This website serves as the official online home for the course CMSC 191: Special Topic — Introduction to Computational Social Network Analysis. It has been created to present the course materials, provide access to the course pack, and showcase the academic endeavors undertaken in this field within UPLB. As the university’s face to the world for this subject area, the website is envisioned not only to inform but also to encourage engagement and collaboration among current students, prospective learners, and researchers in computational sociology and related disciplines.

The study of networks is the study of relationships—how things, people, and ideas connect to form the intricate web that sustains modern society. In this course, computational social network analysis (CSNA) is explored as a field that brings together the precision of algorithms, the interpretive depth of the social sciences, and the ethical reflection of responsible computing.

CSNA provides a language to describe connection and influence. It helps quantify the seemingly unquantifiable: friendships, collaborations, patterns of misinformation, or movements across digital communities. It enables structure to be seen in complexity, and in doing so, to reveal the hidden architectures of the social world.

This course guide serves both as a reference and as a companion. It outlines the path from foundational principles to advanced applications, balancing computation with reflection, and analysis with empathy. Through this journey, students are invited to navigate, model, and interpret the digital networks that both mirror and mold human behavior.

CMSC 191: Special Topic — Introduction to Computational Social Network Analysis introduces students to the theories, algorithms, and ethical practices involved in examining networks of social interaction through computational methods.

Social networks—whether among people, organizations, or technologies—are the connective tissue of our digital society. They shape how ideas spread, how communities form, and how behaviors evolve. Through CSNA, we can analyze these patterns systematically, using graph theory, algorithmic modeling, and data visualization.

Our examples will draw from local and global phenomena. We may examine online misinformation networks to understand how false narratives spread; coordinated influence or “troll” networks to study collective behavior in digital spaces; co-authorship networks to map academic collaboration; and substance distribution networks to explore risk structures and social contagion. Every dataset we touch, however, will be treated with respect for privacy, integrity, and ethical research practice.

By the end of the course, we aim not only to master computational tools but also to cultivate a critical understanding of the social systems these tools help us uncover. As data scientists, we must be both analysts and stewards—translating numerical patterns into humane insight.

By the end of this course, we should be able to:

1. Construct and curate social network datasets using ethical, transparent, and reproducible methods.

2. Derive and implement computational models for analyzing the structural and dynamic properties of social networks.

3. Interpret and evaluate quantitative findings to reveal meaningful social patterns and emergent behaviors.

4. Visualize and communicate network insights through clear, data-driven narratives.

5. Reflect critically on the ethical, societal, and policy implications of social network analytics in the digital age.