Social Network Analysis: Introduction, Network Properties, and ApplicationsSpeaker: Tanmoy Chakraborty – New Delhi, India
Topic(s): Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing
AbstractThe social network, being a major part of online media, has emerged with several avatars in providing a variety of services -- virtual networking sites (e.g., Facebook, LinkedIn), microblogs (e.g., Twitter, Tumblr), community media sites (e.g., YouTube, Flickr, Instagram), social QA sites (e.g., Quora, Yahoo Answers), user reviews (e.g., Yelp, Amazon), social curation sites (e.g., Reddit, Pinterest), location-based social networks (e.g., Foursquare), to name a few. The amount of user bases each of these social networking services accommodates may amaze us and also motivates to know the micro-dynamics underneath such massive ecosystems. Here comes the application of social network analysis.
Why is social network analysis useful? Well, every entity in our world is connected -- People are connected through social networks; they are connected to information, organization, places, etc. One can get a piece of partial information by studying the activities of individuals. To know the complete information, one needs to study the system as a whole, perhaps by modelling it as a network. Social network analysis falls at the intersection of graph theory, mathematics, sociology, and more recently in the last 15-20 years, several developments in fields as diverse as computer science, physics, biology and economics.
The talk will cover recent research on the structure and analysis of such large networks and on models and algorithms that abstract their basic properties. I will present how to practically analyze large-scale network data and how to reason about it through models for network structure and evolution. Topics covered in this talk include structural properties of a network, formation of network communities, how information spreads through society; robustness and fragility of networks; algorithms for the World Wide Web; prediction and recommendation in online social networks; representation learning for large networks; etc.
About this LectureNumber of Slides: 80
Duration: 60 minutes
Languages Available: English
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