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VERSION:2.0
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SUMMARY:IC Colloquium : Human Behavior in Networks
DTSTART:20160229T101500
DTEND:20160229T113000
DTSTAMP:20260407T025710Z
UID:9d999f80c473b794f1726979d28b6ff81be3bf82ff17560bfa763d9f
CATEGORIES:Conferences - Seminars
DESCRIPTION:By : Robert West - Stanford University\nIC Faculty candidateAb
 stract :\nHumans as well as information are organized in networks. Interac
 ting with these networks is part of our daily lives: we talk to friends in
  our social network\; we find information by navigating the Web\; and we f
 orm opinions by listening to others and to the media. Thus\, understanding
 \, predicting\, and enhancing human behavior in networks poses important r
 esearch problems for computer and data science with practical applications
  of high impact. In this talk I will present some of my work in this area\
 , focusing on (1) human navigation of information networks and (2) person-
 to-person opinions in social networks.\nNetwork navigation constitutes a f
 undamental human behavior: in order to make use of the information and res
 ources around us\, we constantly explore\, disentangle\, and navigate netw
 orks such as the Web. Studying navigation patterns lets us understand bett
 er how humans reason about complex networks and lets us build more human-f
 riendly information systems. As an example\, I will present an algorithm f
 or improving website hyperlink structure by mining raw web server logs. Th
 e resulting system is being deployed on Wikipedia's full server logs at te
 rabyte scale\, producing links that are clicked 10 times as frequently as 
 the average link added by human Wikipedia editors.\nCommunication and coor
 dination through natural language is another prominent human network behav
 ior. Studying the interplay of social network structure and language has t
 he potential to benefit both sociolinguistics and natural language process
 ing. Intriguing opportunities and challenges have arisen recently with the
  advent of online social media\, which produce large amounts of both netwo
 rk and natural language data. As an example\, I will discuss my work on pe
 rson-to-person sentiment analysis in social networks\, which combines the 
 sociological theory of structural balance with techniques from natural lan
 guage processing\, resulting in a machine-learning model for sentiment pre
 diction that clearly outperforms both text-only and network-only versions.
 \nI will conclude the talk by sketching interesting future directions for 
 computational approaches to studying and enhancing human behavior in netwo
 rks.Bio :\nRobert West is a sixth-year Ph.D. candidate in Computer Science
  in the Infolab at Stanford University\, advised by Jure Leskovec. His res
 earch aims to understand\, predict\, and enhance human behavior in social 
 and information networks by developing techniques in data science\, data m
 ining\, network analysis\, machine learning\, and natural language process
 ing. Previously\, he obtained a Master's degree from McGill University in 
 2010 and a Diplom degree from Technische Universität München in 2007.Mor
 e information
LOCATION:BC 420 https://plan.epfl.ch/?room==BC%20420
STATUS:CONFIRMED
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