πExploring the use of social network analysis methods in process improvement within healthcare organizations: a scoping review
π Publish year: 2024
πJournal: BMC Health Services Research (I.F=2.7)
π§βπ»Authors: Troy FrancisΨ Morgan DavidsonΨ Laura SeneseΨ Lianne JeffsΨ Reza Yousefi-NooraieΨ Mathieu OuimetΨ Valeria RacΨ Patricia Trbovich
π’Universities: University of Toronto, Toronto, Canada.
North York General Hospital, North York, Canada.
University Health Network, Toronto, ON, Canada.
University of Rochester, New York, USA.
π Study paper
π±Channel: @ComplexNetworkAnalysis
#paper #healthcare #organizations #review
π Publish year: 2024
πJournal: BMC Health Services Research (I.F=2.7)
π§βπ»Authors: Troy FrancisΨ Morgan DavidsonΨ Laura SeneseΨ Lianne JeffsΨ Reza Yousefi-NooraieΨ Mathieu OuimetΨ Valeria RacΨ Patricia Trbovich
π’Universities: University of Toronto, Toronto, Canada.
North York General Hospital, North York, Canada.
University Health Network, Toronto, ON, Canada.
University of Rochester, New York, USA.
π Study paper
π±Channel: @ComplexNetworkAnalysis
#paper #healthcare #organizations #review
πCan Graph Neural Networks be Adequately Explained? A Survey
π Journal: ACM Computing Surveys (π₯I.F.=23.8)
π Publish year: 2025
π§βπ»Authors: Xuyan Li, Jie Wang, Zheng Yan
π’University: Xidian University, China
π Study the paper
β‘οΈChannel: @ComplexNetworkAnalysis
#review #gnn #explainability
π Journal: ACM Computing Surveys (π₯I.F.=23.8)
π Publish year: 2025
π§βπ»Authors: Xuyan Li, Jie Wang, Zheng Yan
π’University: Xidian University, China
π Study the paper
β‘οΈChannel: @ComplexNetworkAnalysis
#review #gnn #explainability
π Machine Learning with Graphs: hyperbolic graph embeddings
π₯Free recorded course by Prof. Jure Leskovec
π₯ This part focused on graph representation learning in Euclidean embedding spaces. In this lecture, we introduce hyperbolic embedding spaces, which are great for modeling hierarchical, tree-like graphs. Moreover, we introduce basics for hyperbolic geometry models, which leads to the idea of hyperbolic GNNs. More details can be found in the paper: Hyperbolic Graph Convolutional Neural Networks
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #Machine_Learning
π₯Free recorded course by Prof. Jure Leskovec
π₯ This part focused on graph representation learning in Euclidean embedding spaces. In this lecture, we introduce hyperbolic embedding spaces, which are great for modeling hierarchical, tree-like graphs. Moreover, we introduce basics for hyperbolic geometry models, which leads to the idea of hyperbolic GNNs. More details can be found in the paper: Hyperbolic Graph Convolutional Neural Networks
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #Machine_Learning
YouTube
Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 19.2 - Hyperbolic Graph Embeddings
For more information about Stanfordβs Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3Brc7vN
Jure Leskovec
Computer Science, PhD
In previous lectures, we focused on graph representation learning in Euclidean embeddingβ¦
Jure Leskovec
Computer Science, PhD
In previous lectures, we focused on graph representation learning in Euclidean embeddingβ¦
π2
π Intro to Graph Analytics in Python
π₯ Watch
β‘οΈChannel: @ComplexNetworkAnalysis
#video #graph #python
π₯ Watch
β‘οΈChannel: @ComplexNetworkAnalysis
#video #graph #python
YouTube
Intro to Graph Analytics in Python
Graphs are a way to represent a network or a collection of interconnected objects formally. There are many powerful tools out there to explore that kind of network by applying graph algorithms. But sometimes itβs hard to keep track of them!
We have createdβ¦
We have createdβ¦
π₯1π1
Forwarded from Bioinformatics
π Enhancing Molecular Network-Based Cancer Driver Gene Prediction Using Machine Learning Approaches: Current Challenges and Opportunities
π Journal: Journal of Cellular and Molecular Medicine (I.F.=4.3)
πPublish year: 2025
π§βπ»Authors: Hao Zhang, Chaohuan Lin, Ying'ao Chen, ...
