{"id":155148,"date":"2009-02-01T00:00:00","date_gmt":"2009-02-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/less-is-more-sampling-the-neighborhood-graph-makes-salsa-better-and-faster\/"},"modified":"2018-10-16T21:57:50","modified_gmt":"2018-10-17T04:57:50","slug":"less-is-more-sampling-the-neighborhood-graph-makes-salsa-better-and-faster","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/less-is-more-sampling-the-neighborhood-graph-makes-salsa-better-and-faster\/","title":{"rendered":"Less is More: Sampling the Neighborhood Graph Makes SALSA Better and Faster"},"content":{"rendered":"<div class=\"asset-content\">\n<p>In this paper, we attempt to improve the effectiveness and the efficiency of query-dependent link-based ranking algorithms such as HITS, MAX and SALSA. All these ranking algorithms view the results of a query as nodes in the web graph, expand the result set to include neighboring nodes, and compute scores on the induced neighborhood graph. In previous work it was shown that SALSA in particular is substantially more effective than query-independent link-based ranking algorithms such as PageRank. In this work, we show that whittling down the neighborhood graph through consistent sampling of nodes and edges makes SALSA and its cousins both faster (more efficient) and better (more effective). We offer a hypothesis as to why \u201cless is more\u201d, i.e. why using a reduced graph improves performance.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, we attempt to improve the effectiveness and the efficiency of query-dependent link-based ranking algorithms such as HITS, MAX and SALSA. All these ranking algorithms view the results of a query as nodes in the web graph, expand the result set to include neighboring nodes, and compute scores on the induced neighborhood graph. [&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":[{"type":"user_nicename","value":"najork","user_id":"33048"},{"type":"user_nicename","value":"sreenig","user_id":"33705"},{"type":"user_nicename","value":"rina","user_id":"33407"}],"msr_publishername":"Association for Computing Machinery, Inc.","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"2nd ACM International Conference on Web Search and Data Mining (WSDM)","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"Copyright \u00a9 2007 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and\/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212) 869-0481, or permissions@acm.org. 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