Network Analysis and Graph Machine Learning

University of Oulu

This course will focus on the representation and statistical analysis of large networks, alongside traditional and deep learning techniques applied to graphs. It will explore various use cases of Graph Machine Learning across diverse domains such as Natural Language Processing, Social Network Analysis, Finance, and Computational Biology.

Many real-world systems can be modeled as networks of interconnected entities, where the number of entities can be vast, forming large-scale networks. Examples include knowledge graph entities, keyword co-occurrence graphs in natural language, user interaction graphs in social networks, protein-protein interaction networks, Internet router networks, IoT edge devices, and financial transaction networks. Analyzing these networks is essential for relational learning tasks and creating frameworks that capture the intrinsic structure of data. This course will primarily cover methods for studying the properties of such large networks and applying advanced machine-learning techniques to these networks for various downstream applications.

By the end of the course, participants are expected to achieve the following outcomes:

• Represent and analyze graph structures effectively.
• Identify significant and recurring patterns in data using network motifs and community structures.
• Understand and apply traditional machine learning techniques to graph data.
• Utilize graph-structured data to enhance predictive capabilities through graph neural networks.

Course Contents:

The course will cover the following topics:

• Fundamentals of graphs and essential background knowledge
• Characterization of graph properties, including measures and metrics
• Basics of spectral graph analysis and its applications, such as graph partitioning and community detection
• Overview of machine learning applications on graphs, including shallow and deep learning techniques for generating node, link, and graph embeddings
• Use cases of graph neural networks across various domains

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