Turnkey Library for Diving into Graph Deep Learning Research": {"citation": 65, "last update": "2023-10-09"}, "Accelerating Graph Convolutional Networks Using Crossbar-based Processing-In-Memory Architectures": {"citation": 17, "last update": "2023-10-09"}, "Understanding the Design Space of Sparse/Dense Multiphase Dataflows for Mapping Graph Neural Networks on Spatial Accelerators": {"citation": 5, "last update": "2023-10-09"}, "Understanding and Bridging the Gaps in Current GNN Performance Optimizations": {"citation": 52, "last update": "2023-10-09"}, "MGG: Accelerating Graph Neural Networks with Fine-Grained Intra-Kernel Communication-Computation Pipelining on Multi-GPU Platforms": {"citation": 2, "last update": "2023-10-09"}, "GNNPipe: Accelerating Distributed Full-Graph GNN Training with Pipelined Model Parallelism": {"citation": 0, "last update": "2023-10-09"}, "GNN-PIM: A Processing-in-Memory Architecture for Graph Neural Networks": {"citation": 20, "last update": "2023-10-09"}, "Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks": {"citation": 24, "last update": "2023-10-09"}, "Distributed Graph Neural Network Training: A Survey": {"citation": 10, "last update": "2023-10-09"}, "Bi-GCN: Binary Graph Convolutional Network": {"citation": 33, "last update": "2023-10-09"}, "Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs": {"citation": 14, "last update": "2023-10-09"}, "PCGCN: Partition-Centric Processing for Accelerating Graph Convolutional Network": {"citation": 40, "last update": "2023-10-09"}, "Hardware Acceleration of Graph Neural Networks": {"citation": 102, "last update": "2023-10-09"}, "Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Graphs": {"citation": 13, "last update": "2023-10-09"}, "DGCL: An Efficient Communication Library for Distributed GNN Training": {"citation": 46, "last update": "2023-10-09"}, "TLPGNN: A Lightweight Two-Level Parallelism Paradigm for Graph Neural Network Computation on GPU": {"citation": 7, "last update": "2023-10-12"}, "GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings": {"citation": 88, "last update": "2023-10-12"}, "I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization": {"citation": 60, "last update": "2023-10-12"}, "ByteGNN: Efficient Graph Neural Network Training at Large Scale": {"citation": 31, "last update": "2023-10-12"}, "AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing": {"citation": 206, "last update": "2023-10-12"}, "GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs": {"citation": 103, "last update": "2023-10-12"}, "DyGNN: Algorithm and Architecture Support of vertex Dynamic Pruning for Graph Neural Networks": {"citation": 12, "last update": "2023-10-12"}, "BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing": {"citation": 23, "last update": "2023-10-12"}, "EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks": {"citation": 136, "last update": "2023-10-12"}, "Reducing Communication in Graph Neural Network Training": {"citation": 76, "last update": "2023-10-12"}, "fuseGNN: Accelerating Graph Convolutional Neural Network Training on GPGPU": {"citation": 13, "last update": "2023-10-12"}, "Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks": {"citation": 825, "last update": "2023-10-12"}, "Fast Graph Representation Learning with PyTorch Geometric": {"citation": 3110, "last update": "2023-10-12"}, "Relational Inductive Biases, Deep Learning, and Graph Networks": {"citation": 2981, "last update": "2023-10-12"}, "FlexGraph: A Flexible and Efficient Distributed Framework for GNN Training": {"citation": 35, "last update": "2023-10-12"}, "TigerGraph: A Native MPP Graph Database": {"citation": 53, "last update": "2023-10-12"}, "Degree-Quant: Quantization-Aware Training for Graph Neural Networks": {"citation": 111, "last update": "2023-10-12"}, "BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling": {"citation": 29, "last update": "2023-10-12"}, "Binarized Graph Neural Network": {"citation": 22, "last update": "2023-10-12"}, "GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration": {"citation": 7, "last update": "2023-10-12"}, "GNNLab: A Factored System for Sample-based GNN Training over GPUs": {"citation": 29, "last update": "2023-10-12"}, "StreamGCN: Accelerating Graph Convolutional Networks with Streaming Processing": {"citation": 2, "last update": "2023-10-12"}, "FusedMM: A Unified SDDMM-SpMM Kernel for Graph Embedding and Graph Neural Networks": {"citation": 27, "last update": "2023-10-12"}, "$P^3$: Distributed Deep Graph Learning at Scale": {"citation": 85, "last