📃Research Output, Key Topics, and Trends in Productivity, Visibility, and Collaboration in Social Sciences Research on COVID-19: A Scientometric Analysis and Visualization
🗓 Publish year: 2024
📘Journal: SAGE Open (I.F=2)
🧑💻Authors: Walaa Hamdan, Hanan Alsuqaih
🏢Universities: College of Humanities and Social Sciences, Princess Nourah bint Abdulrahman University, Airport Road, P.O.Box 84428, Riyadh 11671, Saudi Arabia.
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📱Channel: @ComplexNetworkAnalysis
#paper #Visibility #Collaboration #Productivity #Scientometric #COVID_19 #Visualization
🗓 Publish year: 2024
📘Journal: SAGE Open (I.F=2)
🧑💻Authors: Walaa Hamdan, Hanan Alsuqaih
🏢Universities: College of Humanities and Social Sciences, Princess Nourah bint Abdulrahman University, Airport Road, P.O.Box 84428, Riyadh 11671, Saudi Arabia.
📎 Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Visibility #Collaboration #Productivity #Scientometric #COVID_19 #Visualization
🎞 Link Prediction with GraphSage explained | step-by-step
🎥 Watch
⚡️Channel: @ComplexNetworkAnalysis
#video #link_prediction #gnn
🎥 Watch
⚡️Channel: @ComplexNetworkAnalysis
#video #link_prediction #gnn
YouTube
03 - Link Prediction with GraphSage explained | step-by-step
0:00 What is link prediction?
1:53 Preparing the graph for link prediction
15:20 GNN with SageConv
26:20 Training and Testing
In this video, we explore the exciting process of link prediction using GraphSAGE Convolution (SAGEConv).
Link prediction helps…
1:53 Preparing the graph for link prediction
15:20 GNN with SageConv
26:20 Training and Testing
In this video, we explore the exciting process of link prediction using GraphSAGE Convolution (SAGEConv).
Link prediction helps…
📃Exploring the use of social network analysis methods in process improvement within healthcare organizations: a scoping review
🗓 Publish year: 2024
📘Journal: BMC Health Services Research (I.F=2.7)
🧑💻Authors: Troy Francis، Morgan Davidson، Laura Senese، Lianne Jeffs، Reza Yousefi-Nooraie، Mathieu Ouimet، Valeria Rac، Patricia Trbovich
🏢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.
📎 Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #healthcare #organizations #review
🗓 Publish year: 2024
📘Journal: BMC Health Services Research (I.F=2.7)
🧑💻Authors: Troy Francis، Morgan Davidson، Laura Senese، Lianne Jeffs، Reza Yousefi-Nooraie، Mathieu Ouimet، Valeria Rac، Patricia Trbovich
🏢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.
📎 Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #healthcare #organizations #review
📑Can Graph Neural Networks be Adequately Explained? A Survey
📗 Journal: ACM Computing Surveys (🔥I.F.=23.8)
🗓 Publish year: 2025
🧑💻Authors: Xuyan Li, Jie Wang, Zheng Yan
🏢University: Xidian University, China
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⚡️Channel: @ComplexNetworkAnalysis
#review #gnn #explainability
📗 Journal: ACM Computing Surveys (🔥I.F.=23.8)
🗓 Publish year: 2025
🧑💻Authors: Xuyan Li, Jie Wang, Zheng Yan
🏢University: Xidian University, China
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⚡️Channel: @ComplexNetworkAnalysis
#review #gnn #explainability
🎞 Machine Learning with Graphs: hyperbolic graph embeddings
💥Free recorded course by Prof. Jure Leskovec
💥 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
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #Machine_Learning
💥Free recorded course by Prof. Jure Leskovec
💥 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
📽 Watch
📲Channel: @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 Stanford’s 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 embedding…
Jure Leskovec
Computer Science, PhD
In previous lectures, we focused on graph representation learning in Euclidean embedding…
👍2
Forwarded from Bioinformatics
📑 Enhancing Molecular Network-Based Cancer Driver Gene Prediction Using Machine Learning Approaches: Current Challenges and Opportunities
📓 Journal: Journal of Cellular and Molecular Medicine (I.F.=4.3)
🗓Publish year: 2025
🧑💻Authors: Hao Zhang, Chaohuan Lin, Ying'ao Chen, ...
🏢Universities: Wenzhou Medical University - University of Chinese Academy of Sciences, China
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📲Channel: @Bioinformatics
#review #cancer #network #driver_gene #machine_learning
📓 Journal: Journal of Cellular and Molecular Medicine (I.F.=4.3)
🗓Publish year: 2025
🧑💻Authors: Hao Zhang, Chaohuan Lin, Ying'ao Chen, ...
