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📑 A Survey of Analytical Methods for Biological Network Analysis: Exploring the Molecular Terrain

🗓 Publish year: 2024
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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
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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
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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
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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
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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
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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
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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
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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
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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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📲Channel: @ComplexNetworkAnalysis
#paper #Survey #GNN
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📃 A Survey of Graph Pre-processing Methods: From Algorithmic to Hardware Perspectives

🗓 Publish year: 2023

🧑‍💻Authors: Zhengyang Lv, Mingyu Yan, Xin Liu, Mengyao Dong, Xiaochun Ye, Dongrui Fan, Ninghui Sun
🏢University: ShanghaiTech Univ, China and SHIC, China

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📱Channel: @ComplexNetworkAnalysis
#paper #Graph #Pre_processing #Algorithm #Hardware #Perspectives #survey
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📃 Graph Neural Network-based EEG Classification: A Survey

🗓 Publish year: 2023

🧑‍💻Authors: Dominik Klepl, Min Wu, and Fei He
🏢University: Coventry University

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📱Channel: @ComplexNetworkAnalysis
#paper #GNN #EEG #Classification #survey
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📃 A Survey of Large Language Models for Graphs

🗓 Publish year: 2024

🧑‍💻Authors: Xubin Ren, Jiabin Tang, Dawei Yin, Nitesh Chawla, Chao Huang
🏢University: University of Hong Kong

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📲Channel: @ComplexNetworkAnalysis
#paper #Survey #LLM