I/O efficient ECC graph decomposition via graph reduction

The problem of computing k -edge connected components ( k - ECC s) of a graph G for a specific k is a fundamental graph problem and has been investigated recently. In this paper, we study the problem of ECC decomposition, which computes the k - ECC s of a graph G for all possible k values. ECC decom...

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Veröffentlicht in:	The VLDB journal Jg. 26; H. 2; S. 275 - 300
Hauptverfasser:	Yuan, Long, Qin, Lu, Lin, Xuemin, Chang, Lijun, Zhang, Wenjie
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2017 Springer Nature B.V
Schlagworte:	Algorithms Computation Computer memory Computer Science Cost analysis Data structures Database Management Decomposition Graph theory Input output analysis Regular Paper Searching Tillage I/O efficient algorithm Graph Edge connected component decomposition
ISSN:	1066-8888, 0949-877X
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Abstract	The problem of computing k -edge connected components ( k - ECC s) of a graph G for a specific k is a fundamental graph problem and has been investigated recently. In this paper, we study the problem of ECC decomposition, which computes the k - ECC s of a graph G for all possible k values. ECC decomposition can be widely applied in a variety of applications such as graph-topology analysis, community detection, Steiner Component Search, and graph visualization. A straightforward solution for ECC decomposition is to apply the existing k - ECC computation algorithm to compute the k - ECC s for all k values. However, this solution is not applicable to large graphs for two challenging reasons. First, all existing k - ECC computation algorithms are highly memory intensive due to the complex data structures used in the algorithms. Second, the number of possible k values can be very large, resulting in a high computational cost when each k value is independently considered. In this paper, we address the above challenges, and study I/O efficient ECC decomposition via graph reduction. We introduce two elegant graph reduction operators which aim to reduce the size of the graph loaded in memory while preserving the connectivity information of a certain set of edges to be computed for a specific k . We also propose three novel I/O efficient algorithms, Bottom - Up , Top - Down , and Hybrid , that explore the k values in different orders to reduce the redundant computations between different k values. We analyze the I/O and memory costs for all proposed algorithms. In addition, we extend our algorithm to build an efficient index for Steiner Component Search. We show that our index can be used to perform Steiner Component Search in optimal I/Os when only the node information of the graph is allowed to be loaded in memory. In our experiments, we evaluate our algorithms using seven real large datasets with various graph properties, one of which contains 1.95 billion edges. The experimental results show that our proposed algorithms are scalable and efficient.
AbstractList	The problem of computing k-edge connected components (k- ECC s) of a graph G for a specific k is a fundamental graph problem and has been investigated recently. In this paper, we study the problem of ECC decomposition, which computes the k- ECC s of a graph G for all possible k values. ECC decomposition can be widely applied in a variety of applications such as graph-topology analysis, community detection, Steiner Component Search, and graph visualization. A straightforward solution for ECC decomposition is to apply the existing k- ECC computation algorithm to compute the k- ECC s for all k values. However, this solution is not applicable to large graphs for two challenging reasons. First, all existing k- ECC computation algorithms are highly memory intensive due to the complex data structures used in the algorithms. Second, the