Fast recommendation on bibliographic networks with sparse-matrix ordering and partitioning
Graphs and matrices are widely used in algorithms for social network analyses. Since the number of interactions is much less than the possible number of interactions, the graphs and matrices used in the analyses are usually sparse. In this paper, we propose an efficient implementation of a sparse-ma...
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| Veröffentlicht in: | Social network analysis and mining 2013-12, Vol.3 (4), p.1097-1111 |
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| creator | Küçüktunç, Onur Kaya, Kamer Saule, Erik Çatalyürek, Ümit V. |
| description | Graphs and matrices are widely used in algorithms for social network analyses. Since the number of interactions is much less than the possible number of interactions, the graphs and matrices used in the analyses are usually sparse. In this paper, we propose an efficient implementation of a sparse-matrix computation which arises in our publicly available citation recommendation service the
advisor
as well as in many other recommendation systems. The recommendation algorithm uses a sparse matrix generated from the citation graph. We observed that the nonzero pattern of this matrix is highly irregular and the computation suffers from high number of cache misses. We propose techniques for storing the matrix in memory efficiently and we reduced the number of cache misses with ordering and partitioning. Experimental results show that our techniques are highly efficient in reducing the query processing time which is highly crucial for a web service. |
| doi_str_mv | 10.1007/s13278-013-0106-z |
| format | Article |
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advisor
as well as in many other recommendation systems. The recommendation algorithm uses a sparse matrix generated from the citation graph. We observed that the nonzero pattern of this matrix is highly irregular and the computation suffers from high number of cache misses. We propose techniques for storing the matrix in memory efficiently and we reduced the number of cache misses with ordering and partitioning. Experimental results show that our techniques are highly efficient in reducing the query processing time which is highly crucial for a web service.</description><identifier>ISSN: 1869-5450</identifier><identifier>EISSN: 1869-5469</identifier><identifier>DOI: 10.1007/s13278-013-0106-z</identifier><language>eng</language><publisher>Vienna: Springer Vienna</publisher><subject>Algorithms ; Applications of Graph Theory and Complex Networks ; Bibliographic coupling ; Citation analysis ; Computation ; Computer Science ; Data Mining and Knowledge Discovery ; Economics ; Game Theory ; Graphs ; Humanities ; Law ; Matrices ; Methodology of the Social Sciences ; Network analysis ; Original Article ; Partitioning ; Query processing ; Recommender systems ; Social and Behav. Sciences ; Social network analysis ; Social networks ; Sparse matrices ; Sparsity ; Statistics for Social Sciences ; Web services</subject><ispartof>Social network analysis and mining, 2013-12, Vol.3 (4), p.1097-1111</ispartof><rights>Springer-Verlag Wien 2013</rights><rights>Springer-Verlag Wien 2013.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-8038ae1efb521ff8a0c30bd8881c45c8da4d2cbdf9fce84af7bcc65a78fb38c33</citedby><cites>FETCH-LOGICAL-c316t-8038ae1efb521ff8a0c30bd8881c45c8da4d2cbdf9fce84af7bcc65a78fb38c33</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s13278-013-0106-z$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2920208759?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,776,780,21367,27901,27902,33721,41464,42533,43781,51294</link.rule.ids></links><search><creatorcontrib>Küçüktunç, Onur</creatorcontrib><creatorcontrib>Kaya, Kamer</creatorcontrib><creatorcontrib>Saule, Erik</creatorcontrib><creatorcontrib>Çatalyürek, Ümit V.</creatorcontrib><title>Fast recommendation on bibliographic networks with sparse-matrix ordering and partitioning</title><title>Social network analysis and mining</title><addtitle>Soc. Netw. Anal. Min</addtitle><description>Graphs and matrices are widely used in algorithms for social network analyses. Since the number of interactions is much less than the possible number of interactions, the graphs and matrices used in the analyses are usually sparse. In this paper, we propose an efficient implementation of a sparse-matrix computation which arises in our publicly available citation recommendation service the
advisor
