Network Analysis Resources & Updates

📃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

📃 Knowledge Graphs and their Applications in Civil Security 🗓 Publish year: 2020 🧑‍💻Authors: Simon Ott, Daria Liakhovets, Mina Schütz, Medina Andresel, Mihai Bartha, Sven Schlarb, Alexander Schindler 🏢University: Austrian Institute of Technology GmbH Giefinggasse 4, 1210 Vienna, Austria 📎 Study the paper 📱Channel: @ComplexNetworkAnalysis #paper #Knowledge_Graph #Application #Civil_Security

📃 Knowledge Graphs and their Applications in Civil Security 🗓 Publish year: 2020 🧑‍💻Authors: Simon Ott, Daria Liakhovets, Mina Schütz, Medina Andresel, Mihai Bartha, Sven Schlarb, Alexander Schindler 🏢University: Austrian Institute of Technology GmbH Giefinggasse 4, 1210 Vienna, Austria 📎 Study the paper 📱Channel: @ComplexNetworkAnalysis #paper #Knowledge_Graph #Application #Civil_Security

📃 Explainability in Graph Neural Networks: A Taxonomic Survey 📘 Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence 🗓 Publish year: 2022 🧑‍💻Authors: Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji 🏢University: Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA 📎 Study the paper 📱Channel: @ComplexNetworkAnalysis #paper #Explainability #GNN #Taxonomic #Survey

📑 Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine 📗Journal: Briefings in Bioinformatics (I.F.= 9.5) 🗓 Publish year: 2024 🧑‍💻Authors: Peng Zhang, Dingfan Zhang, Wuai Zhou, ... 🏢University: Tsinghua University, China 📎 Study the paper 📲Channel: @Bioinformatics #review #pharmacology #network #ai #medicine

📃A Survey on Knowledge Editing of Neural Networks 🗓 Publish year: 2023 🧑‍💻Authors: Vittorio Mazzia, Alessandro Pedrani, Andrea Caciolai, Kay Rottmann, Davide Bernardi 🏢Universities: Amazon 📎 Study the paper 📲Channel: @ComplexNetworkAnalysis #paper #Survey #Knowledge #Neural_Networks

📃Graph Condensation: A Survey 🗓 Publish year: 2023 🧑‍💻Authors: Xinyi Gao, Junliang Yu, Wei Jiang, Tong Chen, Wentao Zhang, Hongzhi Yin 🏢Universities: The University of Queensland, Peking University 📎 Study the paper 📲Channel: @ComplexNetworkAnalysis #paper #Graph_Condensation #Survey

📃 A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges 🗓 Publish year: 2024 🧑‍💻Authors: Wei Ju, Siyu Yi, Yifan Wang, Zhiping Xiao, Zhengyang Mao, Hourun Li, Yiyang Gu, Yifang Qin, Nan Yin, Senzhang Wang, Xinwang Liu, Xiao Luo, Philip S. Yu, Ming Zhang 📎 Study the paper 📱Channel: @ComplexNetworkAnalysis #paper #GNN #Imbalance #Noise #Privacy #OOD_Challenges #Survey

📃SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning 🗓 Publish year: 2023 🧑‍💻Authors: Keyu Duan, Qian Liu,Tat-Seng Chua, Shuicheng Yan, Wei Tsang Ooi, Qizhe Xie, Junxian He 🏢Universities: ENational University of Singapore, The Hong Kong University of Science and Technology 📎 Study the paper 💻 Code 📲Channel: @ComplexNetworkAnalysis #paper #Graph_Learning #Textual

📃Multilayer Clustered Graph Learning 🗓 Publish year: 2020 🧑‍💻Authors: Mireille El Gheche, Pascal Frossard 🏢Universities: Ecole Polytechnique Fed´ erale de Lausanne (EPFL) 📎 Study the paper 📲Channel: @ComplexNetworkAnalysis #paper #Graph_Learning #Multilayer_graph

📕Handbook on Biological networks ✨Networks at the Cellular Level -The Structural Network Properties of Biological Systems (M Brilli & P Lió) -Dynamics of Multicellular Synthetic Gene Networks (E Ullner et al.) -Boolean Networks in Inference and Dynamic Modeling of Biological Systems at the Molecular and Physiological Level (J Thakar & R Albert) -Complexity of Boolean Dynamics in Simple Models of Signaling Networks and in Real Genetic Networks (A Díaz-Guilera & R Álvarez-Buylla) -Geometry and Topology of Folding Landscapes (L Bongini & L Casetti) -Elastic Network Models for Biomolecular Dynamics: Theory and Application to Membrane Proteins and Viruses (T R Lezon et al.) -Metabolic Networks (M C Palumbo et al.) ✨Brain Networks: -The Human Brain Network (O Sporns) -Brain Network Analysis from High-Resolution EEG Signals (F De Vico Fallani & F Babiloni) -An Optimization Approach to the Structure of the Neuronal layout of C elegans (A Arenas et al.) -Cultured Neuronal Networks Express Complex Patterns of Activity and Morphological Memory (N Raichman et al.) -Synchrony and Precise Timing in Complex Neural Networks (R-M Memmesheimer & M Timme) ✨Networks at the Individual and Population Levels: -Ideas for Moving Beyond Structure to Dynamics of Ecological Networks (D B Stouffer et al.) -Evolutionary Models for Simple Biosystems (F Bagnoli) -Evolution of Cooperation in Adaptive Social Networks (S Van Segbroeck et al.) -From Animal Collectives and Complex Networks to Decentralized Motion Control Strategies (A Buscarino et al.) -Interplay of Network State and Topology in Epidemic Dynamics (T Gross) 🌐 Read online 📲Channel: @ComplexNetworkAnalysis #Handbook #Biological