Engineering in Computer Science and Artificial Intelligence - Ingegneria Informatica e Intelligenza Artificiale

ARISTIDIS ANAGNOSTOPOULOS
ARISTIDIS ANAGNOSTOPOULOS

Il corso presenterà modelli e algoritmi per l'analisi di grafi con applicazioni in vari ambiti. L'obiettivo alla fine del corso è che gli studenti conoscano algoritmi e framework che possano consentire loro di analizzare dati grafici di grandi dimensioni.

Graphs have applications in multiple areas, including social networks, bioinformatics, network medicine, computational chemistry, and they can be used to provide tools in these areas.

The course will present models and algorithms for the analysis of graphs as with applications on various areas. The goal at the end of the course, is for student to know algorithms and frameworks that can allow them to analyze large graph data, as well as be exposed to current topics of research in graph mining

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

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

We will use some book chapters and current research publications.

Regular Classes

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

Questions related to the course program