{"id":12784,"date":"2021-11-21T16:51:05","date_gmt":"2021-11-21T21:51:05","guid":{"rendered":"https:\/\/carleton.ca\/scs\/?page_id=12784"},"modified":"2021-11-21T16:51:15","modified_gmt":"2021-11-21T21:51:15","slug":"tr-95-13-efficient-computation-of-implicit-representations-of-sparse-graph","status":"publish","type":"page","link":"https:\/\/carleton.ca\/scs\/research\/scs-technical-reports\/technical-reports-1995\/tr-95-13-efficient-computation-of-implicit-representations-of-sparse-graph\/","title":{"rendered":"TR-95-13: Efficient Computation of Implicit Representations of Sparse Graph"},"content":{"rendered":"

Carleton University
\nTechnical Report<\/a> TR-95-13
\nMay 1995<\/p>\n

Efficient Computation of Implicit Representations of Sparse Graph<\/h2>\n
\n
\n
\n
\n
Srinivasa R. Arikati, Anil Maheshwari, Christos D. Zaroliagis<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
\n

Abstract<\/h3>\n<\/div>\n

The problem of \fnding an implicit representation for a graph such that vertex ad- jacency can be tested quickly is fundamental to all graph algorithms. In particular, it is possible to represent sparse graphs on n vertices using O(n) space such that vertex adjacency is tested in O(1) time. We show here how to construct such a representation e\u000eciently by providing simple and optimal algorithms, both in a sequential and a par- allel setting. Our sequential algorithm runs in O(n) time. The parallel algorithm runs in O(logn) time using O(n=logn) CRCW PRAM processors, or in O(logn log\u0003 n) time using O(n= logn log\u0003 n) EREW PRAM processors. Previous results for this problem are based on matroid partitioning and thus have a high complexity.<\/p>\n

TR-95-13.pdf<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Carleton University Technical Report TR-95-13 May 1995 Efficient Computation of Implicit Representations of Sparse Graph Srinivasa R. Arikati, Anil Maheshwari, Christos D. Zaroliagis Abstract The problem of \fnding an implicit representation for a graph such that vertex ad- jacency can be tested quickly is fundamental to all graph algorithms. In particular, it is possible to […]<\/p>\n","protected":false},"author":49,"featured_media":0,"parent":11736,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","_mi_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":"","_links_to":"","_links_to_target":""},"yoast_head":"\nTR-95-13: Efficient Computation of Implicit Representations of Sparse Graph - School of Computer Science<\/title>\n<meta name=\"description\" content=\"Carleton University Technical Report TR-95-13 May 1995 Efficient Computation of Implicit Representations of Sparse Graph Srinivasa R. 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