{"id":161581,"date":"2011-07-01T00:00:00","date_gmt":"2011-07-01T00:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/msr-research-item\/distributed-ranking-in-networks-with-limited-memory-and-communication-2\/"},"modified":"2018-10-16T20:13:13","modified_gmt":"2018-10-17T03:13:13","slug":"distributed-ranking-in-networks-with-limited-memory-and-communication-2","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/distributed-ranking-in-networks-with-limited-memory-and-communication-2\/","title":{"rendered":"Distributed Ranking in Networks with Limited Memory and Communication"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We consider the problem of information ranking in a network where each node has an individual preference over a set of candidates and the goal for each node is to correctly identify a list of top ranked candidates with respect to aggregate ranking scores. This is to be achieved by a distributed algorithm that uses limited memory per node and limited communication between each pair of nodes.<\/p>\n<p>We show that this problem reduces to a plurality selection problem where the goal for each node is to identify one candidate with the largest aggregate ranking score and we provide an efficient randomized algorithm for this reduction.<\/p>\n<p>We further study a plurality selection algorithm for the general case of m > 1 candidates that is lightweight in using only log<sub>2<\/sub>(m)+1 bits of memory per node and the same amount of bits per node pair-wise communication. We establish correctness of the algorithm for the case of complete graphs with high probability as the number of nodes grows large, and establish tight convergence time bounds.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We consider the problem of information ranking in a network where each node has an individual preference over a set of candidates and the goal for each node is to correctly identify a list of top ranked candidates with respect to aggregate ranking scores. This is to be achieved by a distributed algorithm that uses [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"Microsoft Technical Report","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"MSR-TR-2011-88","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"Kyomin Jung, Bo Young 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