number of possible k values can be very large, resulting in a high computational cost when each k value is independently considered. In this paper, we address the above challenges, and study I/O efficient ECC decomposition via graph reduction. We introduce two elegant graph reduction operators which aim to reduce the size of the graph loaded in memory while preserving the connectivity information of a certain set of edges to be computed for a specific k. We also propose three novel I/O efficient algorithms, Bottom - Up , Top - Down , and Hybrid , that explore the k values in different orders to reduce the redundant computations between different k values. We analyze the I/O and memory costs for all proposed algorithms. In addition, we extend our algorithm to build an efficient index for Steiner Component Search. We show that our index can be used to perform Steiner Component Search in optimal I/Os when only the node information of the graph is allowed to be loaded in memory. In our experiments, we evaluate our algorithms using seven real large datasets with various graph properties, one of which contains 1.95 billion edges. The experimental results show that our proposed algorithms are scalable and efficient. The problem of computing k -edge connected components ( k - ECC s) of a graph G for a specific k is a fundamental graph problem and has been investigated recently. In this paper, we study the problem of ECC decomposition, which computes the k - ECC s of a graph G for all possible k values. ECC decomposition can be widely applied in a variety of applications such as graph-topology analysis, community detection, Steiner Component Search, and graph visualization. A straightforward solution for ECC decomposition is to apply the existing k - ECC computation algorithm to compute the k - ECC s for all k values. However, this solution is not applicable to large graphs for two challenging reasons. First, all existing k - ECC computation algorithms are highly memory intensive due to the complex data structures used in the algorithms. Second, the number of possible k values can be very large, resulting in a high computational cost when each k value is independently considered. In this paper, we address the above challenges, and study I/O efficient ECC decomposition via graph reduction. We introduce two elegant graph reduction operators which aim to reduce the size of the graph loaded in memory while preserving the connectivity information of a certain set of edges to be computed for a specific k . We also propose three novel I/O efficient algorithms, Bottom - Up , Top - Down , and Hybrid , that explore the k values in different orders to reduce the redundant computations between different k values. We analyze the I/O and memory costs for all proposed algorithms. In addition, we extend our algorithm to build an efficient index for Steiner Component Search. We show that our index can be used to perform Steiner Component Search in optimal I/Os when only the node information of the graph is allowed to be loaded in memory. In our experiments, we evaluate our algorithms using seven real large datasets with various graph properties, one of which contains 1.95 billion edges. The experimental results show that our proposed algorithms are scalable and efficient.
Author	Zhang, Wenjie Yuan, Long Qin, Lu Chang, Lijun Lin, Xuemin
Author_xml	– sequence: 1 givenname: Long surname: Yuan fullname: Yuan, Long organization: The University of New South Wales – sequence: 2 givenname: Lu surname: Qin fullname: Qin, Lu email: lu.qin@uts.edu.au organization: Centre for QCIS, University of Technology – sequence: 3 givenname: Xuemin surname: Lin fullname: Lin, Xuemin organization: The University of New South Wales – sequence: 4 givenname: Lijun surname: Chang fullname: Chang, Lijun organization: The University of New South Wales – sequence: 5 givenname: Wenjie surname: Zhang fullname: Zhang, Wenjie organization: The University of New South Wales
