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Content Provider | ACM Digital Library |
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Author | Gebremedhin, Assefaw H. Gleich, David F. Patwary, Md. Mostofa Ali Rossi, Ryan A. |
Abstract | We propose a fast, parallel maximum clique algorithm for large sparse graphs that is designed to exploit characteristics of social and information networks. Despite clique's status as an NP-hard problem with poor approximation guarantees, our method exhibits nearly linear runtime scaling over real-world networks ranging from 1000 to 100 million nodes. In a test on a social network with 1.8 billion edges, the algorithm finds the largest clique in about 20 minutes. Key to the efficiency of our algorithm are an initial heuristic procedure that finds a large clique quickly and a parallelized branch and bound strategy with aggressive pruning and ordering techniques. We use the algorithm to compute the largest temporal strong components of temporal contact networks. |
Starting Page | 365 |
Ending Page | 366 |
Page Count | 2 |
File Format | |
ISBN | 9781450327459 |
DOI | 10.1145/2567948.2577283 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2014-04-07 |
Publisher Place | New York |
Access Restriction | Subscribed |
Subject Keyword | Neighborhood k-core Social and information networks Core numbers Parallel algorithms Graph compression Heuristic clique finder Dynamic networks Maximum clique Network analysis Greedy coloring Temporal strong components Large cliques Graph mining |
Content Type | Text |
Resource Type | Article |
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