πFederated Graph Neural Networks: Overview, Techniques, and Challenges
π Publish year: 2024
π Journal: IEEE Transactions on Neural Networks and Learning Systems (I.F=14.255)
π§βπ»Authors: Rui Liu , Pengwei Xing , Zichao Deng, Anran Li , Cuntai Guan , Fellow, IEEE, and Han Yu
π’Universities: Nanyang Technological University
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Federated_Graph_Neural_Networks #Challenges #Techniques #Overview
π Publish year: 2024
π Journal: IEEE Transactions on Neural Networks and Learning Systems (I.F=14.255)
π§βπ»Authors: Rui Liu , Pengwei Xing , Zichao Deng, Anran Li , Cuntai Guan , Fellow, IEEE, and Han Yu
π’Universities: Nanyang Technological University
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Federated_Graph_Neural_Networks #Challenges #Techniques #Overview
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π Machine Learning with Graphs: Pre-Training Graph Neural Networks
π₯Free recorded course by Prof. Jure Leskovec
π₯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.
π½ Watch
πMore details can be found in the paper: Strategies for Pre-training Graph Neural Networks
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #Machine_Learning
π₯Free recorded course by Prof. Jure Leskovec
π₯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.
π½ Watch
πMore details can be found in the paper: Strategies for Pre-training Graph Neural Networks
π²Channel: @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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π A survey of dynamic graph neural networks
π Publish year: 2024
π§βπ»Authors: Yanping ZHENG, Lu YI, Zhewei WEI
π’University: Renmin University of China
π Study the paper
π±Channel: @ComplexNetworkAnalysis
#paper #dynamic #GNN #survey
π Publish year: 2024
π§βπ»Authors: Yanping ZHENG, Lu YI, Zhewei WEI
π’University: Renmin University of China
π Study the paper
π±Channel: @ComplexNetworkAnalysis
#paper #dynamic #GNN #survey
π5
πDistributed Graph Neural Network Training: A Survey
π Publish year: 2024
π Journal: ACM Computing Surveys (I.F=16.6)
π§βπ»Authors:thors: Yingxia Shao, Hongzheng Li, Xizhi Gu, Hongbo Yin, Yawen Li, Xupeng Miao, Wentao Zhang, Bin Cui, Lei Chen
π’Universities: Beijing University of Posts and Telecommunications, Carnegie Mellon University, Peking University, The Hong Kong University of Science and Technology (Guangzhou)
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Survey #GNN #Distributed
π Publish year: 2024
π Journal: ACM Computing Surveys (I.F=16.6)
π§βπ»Authors:thors: Yingxia Shao, Hongzheng Li, Xizhi Gu, Hongbo Yin, Yawen Li, Xupeng Miao, Wentao Zhang, Bin Cui, Lei Chen
π’Universities: Beijing University of Posts and Telecommunications, Carnegie Mellon University, Peking University, The Hong Kong University of Science and Technology (Guangzhou)
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Survey #GNN #Distributed
π3β€1π₯1
A Survey on Graph Representation Learning Methods.pdf
1.2 MB
πA Survey on Graph Representation Learning Methods
π Publish year: 2024
π Journal: ACM Transactions on Intelligent Systems and Technology (I.F=10.489)
π§βπ»Authors: Shima Khoshraftar, Aijun An
π’Universities: York University
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Survey #GNN
π Publish year: 2024
π Journal: ACM Transactions on Intelligent Systems and Technology (I.F=10.489)
π§βπ»Authors: Shima Khoshraftar, Aijun An
π’Universities: York University
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Survey #GNN
π4π₯1
π Survey on Graph Neural Network Acceleration: An Algorithmic Perspective
π Publish year: 2022
πConference: International Joint Conference on Artificial Intelligence