π’Universities: Wenzhou Medical University - University of Chinese Academy of Sciences, China
π Study the paper
π²Channel: @Bioinformatics
#review #cancer #network #driver_gene #machine_learning
π Journal: Journal of Cellular and Molecular Medicine (I.F.=4.3)
πPublish year: 2025
π§βπ»Authors: Hao Zhang, Chaohuan Lin, Ying'ao Chen, ...
π’Universities: Wenzhou Medical University - University of Chinese Academy of Sciences, China
π Study the paper
π²Channel: @Bioinformatics
#review #cancer #network #driver_gene #machine_learning
πUnderstanding When and Why Graph Attention Mechanisms Work via Node Classification
πPublish year: 2024
π§βπ»Authors: Didier A. Vega-Oliveros, Alneu de Andrade Lopes, Lilian Berton
π’University: Northwestern Polytechnical University, Shanghai Artificial Intelligence Laboratory, Shanghai Jiaotong University
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#Paper #GAT #Attention #node_classification
πPublish year: 2024
π§βπ»Authors: Didier A. Vega-Oliveros, Alneu de Andrade Lopes, Lilian Berton
π’University: Northwestern Polytechnical University, Shanghai Artificial Intelligence Laboratory, Shanghai Jiaotong University
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#Paper #GAT #Attention #node_classification
πA comprehensive survey on graph neural network accelerators
π Publish year: 2025
πJournal: Frontiers of Computer Science (I.F=3.4)
π§βπ»Authors: Jingyu LIU, Shi CHEN, Li SHEN
π’Universities: School of Computer, National University of Defense Technology, Changsha 410073, China
π Study paper
π±Channel: @ComplexNetworkAnalysis
#paper #GNN #survey
π Publish year: 2025
πJournal: Frontiers of Computer Science (I.F=3.4)
π§βπ»Authors: Jingyu LIU, Shi CHEN, Li SHEN
π’Universities: School of Computer, National University of Defense Technology, Changsha 410073, China
π Study paper
π±Channel: @ComplexNetworkAnalysis
#paper #GNN #survey
π A comprehensive bibliometric analysis on social network anonymization: current approaches and future directions
π Journal: Knowledge and Information System (I.F.=2.5)
π Publish year: 2025
π§βπ»Authors: Navid Yazdanjue, Hossein Yazdanjouei, Hassan Gharoun,...
π’University:
- University of Technology Sydney, Ultimo, Australia
- Urmia University, Urmia &Iran University of Science and Technology, Iran
π Study the paper
β‘οΈChannel: @ComplexNetworkAnalysis
#review #anonymization
π Journal: Knowledge and Information System (I.F.=2.5)
π Publish year: 2025
π§βπ»Authors: Navid Yazdanjue, Hossein Yazdanjouei, Hassan Gharoun,...
π’University:
- University of Technology Sydney, Ultimo, Australia
- Urmia University, Urmia &Iran University of Science and Technology, Iran
π Study the paper
β‘οΈChannel: @ComplexNetworkAnalysis
#review #anonymization
π1
πA Survey on Graph Neural Networks and its Applications in Various Domains
πPublish year: 2025
π§βπ»Authors: Tejaswini R. Murgod, P. Srihith Reddy, Shamitha Gaddam, S. Meenakshi Sundaram & C. Anitha
π’University: BNM Institute of Technology, NITTE Meenakshi Institute of Technology,
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#Paper #Survey #GNN #Application
πPublish year: 2025
π§βπ»Authors: Tejaswini R. Murgod, P. Srihith Reddy, Shamitha Gaddam, S. Meenakshi Sundaram & C. Anitha
π’University: BNM Institute of Technology, NITTE Meenakshi Institute of Technology,
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#Paper #Survey #GNN #Application
π1
π Systematic Review of Fake News, Propaganda, and Disinformation: Examining Authors, Content, and Social Impact through Machine Learning
π Journal: IEEE ACCESS (I.F.=3.4)
π Publish year: 2025
π§βπ»Authors: D. Plikynas, I. RizgelienΔ, G. Korvel,...
