📃A Survey of Graph Neural Networks for Social Recommender Systems

🗓 Publish year: 2024

🧑‍💻Authors: KARTIK SHARMA, YEON-CHANG LEE, SIVAGAMI NAMBI,...

🏢Universities: Georgia Institute of Technology, Ulsan National Institute of Science and Technology, Hanyang University.

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🎞 Machine Learning with Graphs: hyperbolic graph embeddings

💥Free recorded course by Prof. Jure Leskovec

💥 This part focused on graph representation learning in Euclidean embedding spaces. In this lecture, we introduce hyperbolic embedding spaces, which are great for modeling hierarchical, tree-like graphs. Moreover, we introduce basics for hyperbolic geometry models, which leads to the idea of hyperbolic GNNs. More details can be found in the paper: Hyperbolic Graph Convolutional Neural Networks

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🎞 Intro to Graph Analytics in Python

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🎞 Graph Coloring Problem Explained

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

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#github #graph #visualization #book

📃 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

🎞 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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📃A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions

📕 Journal:IEEE Transactions on Knowledge and Data Engineering (I.F.=8.9)
🗓 Publish year: 2025

🧑‍💻Authors: Zemin Liu; Yuan Li; Nan Chen, ...
🏢Universities: National University of Singapore

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📦 Github
💥 Early access

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📚 A Simple Introduction to Graph Theory
💥Booklet

🗓Publish year: 2024

🧑‍💻Author: Brian Heinold
🏢University: Mount Saint Mary's University, USA

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📄 Graph Foundation Models: A Comprehensive Survey

🗓 Publish year: 2025

🧑‍💻Authors: Zehong Wang, Zheyuan Liu, Tianyi Ma, ...
🏢Universities: University of Notre Dame, University of Connecticut, University of Virginia,
University of Illinois Urbana-Champaign, USA - University of Cambridge, England

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📦 GitHub Resources

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🎥 Graph Classification: Step-by-step in action

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📹 Introduction to Graph Machine Learning: Methods and Applications
💥 Jundong Li, Associate Professor, University of Virginia

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