update": "2023-10-15"}, "G$^3$: When Graph Neural Networks Meet Parallel Graph Processing Systems on GPUs": {"citation": 36, "last update": "2023-10-15"}, "PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication": {"citation": 30, "last update": "2023-10-15"}, "EPQuant: A Graph Neural Network Compression Approach Based on Product Quantization": {"citation": 5, "last update": "2023-10-15"}, "Graph Neural Networks in TensorFlow and Keras with Spektral": {"citation": 216, "last update": "2023-10-15"}, "Efficient Scaling of Dynamic Graph Neural Networks": {"citation": 9, "last update": "2023-10-15"}, "Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining": {"citation": 22, "last update": "2023-10-15"}, "QGTC: Accelerating Quantized Graph Neural Networks via GPU Tensor Core": {"citation": 25, "last update": "2023-10-15"}, "BlockGNN: Towards Efficient GNN Acceleration Using Block-Circulant Weight Matrices": {"citation": 25, "last update": "2023-10-15"}, "Learned Low Precision Graph Neural Networks": {"citation": 24, "last update": "2023-10-15"}, "2PGraph: Accelerating GNN Training over Large Graphs on GPU Clusters": {"citation": 11, "last update": "2023-10-15"}, "Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc": {"citation": 165, "last update": "2023-10-15"}, "Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture": {"citation": 44, "last update": "2023-10-15"}, "GraphFM: Improving Large-Scale GNN Training via Feature Momentum": {"citation": 11, "last update": "2023-10-15"}, "Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads": {"citation": 82, "last update": "2023-10-15"}, "PaGraph: Scaling GNN Training on Large Graphs via Computation-aware Caching": {"citation": 87, "last update": "2023-10-15"}, "NeuGraph: Parallel Deep Neural Network Computation on Large Graphs": {"citation": 208, "last update": "2023-10-15"}, "Computing Graph Neural Networks: A Survey from Algorithms to Accelerators": {"citation": 149, "last update": "2023-10-15"}, "ZIPPER: Exploiting Tile- and Operator-level Parallelism for General and Scalable Graph Neural Network Acceleration": {"citation": 4, "last update": "2023-10-15"}, "GCNAX: A Flexible and Energy-efficient Accelerator for Graph Convolutional Neural Networks": {"citation": 90, "last update": "2023-10-15"}, "DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs": {"citation": 84, "last update": "2023-10-15"}, "SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization": {"citation": 28, "last update": "2023-10-15"}, "CogDL: A Toolkit for Deep Learning on Graphs": {"citation": 14, "last update": "2023-10-15"}, "AliGraph: A Comprehensive Graph Neural Network Platform": {"citation": 230, "last update": "2023-10-15"}, "Hardware Acceleration of Large Scale GCN Inference": {"citation": 61, "last update": "2023-10-15"}, "Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks": {"citation": 20, "last update": "2023-10-15"}, "TARe: Task-Adaptive in-situ ReRAM Computing for Graph Learning": {"citation": 10, "last update": "2023-10-15"}}
\ No newline at end of file
diff --git a/README.md b/README.md
index 19abbaa..a25c98a 100644
--- a/README.md
+++ b/README.md
@@ -34,15 +34,15 @@ A list of awesome systems for graph neural network (GNN). If you have any commen
 | :---: | :---: | :---------: | :---: | :----: |
 |arXiv 2022|Distributed Graph Neural Network Training: A Survey|BUPT| [[paper]](https://arxiv.org/abs/2211.00216)![Scholar citations](https://img.shields.io/badge/Citations-10-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |arXiv 2022|Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis|ETHZ| [[paper]](https://arxiv.org/abs/2205.09702)![Scholar citations](https://img.shields.io/badge/Citations-18-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
-|CSUR 2022|Computing Graph Neural Networks: A Survey from Algorithms to Accelerators|UPC| [[paper]](https://dl.acm.org/doi/10.1145/3477141)![Scholar citations](https://img.shields.io/badge/Citations-147-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
+|CSUR 2022|Computing Graph Neural Networks: A Survey from Algorithms to Accelerators|UPC| [[paper]](https://dl.acm.org/doi/10.1145/3477141)![Scholar citations](https://img.shields.io/badge/Citations-149-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 ### GNN Libraries
 | Venue | Title | Affiliation |       Link       |   Source   |
 | :---: | :---: | :---------: | :---: | :----: |