π§βπ»Authors: Xin Liu, Mingyu Yan, Lei Deng, Guoqi Li, Xiaochun Ye,Dongrui Fan, Shirui Pan, Yuan Xie
π’Universities: University of Chinese Academy of Sciences,Tsinghua University, Monash University, University of California
π Study the paper
π±Channel: @ComplexNetworkAnalysis
#paper #GNN #Acceleration #Algorithmic #Perspective #survey
π Publish year: 2022
πConference: International Joint Conference on Artificial Intelligence
π§βπ»Authors: Xin Liu, Mingyu Yan, Lei Deng, Guoqi Li, Xiaochun Ye,Dongrui Fan, Shirui Pan, Yuan Xie
π’Universities: University of Chinese Academy of Sciences,Tsinghua University, Monash University, University of California
π Study the paper
π±Channel: @ComplexNetworkAnalysis
#paper #GNN #Acceleration #Algorithmic #Perspective #survey
π3β€1π1
π Graph Time-series Modeling in Deep Learning: A Survey
π Publish year: 2024
πJournal: ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA (I.F=3.6)
π§βπ»Authors: Hongjie Che, Hoda Eldardiry
π’University: Virginia Tech, USA
π Study the paper
π±Channel: @ComplexNetworkAnalysis
#paper #Graph #Time_series #Deep_learning #survey
π Publish year: 2024
πJournal: ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA (I.F=3.6)
π§βπ»Authors: Hongjie Che, Hoda Eldardiry
π’University: Virginia Tech, USA
π Study the paper
π±Channel: @ComplexNetworkAnalysis
#paper #Graph #Time_series #Deep_learning #survey
π3
πA Comprehensive Survey on Deep Graph Representation Learning
π Publish year: 2023
π Journal: Journal of Artificial Intelligence Research (I.F=5)
π§βπ»Authors: Ijeoma Amuche Chikwendu, Xiaoling Zhang, Isaac Osei Agyemang, Isaac Adjei-Mensah
π’Universities: University of Electronic Science and Technology of China
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Survey #GNN
π Publish year: 2023
π Journal: Journal of Artificial Intelligence Research (I.F=5)
π§βπ»Authors: Ijeoma Amuche Chikwendu, Xiaoling Zhang, Isaac Osei Agyemang, Isaac Adjei-Mensah
π’Universities: University of Electronic Science and Technology of China
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Survey #GNN
π2β€1
π A Survey of Graph Pre-processing Methods: From Algorithmic to Hardware Perspectives
π Publish year: 2023
π§βπ»Authors: Zhengyang Lv, Mingyu Yan, Xin Liu, Mengyao Dong, Xiaochun Ye, Dongrui Fan, Ninghui Sun
π’University: ShanghaiTech Univ, China and SHIC, China
π Study the paper
π±Channel: @ComplexNetworkAnalysis
#paper #Graph #Pre_processing #Algorithm #Hardware #Perspectives #survey
π Publish year: 2023
π§βπ»Authors: Zhengyang Lv, Mingyu Yan, Xin Liu, Mengyao Dong, Xiaochun Ye, Dongrui Fan, Ninghui Sun
π’University: ShanghaiTech Univ, China and SHIC, China
π Study the paper
π±Channel: @ComplexNetworkAnalysis
#paper #Graph #Pre_processing #Algorithm #Hardware #Perspectives #survey
π3
π Graph Neural Network-based EEG Classification: A Survey
π Publish year: 2023
π§βπ»Authors: Dominik Klepl, Min Wu, and Fei He
π’University: Coventry University
π Study the paper
π±Channel: @ComplexNetworkAnalysis
#paper #GNN #EEG #Classification #survey
π Publish year: 2023
π§βπ»Authors: Dominik Klepl, Min Wu, and Fei He
π’University: Coventry University
π Study the paper
π±Channel: @ComplexNetworkAnalysis
#paper #GNN #EEG #Classification #survey
π4β€1
π A Survey of Large Language Models for Graphs
π Publish year: 2024
π§βπ»Authors: Xubin Ren, Jiabin Tang, Dawei Yin, Nitesh Chawla, Chao Huang
π’University: University of Hong Kong
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Survey #LLM
π Publish year: 2024
π§βπ»Authors: Xubin Ren, Jiabin Tang, Dawei Yin, Nitesh Chawla, Chao Huang
π’University: University of Hong Kong
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Survey #LLM
π Emerging landscape of molecular interaction networks:
Opportunities, challenges and prospects
