Engineering in Computer Science and Artificial Intelligence

The course will include some of the following topics: • Theoretical algorithms for graph modeling and analysis: ◦ Real graph properties and models (Gnp, preferential attachment, Kleinberg’s reachability) ◦ Models for propagation (linear threshold, cascade) and for opinion formation ◦ Homophily and influence and algorithms for identifying and distinguishing ◦ Influence maximization ◦ Algorithms for graph alignment ◦ Dense subgraphs, community detection, graph minors ◦ Graph summarization and sampling • Machine-learning approaches: ◦ Label propagation ◦ Graph transformers ◦ Knowledge-graph emdeddings ◦ Models for analysis of temporal graphs ◦ Explainability • Architectures for handling large graph data: ◦ Spark GraphsX ◦ AWS Neptune ◦ AWS GraphStorm ◦ Neo4J

- Knowledge of basic algorithms - Programming - Linear algebra - Probability - Neural networks

We will use some book chapters and current research publications.

Whereas class participation is strongly recommended, it is not obbligatory. However, student active participation in class (e.g., by making questions and responding to questions) will be rewarded.

The evaluation will include one or more of the following: - homework problems - presentations - project

The course will include some of the following topics: • Theoretical algorithms for graph modeling and analysis: ◦ Real graph properties and models (Gnp, preferential attachment, Kleinberg’s reachability) ◦ Models for propagation (linear threshold, cascade) and for opinion formation ◦ Homophily and influence and algorithms for identifying and distinguishing ◦ Influence maximization ◦ Algorithms for graph alignment ◦ Dense subgraphs, community detection, graph minors ◦ Graph summarization and sampling • Machine-learning approaches: ◦ Label propagation ◦ Graph transformers ◦ Knowledge-graph emdeddings ◦ Models for analysis of temporal graphs ◦ Explainability • Architectures for handling large graph data: ◦ Spark GraphsX ◦ AWS Neptune ◦ AWS GraphStorm ◦ Neo4J

- Knowledge of basic algorithms - Programming - Linear algebra - Probability - Neural networks

We will use some book chapters and current research publications.

Whereas class participation is strongly recommended, it is not obbligatory. However, student active participation in class (e.g., by making questions and responding to questions) will be rewarded.

The evaluation will include one or more of the following: - homework problems - presentations - project