Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Ahmet Erdem Sariyuce"'
Publikováno v:
IEEE/ACM Transactions on Computational Biology and Bioinformatics. 20:1092-1103
With the emergence of portable DNA sequencers, such as Oxford Nanopore Technology MinION, metagenomic DNA sequencing can be performed in real-time and directly in the field. However, because metagenomic DNA analysis tasks, e.g., classification, taxon
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e4083effbfc73cefc898c234ae6fc26c
https://doi.org/10.1109/tcbb.2022.3172661
https://doi.org/10.1109/tcbb.2022.3172661
Publikováno v:
2022 IEEE 38th International Conference on Data Engineering (ICDE).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6687713cec4e89c681ffb8dc75667ed0
https://doi.org/10.1109/icde53745.2022.00142
https://doi.org/10.1109/icde53745.2022.00142
Autor:
Ahmet Erdem Sariyuce
Publikováno v:
WWW
Dense regions in networks are an indicator of interesting and unusual information. However, most existing methods only consider simple, undirected, unweighted networks. Complex networks in the real-world often have rich information though: edges are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::72f782aa3652dc9ffbfed084aef172d8
Autor:
Penghang Liu, Ahmet Erdem Sariyuce
Publikováno v:
IEEE BigData
Finding the dense regions in a graph is an important problem in network analysis. Core decomposition and truss decomposition address this problem from two different perspectives. The former is a vertex-driven approach that assigns density indicators
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2c29e34732471a39ffa0cc7bc429632f
https://doi.org/10.1109/bigdata50022.2020.9378497
https://doi.org/10.1109/bigdata50022.2020.9378497
Centrality rankings such as degree, closeness, betweenness, Katz, PageRank, etc. are commonly used to identify critical nodes in a graph. These methods are based on two assumptions that restrict their wider applicability. First, they assume the exact
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::48d6cf04ab8505c35cbdd1a103ac203b
https://doi.org/10.2172/1733256
https://doi.org/10.2172/1733256
With the emergence of portable DNA sequencers, such as Oxford Nanopore Technology MinION, metagenomic DNA sequencing can be performed in real-time and directly in the field. However, because metagenomic DNA analysis is computationally and memory inte
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::29ea6fcb1efc363cbd619e2bfa784bc3
https://doi.org/10.1101/2020.08.21.261313
https://doi.org/10.1101/2020.08.21.261313
Publikováno v:
Computational Data and Social Networks ISBN: 9783030660451
CSoNet
CSoNet
Understanding the structure of dense regions in real-world networks is an important research area with myriad practical applications. Using higher-order structures (motifs), such as triangles, had been shown to be effective to locate the dense subgra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::785130bbd21bcc5d4290b9b6396f408a
https://doi.org/10.1007/978-3-030-66046-8_15
https://doi.org/10.1007/978-3-030-66046-8_15
Publikováno v:
Proceedings of the VLDB Endowment. 12:43-56
Finding the dense regions of a graph and relations among them is a fundamental problem in network analysis. Core and truss decompositions reveal dense subgraphs with hierarchical relations. The incremental nature of algorithms for computing these dec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::416f6441f2c8f112d37353ef0940a0bb
https://doi.org/10.14778/3275536.3275540
https://doi.org/10.14778/3275536.3275540
Publikováno v:
ACM Transactions on the Web. 11:1-27
Finding dense substructures in a graph is a fundamental graph mining operation, with applications in bioinformatics, social networks, and visualization to name a few. Yet most standard formulations of this problem (like clique, quasi-clique, densest
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3ad6a747ee9b880c28b32441af37934f
https://doi.org/10.1145/3057742
https://doi.org/10.1145/3057742
Publikováno v:
ACM Transactions on Knowledge Discovery from Data. 11:1-25
The betweenness and closeness metrics have always been intriguing and used in many analyses. Yet, they are expensive to compute. For that reason, making the betweenness and closeness centrality computations faster is an important and well-studied pro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::840871159e1b62127474b301f2de09fe
https://doi.org/10.1145/3022668
https://doi.org/10.1145/3022668