Social Network Analysis and Social Phenomena - online

Why do our friends often know one another, even though we are only a few connections away from people in very different social circles? Why does information spread easily through some networks, while changes in behaviour require repeated encouragement? Who should be targeted in an intervention, and how does the answer depend on what we want to achieve?

Social network analysis offers a theoretical framework and a methodology to answer questions like these by focusing on the relationships between and among social entities. A network is a structure composed of units and the relationships that connect them. The position of these units and the overall structure help us understand how social ties form, how information and behaviour spread, and how individuals gain access to resources and opportunities.

The focus of the course is on the connection between the mathematical foundations of networks and the social phenomena we want to understand. We will introduce concepts from matrix algebra and graph theory across both days, and use empirical examples and practical exercises to examine what these concepts tell us about social life. We will also discuss how network data are collected and what evidence is needed to distinguish between different explanations for the patterns we observe.

The course covers:

• Mathematical foundations of social networks: matrix algebra and graph theory;

• Network structure, cohesion and models of network formation;

• Centrality, betweenness and brokerage;

• Contagion, diffusion and network interventions;

• Network data, sampling and research design

Learning outcomes:

• Develop an understanding of social phenomena through social network theory;
• Learn how to use and interpret graph-theoretic and matrix algebra concepts with real-world data;
• Understand how local patterns of connection contribute to the overall structure of a network;
• Examine how network position and structure affect social processes;
• Acquire knowledge of network data collection and the implications of sampling and missing data;
• Assess competing explanations for observed network patterns and identify the evidence needed to investigate them.

The course is aimed researchers and analysts interested in using social network analysis to understand social phenomena, including students, academics, government researchers and researchers in third sector organisations. The course is relevant to work in sociology, survey research, public health and related disciplines.

Pre-requisites

Basic familiarity with data matrices and introductory statistical concepts. No previous training in social network analysis is required. Mathematical concepts will be introduced as they are used throughout the course. Practical exercises and demonstrations will accompany the theoretical sessions. Software requirements will be circulated before the course.

THIS COURSE IS BEING DELIVERED OVER TWO DAYS.