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Cites_doi	10.1145/1081870.1081908 10.3934/nhm.2008.3.371 10.14778/2311906.2311909 10.1007/BF02289199 10.1002/net.20046 10.1007/BF02289146 10.1145/2723372.2723740 10.1145/2463676.2465323 10.1145/2505515.2505751 10.1007/BF01758778 10.1016/S0304-3975(98)00091-7 10.1093/bioinformatics/btl370 10.1109/TVCG.2008.151 10.1145/775152.775227 10.1145/2463676.2463704 10.1073/pnas.2032324100 10.1109/ICDE.2011.5767911 10.1007/3-540-45995-2_51 10.1145/2339530.2339724 10.1007/s00778-015-0408-z 10.1145/2247596.2247652 10.1016/S0020-0190(00)00142-3 10.1016/0378-8733(83)90028-X 10.1145/1081870.1081898 10.1111/0081-1750.00098 10.1145/48529.48535 10.1109/ICDE.2012.35 10.1145/2463676.2463703 10.1145/2723372.2746486 10.1073/pnas.0701175104
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References	Hu, X., Tao, Y., Chung, C.: Massive graph triangulation. In: Proceedings of the SIGMOD, pp. 325–336 (2013) LuceRDConnectivity and generalized cliques in sociometric group structurePsychometrika19501521691903597510.1007/BF02289199 Zhang, Y., Parthasarathy, S.: Extracting analyzing and visualizing triangle k-core motifs within networks. In: Proceedings of the ICDE, pp. 1049–1060 (2012) Agrawal, R., Rajagopalan, S., Srikant, R., Xu, Y.: Mining newsgroups using networks arising from social behavior. In: Proceedings of WWW, pp. 529–535 (2003) Zhang, Z., Yu, J.X., Qin, L., Chang, L., Lin, X.: I/O efficient: computing SCCs in massive graphs. In Proceedings of the SIGMOD, pp. 245–270 (2013) ChenJYuanBDetecting functional modules in the yeast protein-protein interaction networkBioinformatics200622182283229010.1093/bioinformatics/btl370 Alvarez-HamelinJIDall’AstaLBarratAVespignaniAK-core decomposition of internet graphs: hierarchies, self-similarity and measurement biasesNetw. Heterog. Media200832371393239523810.3934/nhm.2008.3.3711145.68470 Wang, J., Cheng, J.: Truss decomposition in massive networks. PVLDB 5(9), 812–823 (2012) JiaYHoberockJGarlandMHartJOn the visualization of social and other scale-free networksIEEE Trans. Vis. Comput. Graph.20081461285129210.1109/TVCG.2008.151 CarmiSHavlinSKirkpatrickSShavittYShirEA model of internet topology using k-shell decompositionProc. Natl Acad. Sci.200710427111501115410.1073/pnas.0701175104 Abello, J., Resende, M.G., Sudarsky, S.: Massive quasi-clique detection. In: Latin American Symposium on Theoretical Informatics, pp. 598–612 (2002) Pei, J., Jiang, D., Zhang, A.: On mining cross-graph quasi-cliques. In: Proceedings of the SIGKDD, pp. 228–238 (2005) Alvarez-Hamelin, J.I., Dall’Asta, L., Barrat, A., Vespignani, A.: Large scale networks fingerprinting and visualization using the k-core decomposition. In: Advances in Neural Information Processing Systems, pp. 41–50 (2005) YuanLQinLLinXChangLZhangWI/O efficient ecc graph decomposition via graph reductionPVLDB201697516527 AggarwalAVitterJThe input/output complexity of sorting and related problemsCommun. ACM198831911161127102179410.1145/48529.48535 HartuvEShamirRA clustering algorithm based on graph connectivityInf. Process. Lett.2000764175181180767610.1016/S0020-0190(00)00142-30996.68525 Akiba, T., Iwata, Y., Yoshida, Y.: Linear-time enumeration of maximal k-edge-connected subgraphs in large networks by random contraction. In: Proceedings CIKM, pp. 909–918 (2013) MagnantiTLRaghavanSStrong formulations for network design problems with connectivity requirementsNetworks20054526179211641610.1002/net.200461067.68104 MatsudaHIshiharaTHashimotoAClassifying molecular sequences using a linkage graph with their pairwise similaritiesTheor. Comput. Sci.19992102305325165545510.1016/S0304-3975(98)00091-70912.68218 Cheng, J., Ke, Y., Chu, S., Ozsu. M.T.: Efficient core decomposition in massive networks. In: Proceedings of the ICDE, pp. 51–62 (2011) Cheng, J., Zhu, L., Ke, Y., Chu, S.: Fast algorithms