Degree-Based Sampling Method with Partition-Based Subgraph Finder for Larger Motif Detection
Network motifs are subnetworks that appear in the network far more frequently than in randomized networks. They have gathered much attention for uncovering structural design principles of complex networks. One of the previous approaches for motif detection is sampling method, in- troduced to perform...
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Published in | Applied Mechanics and Materials Vol. 135-136; pp. 509 - 515 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Zurich
Trans Tech Publications Ltd
01.10.2011
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Subjects | |
Online Access | Get full text |
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Summary: | Network motifs are subnetworks that appear in the network far more frequently than in randomized networks. They have gathered much attention for uncovering structural design principles of complex networks. One of the previous approaches for motif detection is sampling method, in- troduced to perform the computational challenging task. However, it suffers from sampling bias and probability assignment. In addition, subgraph search, being very time-consuming, is a critical process in motif detection as we need to enumerate subgraphs of given sizes in the original input graph and an ensemble of random generated graphs. Therefore, we present a Degree-based Sampling Method with Partition-based Subgraph Finder for larger motif detection. Inspired by the intrinsic feature of real biological networks, Degree-based Sampling is a new solution for probability assignment based on degree. And, Partition-based Subgraph Finder takes its inspiration from the idea of partition, which improves computational efficiency and lowers space consumption. Experimental study on UETZ and E.COLI data set shows that the proposed method achieves more accuracy and efficiency than previous methods and scales better with increasing subgraph size. |
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Bibliography: | Selected, peer reviewed papers from the 2011 WASE Global Conference on Science Engineering (GCSE 2011), December 10-11, 2011, Taiyuan & Xian, China |
ISBN: | 3037852909 9783037852903 |
ISSN: | 1660-9336 1662-7482 1662-7482 |
DOI: | 10.4028/www.scientific.net/AMM.135-136.509 |