Network Analysis Resources & Updates

📃 Graph Neural Networks Designed for Different Graph Types: A Survey 🗓 Publish year: 2023 🧑‍💻Authors: Josephine Thomas, Alice Moallemy-Oureh, Silvia Beddar-Wiesing, Clara Holzhüter 🏢University: University of Kassel, Germany 📎 Study paper 📱Channel: @ComplexNetworkAnalysis #paper #GNN #Different #Types #Survey

📃A survey of clustering large probabilistic graphs: Techniques, evaluations, and applications 🗓 Publish year: 2023 📘 Journal: APSIPA Transactions on Signal and Information Processing (I.F=3.2) 🧑‍💻Authors: Malihe Danesh، Morteza Dorrigiv, Farzin Yaghmaee 🏢Universities: University of Southern California, Institute for Infocomm Research (I2R) 🔸Country: 📎 Study the paper 📲Channel: @ComplexNetworkAnalysis #paper #survey #clusterin

📃 Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities 🗓 Publish year: 2023 📘Journal: Transactions on Graph Data and Knowledge (TGDK) 🧑‍💻Authors: Jiaoyan Chen, Hang Dong, Janna Hastin, Ernesto Jiménez-Ruiz, Vanessa López, Pierre Monnin, Catia Pesquita, Petr Škoda, Valentina Tamm 🏢Universities: University of Manchester, University of Oxford, University of Zurich, Switzerland, University of London, Université Côte d’Azur, Inria, Charles University, Prague, Czechia 📎 Study paper 📱Channel: @ComplexNetworkAnalysis #paper #Knowledge_Graph #Life_Sciences #Developments #Challenges #Opportunities

📃 A Survey on Graph Neural Networks for Microservice-Based Cloud Applications 🗓 Publish year: 2022 📘Journal: SENSORS-BASEL (I.F=3.9) 🧑‍💻Authors: Hoa Xuan Nguyen , Shaoshu Zhu and Mingming Liu 🏢University: Dublin City University 📎 Study the paper 📱Channel: @ComplexNetworkAnalysis #paper #GNN #Microservice #Cloud #Applications #Survey

🎞Community detection (clustering network data) and modularity 💥Free recorded course by Prof. Samin Aref 💥Community detection (clustering network data), optimization-based community detection, Zachary Karate club, modularity function, maximum-modularity partitions, optimal partitions 📽 Watch 📲Channel: @ComplexNetworkAnalysis #video #Graph #Community_detection #modularity

📃 Emerging landscape of molecular interaction networks: Opportunities, challenges and prospects 🗓 Publish year: 2022 📘Journal: Journal of Biosciences 🧑‍💻Authors: Gauri Panditrao, Rupa Bhowmick, Chandrakala Meena and Ram Rup Sarka 🏢University: Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India 📎 Study the paper 📱Channel: @ComplexNetworkAnalysis #paper #Emerging #landscape #molecular #Opportunities #challenges #prospects

📃 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 📎 Study the paper 📲Channel: @ComplexNetworkAnalysis #paper #Survey #LLM

📃 Graph Neural Network-based EEG Classification: A Survey 🗓 Publish year: 2023 🧑‍💻Authors: Dominik Klepl, Min Wu, and Fei He 🏢University: Coventry University 📎 Study the paper 📱Channel: @ComplexNetworkAnalysis #paper #GNN #EEG #Classification #survey

📃 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 📎 Study the paper 📱Channel: @ComplexNetworkAnalysis #paper #Graph #Pre_processing #Algorithm #Hardware #Perspectives #survey

📃A Comprehensive Survey on Deep Graph Representation Learning 🗓 Publish year: 2023 📘 Journal: Journal of Artificial Intelligence Resea (I.F=5) 🧑‍💻Authors: Ijeoma Amuche Chikwendu, Xiaoling Zhang, Isaac Osei Agyemang, Isaac Adjei-Mensah 🏢Universities: University of Electronic Science and Technology of China 📎 Study the paper 📲Channel: @ComplexNetworkAnalysis #paper #Survey #GNN

📃 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 📎 Study the paper 📱Channel: @ComplexNetworkAnalysis #paper #Graph #Time_series #Deep_learning #survey

📃 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 📎 Study the paper 📱Channel: @ComplexNetworkAnalysis #paper #GNN #Acceleration #Algorithmic #Perspective #survey

📃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) 📎 Study the paper 📲Channel: @ComplexNetworkAnalysis #paper #Survey #GNN #Distributed

📃 A survey of dynamic graph neural networks 🗓 Publish year: 2024 🧑‍💻Authors: Yanping ZHENG, Lu YI, Zhewei WEI 🏢University: Renmin University of China 📎 Study the paper 📱Channel: @ComplexNetworkAnalysis #paper #dynamic #GNN #survey

🎞 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. More details can be found in the paper: Strategies for Pre-training Graph Neural Networks 📽 Watch 📲Channel: @ComplexNetworkAnalysis #video #course #Graph #GNN #Machine_Learning

📃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 📎 Study the paper 📲Channel: @ComplexNetworkAnalysis #paper #Federated_Graph_Neural_Networks #Challenges #Techniques #Overview

📃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 📎 Study the paper 📲Channel: @ComplexNetworkAnalysis #paper #Graph_Machine_Learning #LLMs

📑 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 📎 Study the paper 🔮Channel: @ComplexNetworkAnalysis #review #biology

📃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) 📎 Study the paper 📲Channel: @ComplexNetworkAnalysis #paper #Overview #Knowledge_Graph