📃Knowledge Graph Embedding: An Overview
🗓 Publish year: 2024
📘 Journal: APSIPA Transactions on Signal and Information Processing (I.F=3.2)
🧑💻Authors: Xiou Ge, Yun Cheng Wang, Bin Wang, C.-C. Jay Kuo
🏢Universities: University of Southern California, Institute for Infocomm Research (I2R)
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📲Channel: @ComplexNetworkAnalysis
#paper #Overview #Knowledge_Graph
🗓 Publish year: 2024
📘 Journal: APSIPA Transactions on Signal and Information Processing (I.F=3.2)
🧑💻Authors: Xiou Ge, Yun Cheng Wang, Bin Wang, C.-C. Jay Kuo
🏢Universities: University of Southern California, Institute for Infocomm Research (I2R)
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📲Channel: @ComplexNetworkAnalysis
#paper #Overview #Knowledge_Graph
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📑 A Survey of Analytical Methods for Biological Network Analysis: Exploring the Molecular Terrain
🗓 Publish year: 2024
📘 Journal: Symmetry (I.F=2.7)
🧑💻Authors: Trong-The Nguyen, Thi-Kien Dao, Duc-Tinh Pham, Thi-Hoan Duong
🏢Universities: Fujian University of Technology, China - University of Information Technology and Hanoi University of Industry, Vietnam
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🔮Channel: @ComplexNetworkAnalysis
#review #biology
🗓 Publish year: 2024
📘 Journal: Symmetry (I.F=2.7)
🧑💻Authors: Trong-The Nguyen, Thi-Kien Dao, Duc-Tinh Pham, Thi-Hoan Duong
🏢Universities: Fujian University of Technology, China - University of Information Technology and Hanoi University of Industry, Vietnam
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🔮Channel: @ComplexNetworkAnalysis
#review #biology
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📃Graph Machine Learning in the Era of Large Language Models (LLMs)
🗓 Publish year: 2023
🧑💻Authors: Wenqi Fan, Shijie Wang, Jiani Huang, Zhikai Chen, Yu Song, Wenzhuo Tang, Haitao Mao, Hui Liu, Xiaorui Liu, Dawei Yin, Qing Li
🏢Universities: The Hong Kong Polytechnic University,Michigan State University, North Carolina State University
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📲Channel: @ComplexNetworkAnalysis
#paper #Graph_Machine_Learning #LLMs
🗓 Publish year: 2023
🧑💻Authors: Wenqi Fan, Shijie Wang, Jiani Huang, Zhikai Chen, Yu Song, Wenzhuo Tang, Haitao Mao, Hui Liu, Xiaorui Liu, Dawei Yin, Qing Li
🏢Universities: The Hong Kong Polytechnic University,Michigan State University, North Carolina State University
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📲Channel: @ComplexNetworkAnalysis
#paper #Graph_Machine_Learning #LLMs
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📃Federated Graph Neural Networks: Overview, Techniques, and Challenges
🗓 Publish year: 2024
📘 Journal: IEEE Transactions on Neural Networks and Learning Systems (I.F=14.255)
🧑💻Authors: Rui Liu , Pengwei Xing , Zichao Deng, Anran Li , Cuntai Guan , Fellow, IEEE, and Han Yu
🏢Universities: Nanyang Technological University
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📲Channel: @ComplexNetworkAnalysis
#paper #Federated_Graph_Neural_Networks #Challenges #Techniques #Overview
🗓 Publish year: 2024
📘 Journal: IEEE Transactions on Neural Networks and Learning Systems (I.F=14.255)
🧑💻Authors: Rui Liu , Pengwei Xing , Zichao Deng, Anran Li , Cuntai Guan , Fellow, IEEE, and Han Yu
🏢Universities: Nanyang Technological University
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📲Channel: @ComplexNetworkAnalysis
#paper #Federated_Graph_Neural_Networks #Challenges #Techniques #Overview
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🎞 Machine Learning with Graphs: Pre-Training Graph Neural Networks
💥Free recorded course by Prof. Jure Leskovec
💥There are two challenges in applying GNNs to scientific domains: scarcity of labeled data and out-of-distribution prediction. In this video we discuss methods for pre-training GNNs to resolve these challenges. The key idea is to pre-train both node and graph embeddings, which leads to significant performance gains on downstream tasks.
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📑More details can be found in the paper: Strategies for Pre-training Graph Neural Networks
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #Machine_Learning
💥Free recorded course by Prof. Jure Leskovec
💥There are two challenges in applying GNNs to scientific domains: scarcity of labeled data and out-of-distribution prediction. In this video we discuss methods for pre-training GNNs to resolve these challenges. The key idea is to pre-train both node and graph embeddings, which leads to significant performance gains on downstream tasks.
📽 Watch
📑More details can be found in the paper: Strategies for Pre-training Graph Neural Networks
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #Machine_Learning
arXiv.org
Strategies for Pre-training Graph Neural Networks
Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce...