as well as in many other recommendation systems. The recommendation algorithm uses a sparse matrix generated from the citation graph. We observed that the nonzero pattern of this matrix is highly irregular and the computation suffers from high number of cache misses. We propose techniques for storing the matrix in memory efficiently and we reduced the number of cache misses with ordering and partitioning. Experimental results show that our techniques are highly efficient in reducing the query processing time which is highly crucial for a web service.</description><subject>Algorithms</subject><subject>Applications of Graph Theory and Complex Networks</subject><subject>Bibliographic coupling</subject><subject>Citation analysis</subject><subject>Computation</subject><subject>Computer Science</subject><subject>Data Mining and Knowledge Discovery</subject><subject>Economics</subject><subject>Game Theory</subject><subject>Graphs</subject><subject>Humanities</subject><subject>Law</subject><subject>Matrices</subject><subject>Methodology of the Social Sciences</subject><subject>Network analysis</subject><subject>Original Article</subject><subject>Partitioning</subject><subject>Query processing</subject><subject>Recommender systems</subject><subject>Social and Behav. Sciences</subject><subject>Social network analysis</subject><subject>Social networks</subject><subject>Sparse matrices</subject><subject>Sparsity</subject><subject>Statistics for Social Sciences</subject><subject>Web services</subject><issn>1869-5450</issn><issn>1869-5469</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp1UE1LAzEQDaJgqf0B3gKeVyfJfmSPUqwKBS968RKy2aRN7W7WJKXqrzfLip6EGWaYee8N8xC6JHBNAKqbQBiteAaEpYQy-zpBM8LLOivysj797Qs4R4sQdgAJxVgN5Qy9rmSI2Gvluk73rYzW9ThFY5u9dRsvh61VuNfx6PxbwEcbtzgM0geddTJ6-4Gdb7W3_QbLvsVpE-2okQYX6MzIfdCLnzpHL6u75-VDtn66f1zerjPFSBkzDoxLTbRpCkqM4RIUg6blnBOVF4q3Mm-palpTG6V5Lk3VKFUWsuKmYVwxNkdXk-7g3ftBhyh27uD7dFLQmgIFXhV1QpEJpbwLwWsjBm876T8FATG6KCYXRXJRjC6Kr8ShEycM44fa_yn_T_oGAvN4LA</recordid><startdate>20131201</startdate><enddate>20131201</enddate><creator>Küçüktunç, Onur</creator><creator>Kaya, Kamer</creator><creator>Saule, Erik</creator><creator>Çatalyürek, Ümit V.</creator><general>Springer Vienna</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>0-V</scope><scope>3V.</scope><scope>7XB</scope><scope>88J</scope><scope>8BJ</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ALSLI</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FQK</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JBE</scope><scope>JQ2</scope><scope>K7-</scope><scope>M2R</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope></search><sort><creationdate>20131201</creationdate><title>Fast recommendation on bibliographic networks with sparse-matrix ordering and partitioning</title><author>Küçüktunç, Onur ; Kaya, Kamer ; Saule, Erik ; Çatalyürek, Ümit V.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-8038ae1efb521ff8a0c30bd8881c45c8da4d2cbdf9fce84af7bcc65a78fb38c33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Algorithms</topic><topic>Applications of Graph Theory and Complex Networks</topic><topic>Bibliographic coupling</topic><topic>Citation analysis</topic><topic>Computation</topic><topic>Computer Science</topic><topic>Data Mining and Knowledge Discovery</topic><topic>Economics</topic><topic>Game Theory</topic><topic>Graphs</topic><topic>Humanities</topic><topic>Law</topic><topic>Matrices</topic><topic>Methodology of the Social Sciences</topic><topic>Network analysis</topic><topic>Original Article</topic><topic>Partitioning</topic><topic>Query processing</topic><topic>Recommender systems</topic><topic>Social and Behav. 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advisor
as well as in many other recommendation systems. The recommendation algorithm uses a sparse matrix generated from the citation graph. We observed that the nonzero pattern of this matrix is highly irregular and the computation suffers from high number of cache misses. We propose techniques for storing the matrix in memory efficiently and we reduced the number of cache misses with ordering and partitioning. Experimental results show that our techniques are highly efficient in reducing the query processing time which is highly crucial for a web service.</abstract><cop>Vienna</cop><pub>Springer Vienna</pub><doi>10.1007/s13278-013-0106-z</doi><tpages>15</tpages></addata></record> |
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| subjects | Algorithms Applications of Graph Theory and Complex Networks Bibliographic coupling Citation analysis Computation Computer Science Data Mining and Knowledge Discovery Economics Game Theory Graphs Humanities Law Matrices Methodology of the Social Sciences Network analysis Original Article Partitioning Query processing Recommender systems Social and Behav. Sciences Social network analysis Social networks Sparse matrices Sparsity Statistics for Social Sciences Web services |
| title | Fast recommendation on bibliographic networks with sparse-matrix ordering and partitioning |
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