π Publish year: 2022
πJournal: Journal of Biosciences(I.F=2.9)
π§βπ»Authors: Gauri Panditrao, Rupa Bhowmick, Chandrakala Meena and Ram Rup Sarka
π’University: Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
π Study the paper
π±Channel: @ComplexNetworkAnalysis
#paper #Emerging #landscape #molecular #Opportunities #challenges #prospects
Opportunities, challenges and prospects
π Publish year: 2022
πJournal: Journal of Biosciences(I.F=2.9)
π§βπ»Authors: Gauri Panditrao, Rupa Bhowmick, Chandrakala Meena and Ram Rup Sarka
π’University: Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
π Study the paper
π±Channel: @ComplexNetworkAnalysis
#paper #Emerging #landscape #molecular #Opportunities #challenges #prospects
π1
πCommunity detection (clustering network data) and modularity
π₯Free recorded course by Prof. Samin Aref
π₯Community detection (clustering network data), optimization-based community detection, Zachary Karate club, modularity function, maximum-modularity partitions, optimal partitions
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #Graph #Community_detection #modularity
π₯Free recorded course by Prof. Samin Aref
π₯Community detection (clustering network data), optimization-based community detection, Zachary Karate club, modularity function, maximum-modularity partitions, optimal partitions
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #Graph #Community_detection #modularity
YouTube
Lec 34: Community detection (clustering network data) and modularity
Data Science Methods and Statistical Learning, University of Toronto
Prof. Samin Aref
Community detection (clustering network data), optimization-based community detection, Zachary Karate club, modularity function, maximum-modularity partitions, optimalβ¦
Prof. Samin Aref
Community detection (clustering network data), optimization-based community detection, Zachary Karate club, modularity function, maximum-modularity partitions, optimalβ¦
π₯3
π A Survey on Graph Neural Networks for Microservice-Based Cloud Applications
π Publish year: 2022
πJournal: SENSORS-BASEL (I.F=3.9)
π§βπ»Authors: Hoa Xuan Nguyen , Shaoshu Zhu and Mingming Liu
π’University: Dublin City University
π Study the paper
π±Channel: @ComplexNetworkAnalysis
#paper #GNN #Microservice #Cloud #Applications #Survey
π Publish year: 2022
πJournal: SENSORS-BASEL (I.F=3.9)
π§βπ»Authors: Hoa Xuan Nguyen , Shaoshu Zhu and Mingming Liu
π’University: Dublin City University
π Study the paper
π±Channel: @ComplexNetworkAnalysis
#paper #GNN #Microservice #Cloud #Applications #Survey
π Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities
π Publish year: 2023
πJournal: Transactions on Graph Data and Knowledge (TGDK)
π§βπ»Authors: Jiaoyan Chen, Hang Dong, Janna Hastin, Ernesto JimΓ©nez-Ruiz, Vanessa LΓ³pez, Pierre Monnin, Catia Pesquita, Petr Ε koda, Valentina Tamm
π’Universities: University of Manchester, University of Oxford, University of Zurich, Switzerland, University of London, UniversitΓ© CΓ΄te dβAzur, Inria, Charles University, Prague, Czechia
π Study paper
π±Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graph #Life_Sciences #Developments #Challenges #Opportunities
π Publish year: 2023
πJournal: Transactions on Graph Data and Knowledge (TGDK)
π§βπ»Authors: Jiaoyan Chen, Hang Dong, Janna Hastin, Ernesto JimΓ©nez-Ruiz, Vanessa LΓ³pez, Pierre Monnin, Catia Pesquita, Petr Ε koda, Valentina Tamm
π’Universities: University of Manchester, University of Oxford, University of Zurich, Switzerland, University of London, UniversitΓ© CΓ΄te dβAzur, Inria, Charles University, Prague, Czechia
π Study paper
π±Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graph #Life_Sciences #Developments #Challenges #Opportunities
π5