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2023_A_Survey_of_Large_scale_Complex_Information_Network_Representation.pdf
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πŸ“ƒA Survey of Large-scale Complex Information Network Representation Learning Methods

πŸ—“ Publish year: 2023
πŸ“˜
Conference: Consumer Electronics and Computer Engineering (ICCECE)

πŸ§‘β€πŸ’»Authors: Xiaoxian Zhang
🏒Universities: School of Computer Technology and Engineering Changchun Institute of Technology, Changchun, China

πŸ“Ž Study paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #Large_scale #Complex #Information #Representation_Learning #survey
πŸŽ₯ Knowledge graphs - Foundations and applications

🎞 Watch the collection

⚑️Channel: @ComplexNetworkAnalysis
#video #knowledge_graph
πŸ“‘Explaining the Explainers in Graph Neural Networks: a Comparative Study

πŸ“• Journal: ACM Computing Surveys (πŸ”₯I.F.=23.8)
πŸ—“
Publish year: 2025

πŸ§‘β€πŸ’»Authors: Antonio Longa, Steve Azzolin, Gabriele Santin, ...
🏒Universities: University of Trento, Italy - Cambridge University, UK

πŸ“Ž Study the paper

⚑️Channel: @ComplexNetworkAnalysis
#review #explainability #gnn
πŸ“ƒNetwork link prediction via deep learning method: A comparative analysis with traditional methods

πŸ—“ Publish year: 2024
πŸ“˜
Journal: Engineering Science and Technology, an International Journal (I.F=5.1)

πŸ§‘β€πŸ’»Authors: Gholamreza Zare, Nima Jafari Navimipour, Mehdi Hosseinzadeh, Amir Sahafi

🏒Universities: Islamic Azad University, Qeshm Branch, Qeshm, Iran
Islamic Azad University, Tabriz Branch, Tabriz, Iran
National Yunlin University of Science and Technology, Douliou, Yunlin 64002, Taiwan
Western Caspian University, Baku, Azerbaijan
Duy Tan University, Da Nang, Viet Nam
Duy Tan University, School of Medicine and Pharmacy, Da Nang, Viet Nam
Islamic Azad University, South Tehran Branch, Tehran, Iran


πŸ“Ž Study paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #Link_Prediction #Deep_learning #traditional
πŸŽ“ Algorithms and Graph Structures for Splitting Network Flows, in Theory and Practice

πŸ“•PhD thesis from University of Helsinki, Finland

πŸ—“Publish year: 2025

πŸ“Ž Study thesis

⚑️Channel: @ComplexNetworkAnalysis
#thesis #network_flow
πŸ“„ A Survey of Graph Transformers: Architectures, Theories and Applications

πŸ—“ Publish year: 2025

πŸ§‘β€πŸ’»Authors: Chaohao Yuan, Kangfei Zhao, Ercan Engin Kuruoglu, ...
🏒Universities: Tsinghua University - Chinese University of Hong Kong - Chinese Academy of Sciences, China

πŸ“Ž Study the paper

⚑️Channel: @ComplexNetworkAnalysis
#review #transformer
🎞 Node centrality metric and link analysis

πŸ’₯Social Network Analysis Lecture 3

πŸ“½ Watch

πŸ“±Channel: @ComplexNetworkAnalysis
#video #Node #centerality #link
🎞 Machine Learning with Graphs: GraphSAGE Neighbor Sampling

πŸ’₯Free recorded course by Prof. Jure Leskovec

πŸ’₯ This part discussed Neighbor Sampling, That is a representative method used to scale up GNNs to large graphs. The key insight is that a K-layer GNN generates a node embedding by using only the nodes from the K-hop neighborhood around that node. Therefore, to generate embeddings of nodes in the mini-batch, only the K-hop neighborhood nodes and their features are needed to load onto a GPU, a tractable operation even if the original graph is large. To further reduce the computational cost, only a subset of neighboring nodes is sampled for GNNs to aggregate.


πŸ“½ Watch

πŸ“²Channel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #Machine_Learning #GraphSAGE
πŸ“ƒA Review of Link Prediction Algorithms in Dynamic Networks

πŸ“— Journal: Mathematics (I.F.=2.3)
πŸ—“
Publish year: 2025

πŸ§‘β€πŸ’»Authors: Mengdi Sun, Minghu Tang
🏒Universities: Qinghai Minzu University, China

πŸ“Ž Study the paper

⚑️Channel: @ComplexNetworkAnalysis
#review #explainability #gnn
Forwarded from Bioinformatics
πŸ“ƒ Graph Neural Network-Based Approaches to Drug Repurposing: A Comprehensive Survey

πŸ—“ Publish year: 2025

πŸ§‘β€πŸ’»
Authors: Alireza A.Tabatabaei, Mohammad Ebrahim Mahdavi, Ehsan Beiranvand, ...
🏒Universities: University of Isfahan, Shahid Beheshti University of Medical Sciences, University of Tehran - Iran

πŸ“Ž Study the paper

πŸ“²Channel: @Bioinformatics
#review #drug #repurposing #gnn
πŸ“ƒData Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook

πŸ—“ Publish year: 2025

πŸ§‘β€πŸ’»Authors: Jiawei Xue, Ruichen Tan, Jianzhu Ma, Satish V. Ukkusuri

🏒Universities: Purdue University, West Lafayette, IN, USA.
Tsinghua University, Beijing, China.

πŸ“Ž Study paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #Data_Mining #Transportation #GNN #review
πŸ“˜ Introduction to Random Graphs
πŸ’₯ Free online book by Carnegie Mellon University, 2025

🌐 Study

⚑️Channel: @ComplexNetworkAnalysis
#book #graph #random
πŸ“ƒInformation diffusion analysis: process, model, deployment, and application

πŸ“— Journal:The Knowledge Engineering Review (I.F.=2.8)
πŸ—“
Publish year: 2025

πŸ§‘β€πŸ’»Authors: Shashank Sheshar Singh, Divya Srivastava, Madhushi Verma, ...
🏒Universities: Thapar Institute of Engineering & Technology, Bennett University, India

πŸ“Ž Study the paper

⚑️Channel: @ComplexNetworkAnalysis
#review #explainability #gnn
🎞 Introduction to Social Network Analysis


πŸ’₯This session is part of the ESRC Centre for Society and Mental Health's Research Methods Primer and Provocation series.

πŸ’₯In this session, Dr Molly Copeland and Holly Crudgington provide an introduction to social network analysis (SNA) with a focus on major theories and conceptual approaches to using ego-centric and sociometric network data for those new to considering networks.

πŸ“½ Watch

πŸ“±Channel: @ComplexNetworkAnalysis
#video
2025/06/25 21:31:41
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