📃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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#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

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#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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#review #anonymization

📃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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#Paper #Survey #GNN #Application

📄 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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#review #fake_news

🎬 Inferring Biological Networks
💥 from Claudia Solis-Lemus, Wisconsin Institutes for Discovery, UW-Madison

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📲Channel: @Bioinformatics
#video #network

📃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

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#paper #Link_Prediction #review

🎬 Random Graphs

🎞 Watch
▫️Part 1
▫️Part 2

⚡️Channel: @ComplexNetworkAnalysis
#video #random

🎞 Graph Coloring Problem Explained

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⚡️Channel: @ComplexNetworkAnalysis
#video #graph #coloring

🎞 Graph Neural Networks

💥presented by Giannis Nikolentzos at the 2024 HIAS AI Summer School

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📱Channel: @ComplexNetworkAnalysis
#video #GNN

🎞 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

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

📹Creating a Social Network with ChatGPT

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#video #chatgpt

📃Stochastic Block Models for Complex Network Analysis: A Survey

🗓 Publish year: 2024
📘Journal: ACM Transactions on Knowledge Discovery from Data (I.F=4)

🧑‍💻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

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#paper #Stochastic #Block #review

📄 Graph Data Management and Graph Machine Learning: Synergies and Opportunities

🗓 Publish year: 2025

🧑‍💻Authors: Arijit Kha, Xiangyu Ke, Yinghui Wu
🏢University:
- Aalborg University, Denmark
- Zhejiang University, China
- Case Western Reserve University, USA

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⚡️Channel: @ComplexNetworkAnalysis
#review #graph #machine_learning #data_management

📚 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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#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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#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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#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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#paper #Large_scale #Complex #Information #Representation_Learning #survey