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📃 A survey of dynamic graph neural networks
🗓 Publish year: 2024
🧑💻Authors: Yanping ZHENG, Lu YI, Zhewei WEI
🏢University: Renmin University of China
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📱Channel: @ComplexNetworkAnalysis
#paper #dynamic #GNN #survey
🗓 Publish year: 2024
🧑💻Authors: Yanping ZHENG, Lu YI, Zhewei WEI
🏢University: Renmin University of China
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#paper #dynamic #GNN #survey
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📃Distributed Graph Neural Network Training: A Survey
🗓 Publish year: 2024
📘 Journal: ACM Computing Surveys (I.F=16.6)
🧑💻Authors:thors: Yingxia Shao, Hongzheng Li, Xizhi Gu, Hongbo Yin, Yawen Li, Xupeng Miao, Wentao Zhang, Bin Cui, Lei Chen
🏢Universities: Beijing University of Posts and Telecommunications, Carnegie Mellon University, Peking University, The Hong Kong University of Science and Technology (Guangzhou)
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📲Channel: @ComplexNetworkAnalysis
#paper #Survey #GNN #Distributed
🗓 Publish year: 2024
📘 Journal: ACM Computing Surveys (I.F=16.6)
🧑💻Authors:thors: Yingxia Shao, Hongzheng Li, Xizhi Gu, Hongbo Yin, Yawen Li, Xupeng Miao, Wentao Zhang, Bin Cui, Lei Chen
🏢Universities: Beijing University of Posts and Telecommunications, Carnegie Mellon University, Peking University, The Hong Kong University of Science and Technology (Guangzhou)
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📲Channel: @ComplexNetworkAnalysis
#paper #Survey #GNN #Distributed
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A Survey on Graph Representation Learning Methods.pdf
1.2 MB
📃A Survey on Graph Representation Learning Methods
🗓 Publish year: 2024
📘 Journal: ACM Transactions on Intelligent Systems and Technology (I.F=10.489)
🧑💻Authors: Shima Khoshraftar, Aijun An
🏢Universities: York University
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📲Channel: @ComplexNetworkAnalysis
#paper #Survey #GNN
🗓 Publish year: 2024
📘 Journal: ACM Transactions on Intelligent Systems and Technology (I.F=10.489)
🧑💻Authors: Shima Khoshraftar, Aijun An
🏢Universities: York University
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📲Channel: @ComplexNetworkAnalysis
#paper #Survey #GNN
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📃 Survey on Graph Neural Network Acceleration: An Algorithmic Perspective
🗓 Publish year: 2022
📘Conference: International Joint Conference on Artificial Intelligence
🧑💻Authors: Xin Liu, Mingyu Yan, Lei Deng, Guoqi Li, Xiaochun Ye,Dongrui Fan, Shirui Pan, Yuan Xie
🏢Universities: University of Chinese Academy of Sciences,Tsinghua University, Monash University, University of California
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📱Channel: @ComplexNetworkAnalysis
#paper #GNN #Acceleration #Algorithmic #Perspective #survey
🗓 Publish year: 2022
📘Conference: International Joint Conference on Artificial Intelligence
🧑💻Authors: Xin Liu, Mingyu Yan, Lei Deng, Guoqi Li, Xiaochun Ye,Dongrui Fan, Shirui Pan, Yuan Xie
🏢Universities: University of Chinese Academy of Sciences,Tsinghua University, Monash University, University of California
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📱Channel: @ComplexNetworkAnalysis
#paper #GNN #Acceleration #Algorithmic #Perspective #survey
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📃 Graph Time-series Modeling in Deep Learning: A Survey
🗓 Publish year: 2024
📘Journal: ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA (I.F=3.6)
🧑💻Authors: Hongjie Che, Hoda Eldardiry
🏢University: Virginia Tech, USA
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📱Channel: @ComplexNetworkAnalysis
#paper #Graph #Time_series #Deep_learning #survey
🗓 Publish year: 2024
📘Journal: ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA (I.F=3.6)
🧑💻Authors: Hongjie Che, Hoda Eldardiry
🏢University: Virginia Tech, USA
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📱Channel: @ComplexNetworkAnalysis
#paper #Graph #Time_series #Deep_learning #survey
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📃A Comprehensive Survey on Deep Graph Representation Learning
🗓 Publish year: 2023
📘 Journal: Journal of Artificial Intelligence Research (I.F=5)
🧑💻Authors: Ijeoma Amuche Chikwendu, Xiaoling Zhang, Isaac Osei Agyemang, Isaac Adjei-Mensah
🏢Universities: University of Electronic Science and Technology of China
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#paper #Survey #GNN
🗓 Publish year: 2023
📘 Journal: Journal of Artificial Intelligence Research (I.F=5)
🧑💻Authors: Ijeoma Amuche Chikwendu, Xiaoling Zhang, Isaac Osei Agyemang, Isaac Adjei-Mensah
🏢Universities: University of Electronic Science and Technology of China
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#paper #Survey #GNN
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