π’University: Vilnius university, Vilnius, Lithuania
π Study the paper
β‘οΈChannel: @ComplexNetworkAnalysis
#review #fake_news
π Journal: IEEE ACCESS (I.F.=3.4)
π Publish year: 2025
π§βπ»Authors: D. Plikynas, I. RizgelienΔ, G. Korvel,...
π’University: Vilnius university, Vilnius, Lithuania
π Study the paper
β‘οΈChannel: @ComplexNetworkAnalysis
#review #fake_news
Forwarded from Bioinformatics
π¬ Inferring Biological Networks
π₯ from Claudia Solis-Lemus, Wisconsin Institutes for Discovery, UW-Madison
π Watch
π²Channel: @Bioinformatics
#video #network
π₯ from Claudia Solis-Lemus, Wisconsin Institutes for Discovery, UW-Madison
π Watch
π²Channel: @Bioinformatics
#video #network
YouTube
Claudia Solis-Lemus: Inferring Biological Networks
UW-Madison, Wisconsin Evolution, Evolution Seminar Series
https://evolution.wisc.edu/seminars/seminars-info/
https://evolution.wisc.edu
Claudia Solis-Lemus, Assistant Professor, Department of Plant Pathology and Wisconsin Institutes for Discovery, UW-Madisonβ¦
https://evolution.wisc.edu/seminars/seminars-info/
https://evolution.wisc.edu
Claudia Solis-Lemus, Assistant Professor, Department of Plant Pathology and Wisconsin Institutes for Discovery, UW-Madisonβ¦
Link_Prediction_in_Social_Networks_A_Review.pdf
243.8 KB
πLink Prediction in Social Networks: A Review
π Publish year: 2024
πConference: 2024 International Conference on Emerging Innovations and Advanced Computing (INNOCOMP)
π§βπ»Authors: Meghana Sreeya Veeramallu, Harshitha Reddy Mallu, Ramadasu B
π’Universities: Chaitanya Bharathi Institute of Technology, India
π Study paper
π±Channel: @ComplexNetworkAnalysis
#paper #Link_Prediction #review
π Publish year: 2024
πConference: 2024 International Conference on Emerging Innovations and Advanced Computing (INNOCOMP)
π§βπ»Authors: Meghana Sreeya Veeramallu, Harshitha Reddy Mallu, Ramadasu B
π’Universities: Chaitanya Bharathi Institute of Technology, India
π Study paper
π±Channel: @ComplexNetworkAnalysis
#paper #Link_Prediction #review
π1
π¬ Random Graphs
π Watch
β«οΈPart 1
β«οΈPart 2
β‘οΈChannel: @ComplexNetworkAnalysis
#video #random
π Watch
β«οΈPart 1
β«οΈPart 2
β‘οΈChannel: @ComplexNetworkAnalysis
#video #random
YouTube
Mining Complex Networks - Chapter 2 (part 1/2) - Random Graphs
@MiningComplexNetworks
π Graph Coloring Problem Explained
π₯ Watch
β‘οΈChannel: @ComplexNetworkAnalysis
#video #graph #coloring
π₯ Watch
β‘οΈChannel: @ComplexNetworkAnalysis
#video #graph #coloring
YouTube
Graph coloring
Chapter 13, section 2 of www.fundamentalalgorithms.com/fas24.
