Mining Large-Scale Network Data

Big Data Series

Professor Jure Leskovec
Professor, Stanford University
Given on: Jan. 30th, 2014


Massive networks are all around us: social networks allow for information and influence flow through society, viruses become epidemics by spreading through networks, and networks of neurons allow us think and function. With the recent technological advances and the development of online social media we can study networks that were once essentially invisible to us. In this talk we discuss how computational perspectives and machine learning models can be developed to abstract networked phenomena like: How will a community or a social network evolve in the future? What are social circles a person belongs to? What kinds of network structures are there and how can they be modeled?


Jure Leskovec is assistant professor of Computer Science at Stanford University. His research focuses on mining large social and information networks. Problems he investigates are motivated by large scale data, the Web and on-line media. This research has won several awards including a Microsoft Research Faculty Fellowship, the Alfred P. Sloan Fellowship and numerous best paper awards. Leskovec received his bachelor's degree in computer science from University of Ljubljana, Slovenia, and his PhD in in machine learning from the Carnegie Mellon University and postdoctoral training at Cornell University. You can follow him on Twitter @jure.