Syllabus
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Course Code: MTCE-102 Course Name: Social Networks |
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| MODULE NO / UNIT | COURSE SYLLABUS CONTENTS OF MODULE | NOTES |
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| 1 | Introduction to Social Networks: Introduction, uses, examples and types of social networks, Social and economic
networks, Opportunities and challenges in social networks, Social structure in social networks, Properties of social
networks, algorithmic and economic aspects of social networks Social Network Data: Nodes, Edges, Relationship, Graphs, Samples and Boundaries, Formal methods, Adjacency Matrix for undirected and directed networked graphs and using matrices to represent social relations, Random graphs, Properties of random graphs, Percolations, Branching processes, Growing spanning tree in random graphs. Level in Social Networks: Ego networks, partial networks, complete or global networks, social networks methods including binary or valued, directed or undirected. |
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| 2 | Mining Twitter: Fundamental Twitter Terminology, creating a Twitter API Connection, Exploring Trending Topics,
searching for Tweets, extracting Tweets entities, analyzing Tweets and Tweet entities with frequency analysis, computing
the lexical diversity of Tweets, Examining patterns in Retweets, Visualizing frequency data with histograms. Mining Facebook: Understanding the social graph API, Understanding the open graph protocol, Analyzing social graph connections Mining LinkedIn: Making LinkedIn API requests, Downloading LinkedIn connections as a CSV file, Clustering, normalizing data for analysis, measuring similarity, and clustering algorithms. |
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| 3 | Mining Web pages: Scraping, Parsing and Crawling the Web, Discovering semantics by decoding syntax, Entity-Centric
analysis: A paradigm shift, Quality of analytics for processing human language data. Mining the Semantically Marked-Up Web: Microformats: Easy-to-implement Metadata, Semantics markup to semantic Web: A brief interlude, The semantic Web: An evolutionary revolution. Social Network Analysis: Introduction, History, Metrics in social network analysis (Betweenness, Centrality, Equivalence relation, Centralization, Clustering coefficient and Structural cohesion). |
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| 4 | Structural equivalence, Automorphic equivalence and Regular equivalence | |
