https://mathresearch.utsa.edu/wiki/index.php?action=history&feed=atom&title=Graph_Theory_and_Social_Network_AnalysisGraph Theory and Social Network Analysis - Revision history2026-06-11T22:16:17ZRevision history for this page on the wikiMediaWiki 1.34.1https://mathresearch.utsa.edu/wiki/index.php?title=Graph_Theory_and_Social_Network_Analysis&diff=5100&oldid=prevJuan.gutierrez3: Created page with "Lesson 14 of MDC1213 Slide 1: Title Slide Graph Theory and Social Network Analysis Slide 2: Course Overview Brief summary of the course objectives and lesson t..."2023-03-29T15:59:12Z<p>Created page with "Lesson 14 of <a href="/wiki/index.php?title=MDC1213" title="MDC1213">MDC1213</a> Slide 1: Title Slide Graph Theory and Social Network Analysis Slide 2: Course Overview Brief summary of the course objectives and lesson t..."</p>
<p><b>New page</b></p><div>Lesson 14 of [[MDC1213]]<br />
<br />
Slide 1: Title Slide<br />
<br />
Graph Theory and Social Network Analysis<br />
<br />
Slide 2: Course Overview<br />
<br />
Brief summary of the course objectives and lesson topics<br />
<br />
Slide 3: Introduction<br />
<br />
The significance of graph theory and social network analysis in understanding complex systems<br />
<br />
Slide 4: Defining Graph Theory<br />
<br />
Explanation of graph theory and its applications in various fields<br />
<br />
Slide 5: Graph Structures<br />
<br />
Types of graph structures, such as directed and undirected graphs, weighted and unweighted graphs, and bipartite graphs<br />
<br />
Slide 6: Graph Representations<br />
<br />
Representing graphs using adjacency matrices and lists, and other methods<br />
<br />
Slide 7: Graph Algorithms<br />
<br />
Examples of graph algorithms, such as Dijkstra's shortest path algorithm, Kruskal's minimum spanning tree algorithm, and the PageRank algorithm<br />
<br />
Slide 8: Network Analysis<br />
<br />
Understanding networks as interconnected systems<br />
<br />
Slide 9: Social Network Analysis<br />
<br />
Using graph theory to analyze social networks and relationships<br />
<br />
Slide 10: Degree Centrality<br />
<br />
Identifying influential nodes in a network using degree centrality<br />
<br />
Slide 11: Betweenness Centrality<br />
<br />
Identifying key nodes in a network that act as bridges using betweenness centrality<br />
<br />
Slide 12: Closeness Centrality<br />
<br />
Identifying nodes that are central to the network in terms of closeness using closeness centrality<br />
<br />
Slide 13: Eigenvector Centrality<br />
<br />
Identifying influential nodes that are connected to other influential nodes using eigenvector centrality<br />
<br />
Slide 14: Community Detection<br />
<br />
Identifying communities or groups of nodes in a network using community detection algorithms<br />
<br />
Slide 15: Small-World Networks<br />
<br />
Understanding small-world networks and their significance in social network analysis<br />
<br />
Slide 16: Scale-Free Networks<br />
<br />
Understanding scale-free networks and their significance in social network analysis<br />
<br />
Slide 17: Network Dynamics<br />
<br />
Analyzing changes in network structures over time<br />
<br />
Slide 18: Diffusion and Contagion<br />
<br />
Understanding the spread of ideas, behaviors, and diseases through networks using diffusion and contagion models<br />
<br />
Slide 19: Computational Social Science<br />
<br />
Applying social network analysis and graph theory in computational social science research<br />
<br />
Slide 20: Real-World Applications<br />
<br />
Examples of social network analysis in various fields, such as epidemiology, marketing, and political science<br />
<br />
Slide 21: Ethical Considerations<br />
<br />
Ethical considerations in the use and interpretation of social network analysis in various fields<br />
<br />
Slide 22: Privacy and Data Protection<br />
<br />
Addressing privacy concerns in social network analysis and data protection<br />
<br />
Slide 23: Bias and Discrimination<br />
<br />
Addressing issues of bias and discrimination in social network analysis<br />
<br />
Slide 24: Network Visualization<br />
<br />
Techniques for visualizing and communicating network data<br />
<br />
Slide 25: The Future of Graph Theory and Social Network Analysis<br />
<br />
Predictions for the future of graph theory and social network analysis<br />
<br />
Slide 26: Interdisciplinary Connections<br />
<br />
The connections between graph theory and social network analysis with other fields, such as computer science, mathematics, and sociology<br />
<br />
Slide 27: The Importance of Collaboration<br />
<br />
Collaborative efforts in graph theory and social network analysis research<br />
<br />
Slide 28: The Role of Technology<br />
<br />
The use of technology in advancing graph theory and social network analysis<br />
<br />
Slide 29: Overcoming Challenges<br />
<br />
Strategies for overcoming challenges in graph theory and social network analysis research<br />
<br />
Slide 30: Conclusion<br />
<br />
Recap of the importance of graph theory and social network analysis in understanding complex systems and analyzing social networks.</div>Juan.gutierrez3