 |JMLR 2021|DIG: A Turnkey Library for Diving into Graph Deep Learning Research|TAMU| [[paper]](https://arxiv.org/abs/2103.12608)![Scholar citations](https://img.shields.io/badge/Citations-65-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/divelab/DIG)![GitHub stars](https://img.shields.io/github/stars/divelab/DIG.svg?logo=github&label=Stars)|
 |arXiv 2021|CogDL: A Toolkit for Deep Learning on Graphs|THU| [[paper]](https://arxiv.org/abs/2103.00959)![Scholar citations](https://img.shields.io/badge/Citations-14-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/THUDM/cogdl)![GitHub stars](https://img.shields.io/github/stars/THUDM/cogdl.svg?logo=github&label=Stars)|
-|CIM 2021|Graph Neural Networks in TensorFlow and Keras with Spektral|Università della Svizzera italiana| [[paper]](https://arxiv.org/abs/2006.12138)![Scholar citations](https://img.shields.io/badge/Citations-211-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/danielegrattarola/spektral)![GitHub stars](https://img.shields.io/github/stars/danielegrattarola/spektral.svg?logo=github&label=Stars)|
+|CIM 2021|Graph Neural Networks in TensorFlow and Keras with Spektral|Università della Svizzera italiana| [[paper]](https://arxiv.org/abs/2006.12138)![Scholar citations](https://img.shields.io/badge/Citations-216-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/danielegrattarola/spektral)![GitHub stars](https://img.shields.io/github/stars/danielegrattarola/spektral.svg?logo=github&label=Stars)|
 |arXiv 2019|Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks|AWS| [[paper]](https://arxiv.org/abs/1909.01315)![Scholar citations](https://img.shields.io/badge/Citations-825-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/dmlc/dgl)![GitHub stars](https://img.shields.io/github/stars/dmlc/dgl.svg?logo=github&label=Stars)|
-|VLDB 2019|AliGraph: A Comprehensive Graph Neural Network Platform|Alibaba| [[paper]](https://dl.acm.org/doi/pdf/10.14778/3352063.3352127)![Scholar citations](https://img.shields.io/badge/Citations-228-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/alibaba/graph-learn)![GitHub stars](https://img.shields.io/github/stars/alibaba/graph-learn.svg?logo=github&label=Stars)|
+|VLDB 2019|AliGraph: A Comprehensive Graph Neural Network Platform|Alibaba| [[paper]](https://dl.acm.org/doi/pdf/10.14778/3352063.3352127)![Scholar citations](https://img.shields.io/badge/Citations-230-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/alibaba/graph-learn)![GitHub stars](https://img.shields.io/github/stars/alibaba/graph-learn.svg?logo=github&label=Stars)|
 |arXiv 2019|Fast Graph Representation Learning with PyTorch Geometric|TU Dortmund University| [[paper]](https://arxiv.org/abs/1903.02428)![Scholar citations](https://img.shields.io/badge/Citations-3.1k-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/rusty1s/pytorch_geometric)![GitHub stars](https://img.shields.io/github/stars/rusty1s/pytorch_geometric.svg?logo=github&label=Stars)|
 |arXiv 2018|Relational Inductive Biases, Deep Learning, and Graph Networks|DeepMind| [[paper]](https://arxiv.org/abs/1806.01261)![Scholar citations](https://img.shields.io/badge/Citations-3.0k-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/deepmind/graph_nets)![GitHub stars](https://img.shields.io/github/stars/deepmind/graph_nets.svg?logo=github&label=Stars)|
 ### GNN Kernels
@@ -75,8 +75,8 @@ A list of awesome systems for graph neural network (GNN). If you have any commen
 |SC 2021|DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks|Intel| [[paper]](https://dl.acm.org/doi/pdf/10.1145/3458817.3480856)![Scholar citations](https://img.shields.io/badge/Citations-74-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/dmlc/dgl/pull/3024)|
 |SC 2021|Efficient Scaling of Dynamic Graph Neural Networks|IBM| [[paper]](https://dl.acm.org/doi/pdf/10.1145/3458817.3480858)![Scholar citations](https://img.shields.io/badge/Citations-9-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |CLUSTER 2021|2PGraph: Accelerating GNN Training over Large Graphs on GPU Clusters|NUDT| [[paper]](https://ieeexplore.ieee.org/abstract/document/9556026)![Scholar citations](https://img.shields.io/badge/Citations-11-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
-|OSDI 2021|$P^3$: Distributed Deep Graph Learning at Scale|MSR| [[paper]](https://www.usenix.org/system/files/osdi21-gandhi.pdf)![Scholar citations](https://img.shields.io/badge/Citations-82-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
-|OSDI 2021|Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads|UCLA| [[paper]](http://web.cs.ucla.edu/~harryxu/papers/dorylus-osdi21.pdf)![Scholar citations](https://img.shields.io/badge/Citations-81-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/uclasystem/dorylus)![GitHub stars](https://img.shields.io/github/stars/uclasystem/dorylus.svg?logo=github&label=Stars)|
