🎬 Random Graphs

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▫️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

🎥 Knowledge graphs - Foundations and applications

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#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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#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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#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

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#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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#review #transformer

🎞 Node centrality metric and link analysis

💥Social Network Analysis Lecture 3

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#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