📚 A curated list of awesome network analysis resources
💥 GitBook website

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⚡️Channel: @ComplexNetworkAnalysis
#github #graph #visualization #book

📃Bibliometric and visualized analysis of social network analysis research on Scopus databases and VOSviewer

🗓 Publish year: 2024
📘Journal: Cogent Business & Management (I.F=3)

🧑‍💻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

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📱Channel: @ComplexNetworkAnalysis
#paper #Bibliometric #Scopus #VOSviewer

📑A Survey on Exploring Real and Virtual Social Network Rumors: State-of-the-Art and Research Challenges

📕 Journal: ACM Computing Surveys (🔥I.F.=23.8)
🗓 Publish year: 2025

🧑‍💻Authors: Qiang He, Songyangjun Zhang, Yuliang Cai, ...
🏢Universities:
▫️Northeastern University-Liaoning University-The First Hospital of China Medical University, China
▫️Waseda University, Japan

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⚡️Channel: @ComplexNetworkAnalysis
#review #rumor

📃 Methods of decomposition theory and graph labeling in the study of social network structure

🗓 Publish year: 2024

🧑‍💻Authors: L Hulianytskyi, M Semeniuta, S Yakymenko
🏢Universities: Prospekt Universytetskyi,Ukraine

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⚡️Channel: @ComplexNetworkAnalysis
#review #graph_labling #decomposition

📃A Survey of Large-scale Complex Information Network Representation Learning Methods

🗓 Publish year: 2023
📘Conference: Consumer Electronics and Computer Engineering (ICCECE)

🧑‍💻Authors: Xiaoxian Zhang
🏢Universities: School of Computer Technology and Engineering Changchun Institute of Technology, Changchun, China

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📱Channel: @ComplexNetworkAnalysis
#paper #Large_scale #Complex #Information #Representation_Learning #survey

🎥 Knowledge graphs - Foundations and applications

🎞 Watch the collection

⚡️Channel: @ComplexNetworkAnalysis
#video #knowledge_graph

📑Explaining the Explainers in Graph Neural Networks: a Comparative Study

📕 Journal: ACM Computing Surveys (🔥I.F.=23.8)
🗓 Publish year: 2025

🧑‍💻Authors: Antonio Longa, Steve Azzolin, Gabriele Santin, ...
🏢Universities: University of Trento, Italy - Cambridge University, UK

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⚡️Channel: @ComplexNetworkAnalysis
#review #explainability #gnn

📃Network link prediction via deep learning method: A comparative analysis with traditional methods

🗓 Publish year: 2024
📘Journal: Engineering Science and Technology, an International Journal (I.F=5.1)

🧑‍💻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

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📱Channel: @ComplexNetworkAnalysis
#paper #Link_Prediction #Deep_learning #traditional

🎓 Algorithms and Graph Structures for Splitting Network Flows, in Theory and Practice

📕PhD thesis from University of Helsinki, Finland

🗓Publish year: 2025

📎 Study thesis

⚡️Channel: @ComplexNetworkAnalysis
#thesis #network_flow

📄 A Survey of Graph Transformers: Architectures, Theories and Applications

🗓 Publish year: 2025

🧑‍💻Authors: Chaohao Yuan, Kangfei Zhao, Ercan Engin Kuruoglu, ...
🏢Universities: Tsinghua University - Chinese University of Hong Kong - Chinese Academy of Sciences, China

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⚡️Channel: @ComplexNetworkAnalysis
#review #transformer

🎞 Node centrality metric and link analysis

💥Social Network Analysis Lecture 3

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📱Channel: @ComplexNetworkAnalysis
#video #Node #centerality #link

🎞 Machine Learning with Graphs: GraphSAGE Neighbor Sampling

💥Free recorded course by Prof. Jure Leskovec

💥 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.


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📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #Machine_Learning #GraphSAGE

📃A Review of Link Prediction Algorithms in Dynamic Networks

📗 Journal: Mathematics (I.F.=2.3)
🗓 Publish year: 2025

🧑‍💻Authors: Mengdi Sun, Minghu Tang
🏢Universities: Qinghai Minzu University, China

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⚡️Channel: @ComplexNetworkAnalysis
#review #explainability #gnn

📃 Graph Neural Network-Based Approaches to Drug Repurposing: A Comprehensive Survey

🗓 Publish year: 2025

🧑‍💻Authors: Alireza A.Tabatabaei, Mohammad Ebrahim Mahdavi, Ehsan Beiranvand, ...
🏢Universities: University of Isfahan, Shahid Beheshti University of Medical Sciences, University of Tehran - Iran

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📲Channel: @Bioinformatics
#review #drug #repurposing #gnn

📃Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook

🗓 Publish year: 2025

🧑‍💻Authors: Jiawei Xue, Ruichen Tan, Jianzhu Ma, Satish V. Ukkusuri

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

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📱Channel: @ComplexNetworkAnalysis
#paper #Data_Mining #Transportation #GNN #review