+|OSDI 2021|$P^3$: Distributed Deep Graph Learning at Scale|MSR| [[paper]](https://www.usenix.org/system/files/osdi21-gandhi.pdf)![Scholar citations](https://img.shields.io/badge/Citations-85-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
+|OSDI 2021|Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads|UCLA| [[paper]](http://web.cs.ucla.edu/~harryxu/papers/dorylus-osdi21.pdf)![Scholar citations](https://img.shields.io/badge/Citations-82-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/uclasystem/dorylus)![GitHub stars](https://img.shields.io/github/stars/uclasystem/dorylus.svg?logo=github&label=Stars)|
 |arXiv 2021|GIST: Distributed Training for Large-Scale Graph Convolutional Networks|Rice| [[paper]](https://arxiv.org/abs/2102.10424)![Scholar citations](https://img.shields.io/badge/Citations-7-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |EuroSys 2021|FlexGraph: A Flexible and Efficient Distributed Framework for GNN Training|Alibaba| [[paper]](https://dl.acm.org/doi/pdf/10.1145/3447786.3456229)![Scholar citations](https://img.shields.io/badge/Citations-35-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |EuroSys 2021|DGCL: An Efficient Communication Library for Distributed GNN Training|CUHK| [[paper]](https://dl.acm.org/doi/abs/10.1145/3447786.3456233)![Scholar citations](https://img.shields.io/badge/Citations-46-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/czkkkkkk/ragdoll)![GitHub stars](https://img.shields.io/github/stars/czkkkkkk/ragdoll.svg?logo=github&label=Stars)|
@@ -114,9 +114,9 @@ A list of awesome systems for graph neural network (GNN). If you have any commen
 |EuroSys 2022|GNNLab: A Factored System for Sample-based GNN Training over GPUs|SJTU| [[paper]](https://dl.acm.org/doi/abs/10.1145/3492321.3519557)![Scholar citations](https://img.shields.io/badge/Citations-29-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/SJTU-IPADS/gnnlab)![GitHub stars](https://img.shields.io/github/stars/SJTU-IPADS/gnnlab.svg?logo=github&label=Stars)|
 |KDD 2021|Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs|UCLA| [[paper]](https://arxiv.org/pdf/2106.06150.pdf)![Scholar citations](https://img.shields.io/badge/Citations-24-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |PPoPP 2021|Understanding and Bridging the Gaps in Current GNN Performance Optimizations|THU| [[paper]](https://dl.acm.org/doi/pdf/10.1145/3437801.3441585)![Scholar citations](https://img.shields.io/badge/Citations-52-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/xxcclong/GNN-Computing)![GitHub stars](https://img.shields.io/github/stars/xxcclong/GNN-Computing.svg?logo=github&label=Stars)|
-|VLDB 2021|Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture|UIUC| [[paper]](https://arxiv.org/abs/2103.03330)![Scholar citations](https://img.shields.io/badge/Citations-42-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/K-Wu/pytorch-direct_dgl)![GitHub stars](https://img.shields.io/github/stars/K-Wu/pytorch-direct_dgl.svg?logo=github&label=Stars)|
+|VLDB 2021|Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture|UIUC| [[paper]](https://arxiv.org/abs/2103.03330)![Scholar citations](https://img.shields.io/badge/Citations-44-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/K-Wu/pytorch-direct_dgl)![GitHub stars](https://img.shields.io/github/stars/K-Wu/pytorch-direct_dgl.svg?logo=github&label=Stars)|
 |TPDS 2021|Efficient Data Loader for Fast Sampling-Based GNN Training on Large Graphs|USTC| [[paper]](https://gnnsys.github.io/papers/GNNSys21_paper_8.pdf)![Scholar citations](https://img.shields.io/badge/Citations-20-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/zhiqi-0/PaGraph)![GitHub stars](https://img.shields.io/github/stars/zhiqi-0/PaGraph.svg?logo=github&label=Stars)|
-|SoCC 2020|PaGraph: Scaling GNN Training on Large Graphs via Computation-aware Caching|USTC| [[paper]](https://dl.acm.org/doi/pdf/10.1145/3419111.3421281)![Scholar citations](https://img.shields.io/badge/Citations-85-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/zhiqi-0/PaGraph)![GitHub stars](https://img.shields.io/github/stars/zhiqi-0/PaGraph.svg?logo=github&label=Stars)|
+|SoCC 2020|PaGraph: Scaling GNN Training on Large Graphs via Computation-aware Caching|USTC| [[paper]](https://dl.acm.org/doi/pdf/10.1145/3419111.3421281)![Scholar citations](https://img.shields.io/badge/Citations-87-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/zhiqi-0/PaGraph)![GitHub stars](https://img.shields.io/github/stars/zhiqi-0/PaGraph.svg?logo=github&label=Stars)|
