Ateneo - UPO

This course introduces the fundamental concepts, principles and methods in the interdisciplinary field of network science, with a particular focus on analysis techniques, modeling, and applications for the World Wide Web and online social media. Topics covered include structures of networks, mathematical models of networks, common networks topologies, structure of large scale graphs, community structures, epidemic spreading, PageRank and other centrality measures and their applications to modern information retrieval systems, dynamic processes in networks, graphs visualization. Additionally, students will learn how to apply the basic principles of network science to perform CNA (Complex Network Analysis) tasks on real data, with Python and many different packages/libraries such as networkx, igraph, networkx, and so on, as well as advanced graph visualization tools as GePhi.

[1] Filippo Menczer, Santo Fortunato, and Clayton A. Davis, A First Course in Network Science, Cambridge University Press (in biblioteca) [2] David Easley and Jon Kleinberg, Networks, Crowds, and Markets: Reasoning About a Highly Connected World, Cambridge University Press [3] Albert-László Barabási, Network Science, Cambridge University Press [4] C. D. Manning, P. Raghavan and H. Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008 Additional material provided by the teacher

The purpose of the course is twofold. First, students will learn the fundamental principles of network science (NS), including the analysis of complex networks, the basic models for studying network dynamics (e.g., social contagion, viral phenomena, spread of epidemics, etc.). Secondly, they will learn what an information retrieval (IR) system is, what are the basic techniques for designing an efficient and scalable IR system on large collections of documents, the main applications (for example, how to build a web search engine) and future directions.

Recommended skills on Computer Programming and Algorithms, basic knowledge on linear algebra, probability theory and statistics.

Lectures that introduce theoretical concepts and laboratory exercises that apply them. In the lessons, theoretical topics are addressed through slide presentations, with examples and some questions to verify students' learning. In laboratory exercises, students are guided in the implementation of simple projects aimed at putting into practice the theoretical knowledge acquired.

Monitoring the learning process: during the course the students will interact with the teacher to solve exercises and lab assignments. Students with physical disabilities, Learning Disabilities or Special Education Needs can request specific services and tools via the Staff Sviluppo e Coordinamento Carriere e Servizi alle Studentesse e agli Studenti, consulting the University webpage: https://www.uniupo.it/en/services/servicesstudents-physical-or-learning-disabilities. Students with disabilities, learning disabilities or special education needs, once they have contacted the University Staff, can refer to the tutor in charge of the course to define the examination modalities, concerning academic aspects.

The exam assesses the acquired competencies through a mandatory oral exam and the submission of an optional project.
The oral exam takes place on the scheduled exam date and consists of a series of questions of varying difficulty corresponding to different score levels, for a total of 28 points. Top marks can be achieved if students have previously communicated to the instructors their intention to complete a practical network analysis and visualizaztion project in Python. The project and its discussion are worth a score adjustment of -4/+4 points.

18–20 points guarantees a passing grade and demonstrates a minimal understanding of network science theory, and information retrieval.
21–23 points demonstrates intermediate proficiency in the principles of network modeling, community structures, centrality measures, and retrieval system architecture.
24–26 points demonstrates the ability to apply network science principles, dynamic process analysis, and information retrieval to real-world contexts.
27–28 points demonstrates the ability to rigorously apply optimized techniques for large-scale graph analysis, advanced metric extraction, and network diffusion modeling.
29–30 points is reserved for students who, having passed the oral exam with a minimum score of 26, have also developed a comprehensive project in Python (using libraries such as NetworkX or igraph) and/or in GePhi, to be agreed upon with the instructor. The project involves the empirical analysis and visualization of a real-world network dataset.
Honors (30 e Lode) is reserved for those who, in addition to achieving top scores on both the oral exam and the project, presents an impeccable analysis demonstrating complete independence in network modeling, choice of analysis metrics, and interpretation/visualization of the provided data.

Introduction to networks; Recap of graph theory; Homophily; The strength of weak ties; Centralities, Robustness; Network Models, Random Networks and Small World Networks; Power Laws and Rich-Get-Richer Phenomena; Communities; Spreading Phenomena; Cascading Behavior in Networks; Epidemics; Practical Network analysis (structural and dynamical) with Gephi & Python/networkx; Introduction to Information Retrieval; Inverted Index; Terms Postings; Document ingestion and nlp pipeline; Practical NLP and information retrieval with SpaCy; Ranked Retrieval; Evaluating search engines; Directed and Weighted Networks; The Structure of the Web; Link Analysis; Pagerank computation; Basics on Language Models and Word Embedding; Word embedding with SpaCy.

Knowledge and understanding: basic algorithms and data structures for Information Retrieval and Complex Networks Analysis.
Applying knowledge and understanding: students will be able to build and use a basic information retrieval system, as well as analyzing networked data, using available open source tools.
Making judgements: students will be able to autonomously evaluate the best techniques to solve information retrieval and network science problems. Communication skills: students will learn to communicate and justify the use of the appropriate technique for a given problem. Learning skills: students will be able to autonomously learn how to refine basic techniques and how to best use the available tools.