Topics in Social Network Analysis, PhD Syllabus

This course is designed for PhD students in management, organizational behavior and strategy who are interested in applying network ideas in their research. The course will provide an introduction to applied network theory and empirical methods. Over the 6 sessions of the course, students will learn:

  • The basic building blocks of most network theories and how they have been applied in various empirical contexts.
  • How to collect network data, visualize it and calculate basic network statistics.
  • Formulate and test hypotheses drawing on network mechanisms.
  • Understand the broad uses of network analysis in the study of organizations and strategy.

Course Requirements

  • Attendance and Participation (30% of grade)
  • Theoretical Integration Paper (30% of grade)
  • Research Proposal (40% of grade)

April 28, The Foundations of Social Network Analysis

New knowledge is anything that allows you to predict some outcome more accurately than before. The enterprise of network analysis is one example of a focused search for new knowledge. Network scholars seek to find patterns in human relationships that explain important outcomes—health, economic, and political—that are ignored, non-obvious, or run counter to conventional wisdom. The readings for this class helped set the stage for the network revolution in the social sciences. They articulate, very clearly, what our prior assumptions were about how the world worked, and systematically showed us that we should think differently.

Check out the post on how to get started with network analysis in R. 

Readings:


May 5, Network Position and Performance

Part 1: Structural Holes; Part 2: Status

The most frequent use of network analysis has been to examine the relationship between network “position” and the performance of people and organizations. This line of research has produced exciting and important ideas, including those of structural holes, status, and closure. Network ideas have also helped scholars reformulate ideas about power, leadership and identity. The readings from this class will introduce you to some of the central ideas about network positions and their relationship to performance outcomes such as innovation or promotion.

Readings:


May 12, Peer Effects

Theories of network positions are built upon individual-level assumptions regarding informational content and knowledge transfer. Yet, until recently, rigorous empirical evidence for information transfer and learning at the dyadic level has been scarce. In this class we will dig deeper into the growing literature on peer effects and examine when we can expect to observe knowledge transfer, and how to evaluate the quality of evidence.

Readings:


May 19, Network Formation

Are there general patterns in how networks are shaped? What forces lead these patterns to emerge and what are the implications for social processes that we care about (e.g., the generation of innovations)? In this class we will cover some core ideas behind the formation of social networks including homophily, triadic closure, reciprocity, and at the macro-scale small worlds and clusters.

Readings:


May 26, Network Cognition, Activation and Team Structures

There is a lot more to networks than classical formulations of network effects as “positions” or as “peer effects.” Scholars have creatively shown that how people perceive networks also affects their performance, how the overall structure of a team’s internal and external networks affects team outcomes.

Readings:


June 2, The Future of Network Analysis

Your final project presentations go here.


Syllabus header information

Stanford University
Graduate School of Business

OB622, Spring (Second half) 2017

Professor: Sharique Hasan, Associate Prof. of Organizational Behavior, Stanford GSB
Office: W239 (KMC, Stanford, CA)
Email: [firstname]@stanford.edu

Times:  Friday from 1:30 to 4:20 PM
Room: GSB Bass 301

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