This foundational module introduces students to the intersection of social media platforms and network science. You will explore how social media ecosystems function as complex networks and master fundamental graph theory concepts essential for social media analytics. Key concepts include social media platform typologies, graph structures (nodes, edges, directed/undirected networks), representation methods (adjacency matrices, lists), and ethical data collection practices. Through hands-on demonstrations with NetworkX, you will build practical skills in modelling social media interactions as graphs. This module establishes the theoretical and practical foundation necessary for advanced network analysis in subsequent modules.

This module explores advanced graph types, including bipartite, weighted, temporal, and scale-free networks common in social media platforms. Students implement fundamental graph algorithms like DFS, BFS, and Dijkstra's algorithm for network exploration and shortest path analysis. The module covers network connectivity, components, and global properties such as density and efficiency. Students learn to analyse network structures and understand algorithmic complexity considerations for large-scale social media networks. Practical demonstrations guide students through implementing graph algorithms and analysing real social media network properties using computational tools.

This module focuses on measuring node importance and identifying influential users in social networks. Students master fundamental centrality measures including degree, betweenness, closeness, and PageRank algorithms to analyse user roles and network positions. The module covers local node properties, structural patterns like transitivity and homophily, and link prediction techniques. Students learn to profile users based on multiple network measures and understand social network formation principles. Hands-on demonstrations teach students to compute centrality measures and build comprehensive user analysis systems for social media applications.

This module examines methods for identifying and analysing groups within social networks. Students explore community detection approaches, including modularity-based methods, the Louvain algorithm, and spectral clustering techniques. The module covers overlapping communities, dynamic community evolution, and quality evaluation metrics. Students learn to compare different detection algorithms and understand their strengths and limitations. Applications in targeted marketing, content recommendation, and information flow analysis are emphasised. Practical demonstrations guide students through the implementation of community detection algorithms and the analysis of community structure in real social media networks.

This module studies how information and behaviours spread through social media networks. Students explore diffusion models, including independent cascade and linear threshold mechanisms, along with influence maximisation techniques. The module covers collective behaviours such as herd mentality, echo chambers, and social contagion phenomena. Students learn to detect information cascades, distinguish influence from homophily, and predict viral content. Applications in crisis detection, marketing campaigns, and behaviour prediction are emphasised. Comprehensive demonstrations teach students to simulate diffusion models and analyse real-world information spread patterns.

End-Term Examination