COMP7108A - Network data analytics

Semester 2, 2025-26

Instructor
Nikos Mamoulis
Syllabus In the era of data, numerous real-world applications are best represented as networks. This perspective is vital as analyzing these networks can uncover valuable insights, extract interesting information, and make informed decisions. Modern technologies have significantly enhanced our ability to access vast volumes of data, simplifying and reducing the cost of storage. Understanding the importance of data is crucial in addressing diverse challenges, such as traffic congestion, financial network fraud detection, and the spread of misinformation in social networks, to name a few. Consequently, there is an increasing necessity to develop advanced tools that can address these challenges and further understand the importance of data is more necessary than ever. Examples of these technologies can be machine learning techniques (e.g., modeling different problems using GNNs), and natural language processing (NLP) techniques (text preprocessing and sentiment analysis).
Introduction by Professor The main objective of this course is to provide a comprehensive analysis of data management tasks and resources, with a focus on effectively and efficiently working with big data. Specifically, this course will review state-of-the-art technologies for managing and storing data, as well as different approaches to data representation. In the era of big data analytics, many real-world applications can be represented as networks, for example, financial networks where nodes represent users and edges represent monetary transactions. Such network representations enable us to uncover valuable insights, extract meaningful information, and make informed decisions. This course covers big data systems (Apache Spark, Apache Flink), provenance analytics for graphs, and various types of network analysis. We will also explore different database paradigms, including graph databases (Neo4j, TigerGraph), relational databases (PostgreSQL, DuckDB), and NoSQL databases (MongoDB, Cassandra).
Learning Outcomes
Course Learning Outcomes
CLO1. Work with big data processing frameworks for data analysis
CLO2. Apply graph data structures and algorithms for graph analytics
CLO3. Evaluate and implement provenance tracking methods on graph data
CLO4. Perform flow analytics on graphs and analyze spatiotemporal data using graph databases
CLO5. Select and utilize appropriate database technologies to efficiently store and manage data
View Programme Learning Outcomes - MSc(CompSc)
Pre-requisites Very good knowledge of programming (Python and C are recommended) and knowledge of fundamental data science concepts and techniques (e.g. linear algebra)
Compatibility -
Topics covered
Course Content No. of Hours Course Learning Outcomes
1. Introduction to Network Data Analytics 3 CLO1, CLO2, CLO3, CLO4, CLO5
2. A Comprehensive Review of Different Databases 3 CLO1, CLO5
3. Big Data Analytics Systems 4 CLO1
4. Data Provenance and Graph Analytics 4 CLO3
5. Graph Data Management and Graph Databases 4 CLO2, CLO4
6. Flow Analytics on Graphs and Spatiotemporal Data Mining 3 CLO3, CLO4
7. An Introduction in Temporal Interaction Networks 3 CLO3, CLO4
8. Spatiotemporal Pattern Analysis and Trajectories 3 CLO3, CLO4
9. Future Topics in Network Data Analytics 3 CLO1, CLO2, CLO3, CLO4, CLO5
     
 
Assessment
Description Type Weighting * Tentative Assessment Period /
Examination Period ^		 Course Learning Outcomes
Assignments Continuous Assessment 50% - Hands-on practice on the technology the student will be taught during the class

CLO1, CLO2, CLO5
Written examination covering all the taught contents in the course Written Examination 50% 7 - 26 May 2026 An overview of the topics have been taught during the class
CLO1, CLO2, CLO3, CLO4, CLO5
* The weighting of coursework and examination marks is subject to approval
^ The exact examination date is typically announced by the Examinations Office seven weeks prior to the scheduled exam date (three weeks for the summer semester). Students are obliged to follow the examination schedule. If you are unsure of your availability during the examination period, you should NOT enroll in the course. Absence from the examination may result in failure of the course. Please note that there is no supplementary examination for this course.
Course materials

 
Session dates
Date Time Venue Remark
Session 1 13 Feb 2026 (Fri) 9:00am - 12:00pm Online
Session 2 14 Feb 2026 (Sat) 10:00am - 1:00pm Online
Session 3 14 Feb 2026 (Sat) 7:00pm - 10:00pm Online
Session 4 15 Feb 2026 (Sun) 10:00am - 1:00pm Online
Session 5 15 Feb 2026 (Sun) 2:00pm - 5:00pm Online
Session 6 27 Feb 2026 (Fri) 9:00am - 12:00pm CYP-P2
Session 7 28 Feb 2026 (Sat) 10:00am - 1:00pm CYP-P2
Session 8 28 Feb 2026 (Sat) 7:00pm - 10:00pm CYP-P2
Session 9 1 Mar 2026 (Sun) 2:00pm - 5:00pm CYP-P2
Session 10 1 Mar 2026 (Sun) 7:00pm - 10:00pm CYP-P2
Exam 21 May 2026 (Thu) 6:30pm - 8:30pm Pls refer to Exams Office's website
CYP - Chong Yuet Ming Building
Add/drop5 January, 2026 - 14 February, 2026