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Politecnico di Milano

GraphAI: Python for Complex Networks

Politecnico di Milano via Polimi OPEN KNOWLEDGE

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Overview

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Analyzing complex networks and developing machine learning and graph neural network models with Python and NetworkX, from static graphs to temporal networks and responsible AI.

Syllabus

  • Board
  • Week 0
  • Week 1 - Foundations of Network Science with Python
  • 1.1 Introduction
  • 1.2 Measuring Network Structure
  • 1.3 Structural Properties of Real Networks
  • 1.4 Centrality, Communities, and Motifs
  • 1.5 Dynamics on Networks
  • 1.6 From Networks to Data for AI
  • 1.7 Case Study - Exploring a Real Network with NetworkX
  • 1.8 Additional readings
  • 1.9 Evaluation Quiz
  • Week 2 - Machine Learning and Graph Neural Networks
  • 2.1 Classical ML on Network Data
  • 2.2 Graph Representation Learning: Embeddings
  • 2.3 Introduction to Graph Neural Networks
  • 2.4 Variants of GNNs: GCN, GraphSAGE, GAT
  • 2.5 Training and Evaluating GNNs
  • 2.6 Libraries and Practical Pipelines - Node classification
  • 2.7 Libraries and Practical Pipelines - Link prediction
  • 2.8 Case Study - Node Classification
  • 2.9 Additional readings
  • 2.10 Evaluation Quiz
  • Week 3 - Temporal Graphs, Applications and Responsible Graph AI
  • 3.1 From Static Graphs to Temporal Networks
  • 3.2 RNNs, GRUs, and LSTMs
  • 3.3 Temporal GNNs
  • 3.4 Anomaly Detection on Graphs
  • 3.5 Cross-domain Applications of AI on Networks
  • 3.6 Limits, Ethics, and Open Challenges
  • 3.7 Case Study - Responsible Graph Machine Learning
  • 3.8 Additional readings
  • 3.9 Evaluation Quiz
  • Final Survey
  • Additional resources

Taught by

Giacomo Fiumara

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