π2
π Graph Neural Networks
π₯presented by Giannis Nikolentzos at the 2024 HIAS AI Summer School
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video #GNN
π₯presented by Giannis Nikolentzos at the 2024 HIAS AI Summer School
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video #GNN
YouTube
2024 HIAS AI Summer School - Graph Neural Networks - Giannis Nikolentzos
2024 HIAS AI Summer School Day 1
Graph Neural Networks
Giannis Nikolentzos, University of Patras
Graph Neural Networks
Giannis Nikolentzos, University of Patras
π Machine Learning with Graphs: design space of graph neural networks
π₯Free recorded course by Prof. Jure Leskovec
π₯ This part discussed the important topic of GNN architecture design. Here, we introduce 3 key aspects in GNN design: (1) a general GNN design space, which includes intra-layer design, inter-layer design and learning configurations; (2) a GNN task space with similarity metrics so that we can characterize different GNN tasks and, therefore, transfer the best GNN models across tasks; (3) an effective GNN evaluation technique so that we can convincingly evaluate any GNN design question, such as βIs BatchNorm generally useful for GNNs?β. Overall, we provide the first systematic investigation of general guidelines for GNN design, understandings of GNN tasks, and how to transfer the best GNN designs across tasks. We release GraphGym as an easy-to-use code platform for GNN architectural design. More information can be found in the paper: Design Space for Graph Neural Networks
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #Machine_Learning
π₯Free recorded course by Prof. Jure Leskovec
π₯ This part discussed the important topic of GNN architecture design. Here, we introduce 3 key aspects in GNN design: (1) a general GNN design space, which includes intra-layer design, inter-layer design and learning configurations; (2) a GNN task space with similarity metrics so that we can characterize different GNN tasks and, therefore, transfer the best GNN models across tasks; (3) an effective GNN evaluation technique so that we can convincingly evaluate any GNN design question, such as βIs BatchNorm generally useful for GNNs?β. Overall, we provide the first systematic investigation of general guidelines for GNN design, understandings of GNN tasks, and how to transfer the best GNN designs across tasks. We release GraphGym as an easy-to-use code platform for GNN architectural design. More information can be found in the paper: Design Space for Graph Neural Networks
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #Machine_Learning
arXiv.org
Design Space for Graph Neural Networks
The rapid evolution of Graph Neural Networks (GNNs) has led to a growing number of new architectures as well as novel applications. However, current research focuses on proposing and evaluating...
πΉCreating a Social Network with ChatGPT
π Watch
β‘οΈChannel: @ComplexNetworkAnalysis
#video #chatgpt
π Watch
β‘οΈChannel: @ComplexNetworkAnalysis
#video #chatgpt
YouTube
Creating a Social Network with ChatGPT
Website: https://thenewboston.com/
Source Code: https://github.com/thenewboston-developers
Core Deployment Guide: https://docs.google.com/document/d/16NDHWtmwmsnrACytRXp2T9Jg7R5FgzRmkYoDteFKxyc/edit?usp=sharing
Source Code: https://github.com/thenewboston-developers
Core Deployment Guide: https://docs.google.com/document/d/16NDHWtmwmsnrACytRXp2T9Jg7R5FgzRmkYoDteFKxyc/edit?usp=sharing
πStochastic Block Models for Complex Network Analysis: A Survey
π Publish year: 2024
πJournal: ACM Transactions on Knowledge Discovery from Data (I.F=4)
π§βπ»Authors: Xueyan Liu, Wenzhuo Song, Katarzyna Musial, Yang Li, Xuehua Zhao, Bo Yang
π’Universities: Jilin University, Northeast Normal University, University of Technology Sydney, Aviation University of Air Force, Shenzhen Institute of Information Technology, Jilin University
π Study paper
π±Channel: @ComplexNetworkAnalysis
#paper #Stochastic #Block #review
π Publish year: 2024
πJournal: ACM Transactions on Knowledge Discovery from Data (I.F=4)
π§βπ»Authors: Xueyan Liu, Wenzhuo Song, Katarzyna Musial, Yang Li, Xuehua Zhao, Bo Yang
π’Universities: Jilin University, Northeast Normal University, University of Technology Sydney, Aviation University of Air Force, Shenzhen Institute of Information Technology, Jilin University
π Study paper
π±Channel: @ComplexNetworkAnalysis
#paper #Stochastic #Block #review
π Graph Data Management and Graph Machine Learning: Synergies and Opportunities
π Publish year: 2025
π§βπ»Authors: Arijit Kha, Xiangyu Ke, Yinghui Wu
π’University:
- Aalborg University, Denmark
- Zhejiang University, China
- Case Western Reserve University, USA
π Study the paper
β‘οΈChannel: @ComplexNetworkAnalysis
#review #graph #machine_learning #data_management
π Publish year: 2025
π§βπ»Authors: Arijit Kha, Xiangyu Ke, Yinghui Wu
π’University:
- Aalborg University, Denmark
- Zhejiang University, China
- Case Western Reserve University, USA
π Study the paper
β‘οΈChannel: @ComplexNetworkAnalysis
#review #graph #machine_learning #data_management
π1