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Lecture 1 Introduction; Structure of Graphs
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Classroom Contents
Machine Learning with Graphs - Fall 2019
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- 1 Lecture 1 Introduction; Structure of Graphs
- 2 Lecture 2 Properties of Networks And Random Graph Models
- 3 Lecture 3 Motifs and Structural Roles in Networks
- 4 Lecture 4 Community Structure in Networks
- 5 Lecture 5 Spectral Clustering
- 6 Lecture 6 Message Passing and Node Classification
- 7 Lecture 7 Graph Representation Learning
- 8 Lecture 8 Graph Neural Networks
- 9 Lecture 9 Graph Neural Networks Implementation with Pytorch Geometric
- 10 Lecture 10 Deep Generative Models for Graphs
- 11 Lecture 11 Link Analysis - PageRank
- 12 Lecture 12 Network Effects and Cascading Behavior
- 13 Lecture 13 Probabilistic Contagion and Models of Influence
- 14 Lecture 14 Influence Maximization in Networks
- 15 Lecture 15 Outbreak Detection in Networks
- 16 Lecture 16 Network Evolution
- 17 Lecture 17 Reasoning over Knowledge Graphs
- 18 Lecture 18 Limitations of Graph Neural Networks
- 19 Lecture 19 Applications of Graph Neural Networks
