CMSC 191: Computational Social Network Analysis
Social Networks and Graphs
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This topic introduces the mathematical and computational framework that enables the abstraction of social relations into formal graph structures. Real-world systems are described as graphs G = (V, E), where nodes represent actors and edges encode interactions. The translation of qualitative relationships into adjacency matrices or edge lists is explained as the foundation of algorithmic computation in social network analysis.
Core graph theory concepts—paths, cycles, connectivity, degree, and geodesic distance—are elaborated to demonstrate how mathematical precision transforms social meaning into measurable form. The analysis of structural characteristics, including density, degree distribution, and diameter, is shown to reveal patterns of cohesion, fragmentation, and inequality in social systems. Finally, the use of NetworkX and Pajek is highlighted as essential for computational modeling, emphasizing that efficient data representation and algorithm selection determine analytical scalability.
Learning Outcomes
Learning Outcomes
Define the mathematical structures that represent social relationships as graphs.
Illustrate the components of a network and their relevance to social interpretation.
Relate structural properties of graphs to the social meanings they encode.
Guide Questions
Guide Questions
How do nodes and edges serve as abstractions of human or institutional interactions?
What kinds of social insights can be drawn from measures such as degree and density?
How does representing relationships mathematically alter the study of social systems?
Topic Outline
Topic Outline
Social Networks and Graphs* (class handout)
Translating Society into Structure
Basic Elements of a Network: Nodes and Edges
Modeling Real Systems as G = (V, E)
Abstraction in Action: From Data to Graph Objects
Graph Theory as the Mathematical Backbone
Formalizing Structural Concepts
Linking Mathematical Precision to Social Meaning
Structural Characteristics of Social Networks
Density, Degree Distribution, and Diameter: The Big Three
Interpreting Cohesion and Fragmentation
Essential Software Toolkit
Pajek: Software | Companion Book: Exploratory Social Network Analysis with Pajek
Reading the Social Code
Note: Links marked with an asterisk (*) lead to materials accessible only to members of the University community. Please log in with your official University account to view them.
The semester at a glance:
Social Networks & Graphs
Validity and Reliability . . .
Project Development . . .
Implementation . . .
Reading List
Reading List
Barabási, Albert-László, and Réka Albert. "Emergence of scaling in random networks." Science 286, no. 5439 (1999): 509-512.
Burt, Ronald S. Structural Holes: The Social Structure of Competition. Harvard University Press, 1992.
Granovetter, Mark S. "The strength of weak ties." American Journal of Sociology 78, no. 6 (1973): 1360-1380.
Wasserman, Stanley, and Katherine Faust. Social Network Analysis: Methods and Applications. Cambridge University Press, 1994. (Core Text)
Access Note: Published research articles and books are linked to their respective sources. Some materials are freely accessible within the University network or when logged in with official University credentials. Others will be provided to enrolled students through the class learning management system (LMS).
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