 |arXiv 2019|TigerGraph: A Native MPP Graph Database|UCSD| [[paper]](https://arxiv.org/pdf/1901.08248.pdf)![Scholar citations](https://img.shields.io/badge/Citations-53-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 ### GNN Training Accelerators
 | Venue | Title | Affiliation |       Link       |   Source   |
@@ -139,13 +139,13 @@ A list of awesome systems for graph neural network (GNN). If you have any commen
 |HPCA 2022|GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design|Rice, PNNL| [[paper]](https://arxiv.org/abs/2112.11594)![Scholar citations](https://img.shields.io/badge/Citations-22-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/RICE-EIC/GCoD)![GitHub stars](https://img.shields.io/github/stars/RICE-EIC/GCoD.svg?logo=github&label=Stars)|
 |arXiv 2022|GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration|GaTech| [[paper]](https://arxiv.org/abs/2201.08475)![Scholar citations](https://img.shields.io/badge/Citations-7-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |DAC 2021|DyGNN: Algorithm and Architecture Support of vertex Dynamic Pruning for Graph Neural Networks|Hunan University| [[paper]](https://ieeexplore.ieee.org/document/9586298)![Scholar citations](https://img.shields.io/badge/Citations-12-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
-|DAC 2021|BlockGNN: Towards Efficient GNN Acceleration Using Block-Circulant Weight Matrices|PKU| [[paper]](https://arxiv.org/abs/2104.06214)![Scholar citations](https://img.shields.io/badge/Citations-24-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
+|DAC 2021|BlockGNN: Towards Efficient GNN Acceleration Using Block-Circulant Weight Matrices|PKU| [[paper]](https://arxiv.org/abs/2104.06214)![Scholar citations](https://img.shields.io/badge/Citations-25-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |DAC 2021|TARe: Task-Adaptive in-situ ReRAM Computing for Graph Learning|Chinese Academy of Sciences| [[paper]](https://ieeexplore.ieee.org/abstract/document/9586193)![Scholar citations](https://img.shields.io/badge/Citations-10-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |ICCAD 2021|G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency|Rice| [[paper]](https://arxiv.org/abs/2109.08983)![Scholar citations](https://img.shields.io/badge/Citations-18-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |MICRO 2021|I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization|PNNL| [[paper]](https://dl.acm.org/doi/pdf/10.1145/3466752.3480113)![Scholar citations](https://img.shields.io/badge/Citations-60-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |arXiv 2021|ZIPPER: Exploiting Tile- and Operator-level Parallelism for General and Scalable Graph Neural Network Acceleration|SJTU| [[paper]](https://arxiv.org/abs/2107.08709)![Scholar citations](https://img.shields.io/badge/Citations-4-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |TComp 2021|EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks|Chinese Academy of Sciences| [[paper]](https://arxiv.org/abs/1909.00155)![Scholar citations](https://img.shields.io/badge/Citations-136-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
-|HPCA 2021|GCNAX: A Flexible and Energy-efficient Accelerator for Graph Convolutional Neural Networks|GWU| [[paper]](https://ieeexplore.ieee.org/abstract/document/9407104)![Scholar citations](https://img.shields.io/badge/Citations-89-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
+|HPCA 2021|GCNAX: A Flexible and Energy-efficient Accelerator for Graph Convolutional Neural Networks|GWU| [[paper]](https://ieeexplore.ieee.org/abstract/document/9407104)![Scholar citations](https://img.shields.io/badge/Citations-90-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |APA 2020|GNN-PIM: A Processing-in-Memory Architecture for Graph Neural Networks|PKU| [[paper]](http://115.27.240.201/docs/20200915165942122459.pdf)![Scholar citations](https://img.shields.io/badge/Citations-20-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |ASAP 2020|Hardware Acceleration of Large Scale GCN Inference|USC| [[paper]](https://ieeexplore.ieee.org/document/9153263)![Scholar citations](https://img.shields.io/badge/Citations-61-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||
 |DAC 2020|Hardware Acceleration of Graph Neural Networks|UIUC| [[paper]](http://rakeshk.web.engr.illinois.edu/dac20.pdf)![Scholar citations](https://img.shields.io/badge/Citations-102-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||