MIT ESD 342 - Search and Navigation on Social and other Networks (50 pages)

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Search and Navigation on Social and other Networks



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Search and Navigation on Social and other Networks

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Pages:
50
School:
Massachusetts Institute of Technology
Course:
Esd 342 - Network Representations of Complex Engineering Systems
Network Representations of Complex Engineering Systems Documents

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Lecture 14 Search and Navigation on Social and other Networks Affiliation Hierarchies Christopher L Magee April 4 2006 Professor C Magee 2006 Page 1 Lecture 14 Outline Introductory Remarks Search and Navigation search briefly Navigation Milgram s experiment and critiques Kleinberg s first model The influence of structure and Kleinberg s second model and the Watts Dodds and Newman model Modeling Overview Search navigation as case study of model evolution Modeling limitations and benefits The fundamental modeling tradeoff Systems and applications of most interest Professor C Magee 2006 Page 2 Search and Navigation Search To look over carefully in order to find something to explore to make an effort to find something seek hunt quest Navigate To plan record and control the position of to follow a planned Course or to make one s way Use of maps is not explicitly mentioned but their usage relative to navigation seems more normal than for search Professor C Magee 2006 Page 3 Search and Navigation Search To look over carefully in order to find something to explore to make an effort to find something seek hunt quest Network literature to find the node containing information that is desired Navigate To plan record and control the position of to follow a planned Course or to make one s way Network literature to get from one to another specific node by a the short est path using only local information Professor C Magee 2006 Page 4 Network Search I Exhaustive WWW Search Catalog while crawling the network and create a map local index of the entire network Use information in nodes to select relevant web pages Rank nodes for significance using the link information Eigenvector Centrality Brin and Page Each node has a weight i that is defined to be proportional to the weights of all nodes that point to i And xi 1 j Aij x j x And then Ax x Thus the weights are an eigenvector of the adjacency matrix A with eigenvalue Kleinberg has considered a more sophisticated version with weights



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