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🎞 Machine Learning with Graphs: design space of graph neural networks

πŸ’₯Free recorded course by Prof. Jure Leskovec

πŸ’₯ 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 β€œIs 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

πŸ“½ Watch

πŸ“²Channel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #Machine_Learning
πŸ“ƒStochastic Block Models for Complex Network Analysis: A Survey

πŸ—“ Publish year: 2024
πŸ“˜
Journal: ACM Transactions on Knowledge Discovery from Data (I.F=4)

πŸ§‘β€πŸ’»Authors: Xueyan Liu, Wenzhuo Song, Katarzyna Musial, Yang Li, Xuehua Zhao, Bo Yang
🏒Universities: Jilin University, Northeast Normal University, University of Technology Sydney, Aviation University of Air Force, Shenzhen Institute of Information Technology, Jilin University

πŸ“Ž Study paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #Stochastic #Block #review
πŸ“„ Graph Data Management and Graph Machine Learning: Synergies and Opportunities

πŸ—“
Publish year: 2025

πŸ§‘β€πŸ’»Authors: Arijit Kha, Xiangyu Ke, Yinghui Wu
🏒University:
- Aalborg University, Denmark
- Zhejiang University, China

- Case Western Reserve University, USA

πŸ“Ž Study the paper

⚑️Channel: @ComplexNetworkAnalysis
#review #graph #machine_learning #data_management
πŸ‘1
πŸ“ƒCounterfactual Learning on Graphs: A Survey

πŸ—“ Publish year: 2025
πŸ“˜Journal: Machine Intelligence Research

πŸ§‘β€πŸ’»Authors: Zhimeng Guo, Zongyu Wu, Teng Xiao, Charu Aggarwal , Hui Liu, Suhang Wang
🏒Universities: Pennsylvania State University, Watson Research Center

πŸ“Ž Study paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper #Counterfactual #Survey
πŸ“š A curated list of awesome network analysis resources
πŸ’₯ GitBook website

🌐 Study

⚑️Channel: @ComplexNetworkAnalysis
#github #graph #visualization #book
πŸ“ƒBibliometric and visualized analysis of social network analysis research on Scopus databases and VOSviewer

πŸ—“ Publish year: 2024
πŸ“˜
Journal: Cogent Business & Management (I.F=3)

πŸ§‘β€πŸ’»Authors: Dyah gandasari, David tjahjana, Diena Dwidienawati and Mochamad Sugiarto

🏒Universities: Polbangtan Bogor, Bogor, indonesia; universitas Multimedia nusantara, Jakarta, indonesia; Business Management, BinusBusiness school, Bina nusantara university, Jakarta, indonesia; Faculty of animal science, Jendral soedirman university,indonesia

πŸ“Ž Study paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #Bibliometric #Scopus #VOSviewer
πŸ‘2
πŸ“‘A Survey on Exploring Real and Virtual Social Network Rumors: State-of-the-Art and Research Challenges

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

πŸ§‘β€πŸ’»Authors: Qiang He, Songyangjun Zhang, Yuliang Cai, ...
🏒Universities:
▫️Northeastern University-Liaoning University
-The First Hospital of China Medical University, China
▫️Waseda University, Japan

πŸ“Ž Study the paper

⚑️Channel: @ComplexNetworkAnalysis
#review #rumor
πŸ“ƒ Methods of decomposition theory and graph labeling in the study of social network structure

πŸ—“ Publish year: 2024

πŸ§‘β€πŸ’»Authors: L Hulianytskyi, M Semeniuta, S Yakymenko
🏒Universities: Prospekt Universytetskyi,Ukraine

πŸ“Ž Study the paper

⚑️Channel: @ComplexNetworkAnalysis
#review #graph_labling #decomposition
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2023_A_Survey_of_Large_scale_Complex_Information_Network_Representation.pdf
4.2 MB
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‘1
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
πŸ‘1
🎞 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
πŸ‘1
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
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