Graph Mining: The Bridge Connecting Relational Data and Artificial Intelligence

This article introduces the Graph Mining Course Resource Library from the Department of Artificial Intelligence at Catholic University of Korea, which systematically covers core technologies such as Graph Neural Networks (GNN), community detection, and link prediction. It provides a complete learning path from theoretical foundations to practical applications, helping learners master key skills for connecting relational data and artificial intelligence.

Traditional machine learning mainly deals with structured (tabular) and unstructured (text/image) data, but a large amount of real-world data exists in relational forms (such as social networks, knowledge graphs, biological networks, etc.). Graph mining technology can capture complex patterns in these relationships, supporting applications like recommendation systems, fraud detection, and drug discovery; the rise of graph neural networks has further made graph mining an active research direction in the AI field.

• Sample Code: Python code provided every Tuesday, based on libraries like NetworkX and PyTorch Geometric, covering graph representation, basic algorithms, embedding techniques, and GNN architectures;
• Practical Exercises: Hands-on algorithm implementation every Thursday, including data preprocessing, performance optimization, and visualization;
• Homework: Theoretical derivation + programming tasks + experiment reports, graded on a four-level scale;
• Final Project: Solve real-world problems in Kaggle competitions, weighted 50 points, emphasizing project practice.

• Graph Foundations and Representation Learning: Basics of graph theory (types, attributes, matrix representation), node embedding (DeepWalk, Node2Vec, LINE, etc.);
• Graph Neural Networks: Basic architectures like GCN, GAT, GraphSAGE, and advanced topics such as graph pooling, generative models, temporal/heterogeneous graph networks;
• Community Detection: Traditional methods (Louvain, spectral clustering) and deep learning methods (GNN clustering, graph autoencoders);
• Link Prediction: Similarity methods, embedding methods, GNN methods, applied to friend recommendation, knowledge graph completion, etc.;
• Graph Classification and Regression: Tasks like molecular property prediction, traffic flow prediction.

Current Hot Applications: Recommendation systems (PinSage, GraphSAIL), drug discovery (molecular property prediction), knowledge graphs (representation learning and reasoning), financial risk control (fraud detection); Future Trends: Large-scale graph processing (sampling/partitioning/distributed training), dynamic graph analysis (temporal GNN), interpretability, multimodal graph learning.

• Basic Stage: Master basic graph theory algorithms (BFS/DFS), NetworkX tools, and node embedding techniques;
• Advanced Stage: Learn GNN basics (GCN/GAT), implement models using PyTorch Geometric, and complete practical projects;
• Advanced Stage: Explore cutting-edge topics (graph generation, temporal graphs), participate in Kaggle competitions, and read top conference papers (KDD/NeurIPS/ICLR).