Now showing items 2-6 of 6

    • Graphical Multi-Task Learning 

      Sheldon, Daniel (2008-10-31)
      We investigate the problem of learning multiple tasks that are related according to a network structure, using the multi-task kernel framework proposed by Evgeniou, Micchelli and Pontil. Our method combines a graphical ...
    • Manipulation Of Pagerank And Collective Hidden Markov Models 

      Sheldon, Daniel (2010-04-09)
      The first part of this thesis explores issues surrounding the manipulation of PageRank, a popular link-analysis based reputation system for the web. PageRank is an essential part of web search, but it is also subject to ...
    • Manipulation-resistant Reputations Using Hitting Time 

      Hopcroft, John; Sheldon, Daniel (Cornell University, 2007-07-03)
      Popular reputation systems for linked networks can be manipulated by spammers who strategically place links. The reputation of node v is interpreted as the world's opinion of v's importance. In PageRank, v's own opinion ...
    • Network Reputation Games 

      Hopcroft, John; Sheldon, Daniel (2008-10-31)
      Originally, hyperlinks on the web were placed for organic reasons, presumably to aid navigation or identify a resource deemed relevant by the human author. However, link-based reputation measures used by search engines ...
    • Optimal Network Design for the Spread of Cascades 

      Sheldon, Daniel; Dilkina, Bistra; Elmachtoub, Adam; Finseth, Ryan; Sabharwal, Ashish; Conrad, Jon; Gomes, Carla P.; Shmoys, David; Allen, Will; Amundsen, Ole; Vaughan, Buck (2010-04-10)
      We introduce a new optimization framework to maximize the expected spread of cascades in networks. Our model allows a rich set of actions that directly manipulate cascade dynamics by adding nodes or edges to the network. ...