Requirements

Grades are computed from three main components:

• Project Proposal (25%)
  Ethical dataset construction, problem definition, and literature review

• Project Implementation (25%)
  Coding, computational analysis, reproducibility, and validation

• Final Report and Presentation (50%)
  Comprehensive analysis, visualization, reflection, and oral defense

The goal of our projects is to gain hands-on experience in mastering the art and science of computational social network analysis. We will each work on individual projects, but learning should remain a collective endeavor.

We are encouraged to collaborate through idea exchange—to discuss algorithms, clarify concepts, critique methodologies, and share insights. Two or more students may explore the same dataset as long as they pursue distinct analytical questions (for instance, one studying prominence while another focuses on centrality).

However, the code, analysis scripts, and written reports must be produced individually. Collaboration is about connecting nodes of thought, not merging them.

In this course, our networks are built on curiosity and generosity. Each conversation is a link, each insight a new edge connecting us toward collective understanding. Let collaboration be the method through which we become a learning network ourselves.

• Patterns of Internet-based friendship (AREVALO, Chezka Camille P.)

• A Software for inferring citation networks from research papers and journals (BALANDRA, Rene James Jr. P.)

• Identifying noeuds de liaison in networks (JEREMIE, Kambale Simisi)

• Measuring the speed and width of information spread on the Internet: Digg as a case study (SALVANIA, Abigail C.)

• Constructing the Filipino folksonomies from the web (TABUAN, Fatima N.)

• Structural characterization and dynamics of the Samahang Pisika ng Pilipinas (SPP) collaboration network (VILLANUEVA, Kayvee D.)

• Replicating Milgram's small world experiment through Facebook (MORIN, Eliana Katarina B. & PAGSINOHIN, Pierre Alexis M., while under the Science Immersion Program of the Philippine Science High School)

To analyze a network is to see beyond the surface of data—to witness the patterns that shape collective life. Through the lenses of computation and social inquiry, we come to understand not just how networks form, but why they matter.

As we conclude this course, remember that every node you model represents a story, a choice, a relationship. Every edge is a pathway of influence and exchange. The power of CSNA lies not only in quantifying these patterns but in interpreting them responsibly, compassionately, and critically.

Our task as computational scientists is not merely to compute connections, but to comprehend connection itself—to transform raw data into understanding, and understanding into wisdom.

To study networks is to study ourselves—how we connect, influence, and evolve in the vast architecture of the digital world.