Network Analysis Resources & Updates
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🎞 Introduction to Network Analysis Methodologies and Tools
💥Are you interested in the connections between people? Places? Concepts? Things? Do you want to visualize data in a way that is comprehensible to a human? Do you want to see how information flows through systems? Then network analysis is for you! This introductory workshop will introduce participants to network analysis, with an emphasis on its use for data-driven humanities research.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Methodologies #Tools
📄 Network controllability of structured networks: a survey on graph-theoretical approaches
🗓 Publish year: 2024
📓 Journal: SICE Journal of Control, Measurement, and System Integration
🧑💻Authors: Nam-Jin Park & Hyo-Sung Ahn
🏢Universities: Gwangju Institute of Science and Technology (GIST), Korea
📎 Study paper
⚡️Channel: @ComplexNetworkAnalysis
#review #control
📃Leading by the nodes: a survey of film industry network analysis and datasets
🗓 Publish year: 2024
📘Journal: Applied Network Science (I.F=1.3)
🧑💻Authors: Aresh Dadlani, Vi Vo, Ayushi Khemka, Sophie Talalay Harvey, Aigul Kantoro Kyzy, Pete Jones & Deb Verhoeven
🏢Universities: Department of Mathematics and Computing, Mount Royal University, Calgary, Alberta, Canada.
Faculty of Arts, University of Alberta, Edmonton, Alberta, Canada.
📎 Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #nodes #film #industry #survey
📃Distributed Graph Neural Network Training: A Survey
🗓 Publish year: 2023
🧑💻Authors: YINGXIA SHAO، HONGZHENG LI, HONGBO YIN, XIZHI GU، WENTAO ZHANG,...
🏢Universities: Beijing University of Posts and Telecommunications, Carnegie Mellon University, The Hong Kong University of Science and Technology (Guangzhou), Peking University.
📎 Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Distributed #Survey
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📄 A Comprehensive Survey on Knowledge-Defined Networking
🗓 Publish year: 2024
📔Journal: Telecom (I.F=2.1)
🧑💻Authors: Patikiri Arachchige Don Shehan Nilmantha Wijesekara & Subodha Gunawardena
🏢Universities: University of Ruhuna, Sri Lanka
📎 Study paper
⚡️Channel: @ComplexNetworkAnalysis
#review #knowledge
📃Can social network analysis contribute to supply chain
management? A systematic literature review and
bibliometric analysis
🗓 Publish year: 2024
📘Journal: Heliyon (I.F=3.4)
🧑💻Authors: Hesham Fouad, Nazar´e Rego
🏢Universities: Arab Academy for Science, Technology and Maritime Transport, Aswan, Egypt.
School of Economics and Management, University of Minho, Braga, Portugal.
NIPE, School of Economics and Management, University of Minho, Braga, Portugal.
📎 Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #contribute #supply #managemen #bibliometric
📑 Application of graph theory in liver research: A review
🗓 Publish year: 2024
📕Journal: Portal Hypertension & Cirrhosis
🧑💻Authors: Xumei Hu, Longyu Sun, Rencheng Zheng, ...
🏢Universities: Fudan University, China
📎 Study paper
⚡️Channel: @ComplexNetworkAnalysis
#review #liver #graph
📃Public Health Using Social Network Analysis During the COVID-19 Era: A Systematic Review
🗓 Publish year: 2024
📘Journal: Information (I.F=2.4)
🧑💻Authors: Stanislava Gardasevic، Aditi Jaiswal، Manika Lamba، Jena Funakoshi، Kar-Hai Chu، Aekta Shah، Yinan Sun، Pallav Pokhrel، Peter Washington
🏢Universities: University of Hawai’i at Mānoa, University of Hawai’i at Mānoa, University of Oklahoma, University of Hawai’i at Mānoa, University of Pittsburgh, Unaffiliated Researcher, University of Hawai’i Cancer Center
📎 Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Health #COVID_19 #review
📄 Network Analyse in R and Python
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#Network #Analyses #python #code #R
📃A Comprehensive Survey on Automatic Knowledge Graph Construction
🗓 Publish year: 2023
🧑💻Authors: LINGFENG ZHONG, JIA WU, QIAN LI, HAO PENG,
🏢Universities: Macquarie University, Beihang University, Hefei University of Technology.
📎 Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #KnowledgeGraph #Survey
📃Introducing New Node Prediction in Graph Mining:
Predicting All Links from Isolated Nodes with Graph
Neural Networks
🗓 Publish year: 2024
🧑💻Authors: Damiano Zanardinia, Emilio Serrano
🏢Universities: Departamento de Inteligencia Artificial, ETSI Inform´aticos, Universidad Polit´ecnica de Madrid, 28660 Boadilla del Monte, Madrid, Spain
📎 Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #GNN #Node #prediction #Isolated
📃Self-Supervised Learning for Graph-Structured Data in Healthcare Applications: A Comprehensive Review
🗓 Publish year: 2024
🧑💻Authors: Safa Ben Atitallah, Chaima Ben Rabah, Maha Driss, ...
🏢Universities: Prince Sultan University, Saudi Arabia - University of Manouba, Manouba , Tunisia
📎 Study paper
⚡️Channel: @ComplexNetworkAnalysis
#review #self_supervised #gnn
📃Current and future directions in network biology
🗓 Publish year: 2023
📘Journal: Bioinformatics Advances (I.F=2.4)
🧑💻Authors: Marinka Zitnik, Michelle M. Li, Aydin Wells, Kimberly Glass, ...
📎 Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Biology #Direction
📃Machine Learning for Refining Knowledge Graphs: A Survey
🗓 Publish year: 2024
🧑💻Authors: BUDHITAMA SUBAGDJA, D. SHANTHOSHIGAA, ZHAOXIA WANG, and AH-HWEE TAN
🏢Universities: Singapore Management University
📎 Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #ML #Survey
🎓Ensemble approaches for Link Prediction
📕MSc thesis from The University in Stuttgart, Germany
🗓Publish year: 2024
📎 Study thesis
⚡️Channel: @ComplexNetworkAnalysis
#thesis #link_prediction
🎞 Graph Neural Networks for Temporal Graphs: State of the Art, Open Challenges, and Opportunities
💥Temporal Graph Learning Reading Group
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #GNN #Temporal #Challenges #Opportunities
📄3D Clustering with Graph Theory: The Complete Guide with Python
💥 Technical paper
📎 Study paper
⚡️Channel: @ComplexNetworkAnalysis
#clustering #python
🎞 An Introduction to Graph Neural Networks: Models and Applications
💥Free Recorded Lecture on Applications of Graph Neural Networks.
🔹Graph Neural Networks (GNN) are a general class of networks that work over graphs. By representing a problem as a graph — encoding the information of individual elements as nodes and their relationships as edges — GNNs learn to capture patterns within the graph. These networks have been successfully used in applications such as chemistry and program analysis. In this introductory talk, I will do a deep dive in the neural message-passing GNNs, and show how to create a simple GNN implementation.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #GNN #Application