🏢Universities: Wenzhou Medical University - University of Chinese Academy of Sciences, China
📎 Study the paper
📲Channel: @Bioinformatics
#review #cancer #network #driver_gene #machine_learning
📃Understanding When and Why Graph Attention Mechanisms Work via Node Classification
🗓Publish year: 2024
🧑💻Authors: Didier A. Vega-Oliveros, Alneu de Andrade Lopes, Lilian Berton
🏢University: Northwestern Polytechnical University, Shanghai Artificial Intelligence Laboratory, Shanghai Jiaotong University
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📲Channel: @ComplexNetworkAnalysis
#Paper #GAT #Attention #node_classification
🗓Publish year: 2024
🧑💻Authors: Didier A. Vega-Oliveros, Alneu de Andrade Lopes, Lilian Berton
🏢University: Northwestern Polytechnical University, Shanghai Artificial Intelligence Laboratory, Shanghai Jiaotong University
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📲Channel: @ComplexNetworkAnalysis
#Paper #GAT #Attention #node_classification
📃A comprehensive survey on graph neural network accelerators
🗓 Publish year: 2025
📘Journal: Frontiers of Computer Science (I.F=3.4)
🧑💻Authors: Jingyu LIU, Shi CHEN, Li SHEN
🏢Universities: School of Computer, National University of Defense Technology, Changsha 410073, China
📎 Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #GNN #survey
🗓 Publish year: 2025
📘Journal: Frontiers of Computer Science (I.F=3.4)
🧑💻Authors: Jingyu LIU, Shi CHEN, Li SHEN
🏢Universities: School of Computer, National University of Defense Technology, Changsha 410073, China
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📱Channel: @ComplexNetworkAnalysis
#paper #GNN #survey
📄 A comprehensive bibliometric analysis on social network anonymization: current approaches and future directions
📕 Journal: Knowledge and Information System (I.F.=2.5)
🗓 Publish year: 2025
🧑💻Authors: Navid Yazdanjue, Hossein Yazdanjouei, Hassan Gharoun,...
🏢University:
- University of Technology Sydney, Ultimo, Australia
- Urmia University, Urmia &Iran University of Science and Technology, Iran
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⚡️Channel: @ComplexNetworkAnalysis
#review #anonymization
📕 Journal: Knowledge and Information System (I.F.=2.5)
🗓 Publish year: 2025
🧑💻Authors: Navid Yazdanjue, Hossein Yazdanjouei, Hassan Gharoun,...
🏢University:
- University of Technology Sydney, Ultimo, Australia
- Urmia University, Urmia &Iran University of Science and Technology, Iran
📎 Study the paper
⚡️Channel: @ComplexNetworkAnalysis
#review #anonymization
👍1
📃A Survey on Graph Neural Networks and its Applications in Various Domains
🗓Publish year: 2025
🧑💻Authors: Tejaswini R. Murgod, P. Srihith Reddy, Shamitha Gaddam, S. Meenakshi Sundaram & C. Anitha
🏢University: BNM Institute of Technology, NITTE Meenakshi Institute of Technology,
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📲Channel: @ComplexNetworkAnalysis
#Paper #Survey #GNN #Application
🗓Publish year: 2025
🧑💻Authors: Tejaswini R. Murgod, P. Srihith Reddy, Shamitha Gaddam, S. Meenakshi Sundaram & C. Anitha
🏢University: BNM Institute of Technology, NITTE Meenakshi Institute of Technology,
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#Paper #Survey #GNN #Application
👍1
📄 Systematic Review of Fake News, Propaganda, and Disinformation: Examining Authors, Content, and Social Impact through Machine Learning
📗 Journal: IEEE ACCESS (I.F.=3.4)
🗓 Publish year: 2025
🧑💻Authors: D. Plikynas, I. Rizgelienė, G. Korvel,...
🏢University: Vilnius university, Vilnius, Lithuania
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⚡️Channel: @ComplexNetworkAnalysis
#review #fake_news
📗 Journal: IEEE ACCESS (I.F.=3.4)
🗓 Publish year: 2025
🧑💻Authors: D. Plikynas, I. Rizgelienė, G. Korvel,...
🏢University: Vilnius university, Vilnius, Lithuania
📎 Study the paper
⚡️Channel: @ComplexNetworkAnalysis
#review #fake_news
Forwarded from Bioinformatics
🎬 Inferring Biological Networks
💥 from Claudia Solis-Lemus, Wisconsin Institutes for Discovery, UW-Madison
🎞 Watch
📲Channel: @Bioinformatics
#video #network
💥 from Claudia Solis-Lemus, Wisconsin Institutes for Discovery, UW-Madison
🎞 Watch
📲Channel: @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-Madison…
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-Madison…
Link_Prediction_in_Social_Networks_A_Review.pdf
243.8 KB
📃Link Prediction in Social Networks: A Review
🗓 Publish year: 2024
📘Conference: 2024 International Conference on Emerging Innovations and Advanced Computing (INNOCOMP)
🧑💻Authors: Meghana Sreeya Veeramallu, Harshitha Reddy Mallu, Ramadasu B
🏢Universities: Chaitanya Bharathi Institute of Technology, India
📎 Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Link_Prediction #review
🗓 Publish year: 2024
📘Conference: 2024 International Conference on Emerging Innovations and Advanced Computing (INNOCOMP)
🧑💻Authors: Meghana Sreeya Veeramallu, Harshitha Reddy Mallu, Ramadasu B
🏢Universities: Chaitanya Bharathi Institute of Technology, India
📎 Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Link_Prediction #review
👍1
🎞 Graph Neural Networks
💥presented by Giannis Nikolentzos at the 2024 HIAS AI Summer School
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #GNN
💥presented by Giannis Nikolentzos at the 2024 HIAS AI Summer School
📽 Watch
📱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
🎞 Machine Learning with Graphs: design space of graph neural networks
💥Free recorded course by Prof. Jure Leskovec
💥 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 “Is BatchNorm generally useful for GNNs?”. 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
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #Machine_Learning
💥Free recorded course by Prof. Jure Leskovec
💥 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 “Is BatchNorm generally useful for GNNs?”. 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
📽 Watch
📲Channel: @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...