for maximal clique enumeration with limited memory. In: Proceedings of the SIGKDD, pp. 1240–1248 (2012) Zhou, R., Liu, C., Yu, J.X., Liang, W., Chen, B., Li, J.: Finding maximal k-edge-connected subgraphs from a large graph. In: Proceedings of the EDBT, pp. 480–491 (2012) Zhang, Z., Yu, J.X., Qin, L., Shang, Z.: Divide & conquer: I/O efficient depth-first search. In: Proceedings of the SIGMOD, pp. 445–458 (2015) NagamochiHIbarakiTA linear-time algorithm for finding a sparse k-connected spanning subgraph of a k-connected graphAlgorithmica199271–6583596115458910.1007/BF017587780763.05065 SpirinVMirnyLAProtein complexes and functional modules in molecular networksProc. Natl. Acad. Sci.200310021121231212810.1073/pnas.2032324100 Yan, X., Zhou, X., Han, J.: Mining closed relational graphs with connectivity constraints. In: Proceedings of the SIGKDD, pp. 324–333 (2005) Chang, L., Yu, J.X., Qin, L., Lin, X., Liu, C., Liang, W.: Efficiently computing k-edge connected components via graph decomposition. In: Proceedings of the SIGMOD, pp. 205–216 (2013) SeidmanSBNetwork structure and minimum degreeSoc. Netw.19835326928772129510.1016/0378-8733(83)90028-X YuanLQinLLinXChangLZhangWDiversified top-k clique searchVLDB J.201625217119610.1007/s00778-015-0408-z Alvarez-Hamelin, J.I., Dall’Asta, L., Barrat, A., Vespignani, A.: How the k-core decomposition helps in understanding the internet topology. In: ISMA Workshop on the Internet Topology, vol. 1 (2006) Chang, L., Lin, X., Qin, L., Yu, J.X., Zhang, W.: Index-based optimal algorithms for computing Steiner components with maximum connectivity. In: Proceedings of the SIGMOD, pp. 459–474 (2015) LuceRDPerryADA method of matrix analysis of group structurePsychometrika1949142951163597410.1007/BF02289146 WangNZhangJTanKTungAKHOn triangulation-based dense neighborhood graphs discoveryPVLDB2010425868 WhiteDRHararyFThe cohesiveness of blocks in social networks: node connectivity and conditional densitySociol. Methodol.200131130535910.1111/0081-1750.00098 451_CR12 451_CR34 451_CR33 451_CR10 N Wang (451_CR26) 2010; 4 451_CR32 451_CR31 TL Magnanti (451_CR19) 2005; 45 SB Seidman (451_CR23) 1983; 5 A Aggarwal (451_CR2) 1988; 31 S Carmi (451_CR8) 2007; 104 RD Luce (451_CR18) 1949; 14 H Nagamochi (451_CR21) 1992; 7 RD Luce (451_CR17) 1950; 15 451_CR28 JI Alvarez-Hamelin (451_CR7) 2008; 3 E Hartuv (451_CR14) 2000; 76 451_CR25 451_CR1 451_CR22 451_CR5 451_CR4 451_CR3 451_CR9 J Chen (451_CR11) 2006; 22 451_CR6 H Matsuda (451_CR20) 1999; 210 L Yuan (451_CR30) 2016; 9 Y Jia (451_CR16) 2008; 14 DR White (451_CR27) 2001; 31 451_CR15 V Spirin (451_CR24) 2003; 100 L Yuan (451_CR29) 2016; 25 451_CR13
References_xml	– reference: Zhang, Z., Yu, J.X., Qin, L., Shang, Z.: Divide & conquer: I/O efficient depth-first search. In: Proceedings of the SIGMOD, pp. 445–458 (2015) – reference: Pei, J., Jiang, D., Zhang, A.: On mining cross-graph quasi-cliques. In: Proceedings of the SIGKDD, pp. 228–238 (2005) – reference: Abello, J., Resende, M.G., Sudarsky, S.: Massive quasi-clique detection. In: Latin American Symposium on Theoretical Informatics, pp. 598–612 (2002) – reference: Akiba, T., Iwata, Y., Yoshida, Y.: Linear-time enumeration of maximal k-edge-connected subgraphs in large networks by random contraction. In: Proceedings CIKM, pp. 909–918 (2013) – reference: Agrawal, R., Rajagopalan, S., Srikant, R., Xu, Y.: Mining newsgroups using networks arising from social behavior. In: Proceedings of WWW, pp. 529–535 (2003) – reference: WangNZhangJTanKTungAKHOn triangulation-based dense neighborhood graphs discoveryPVLDB2010425868 – reference: AggarwalAVitterJThe input/output complexity of sorting and related problemsCommun. ACM198831911161127102179410.1145/48529.48535 – reference: ChenJYuanBDetecting functional modules in the yeast protein-protein interaction networkBioinformatics200622182283229010.1093/bioinformatics/btl370 – reference: Zhang, Y., Parthasarathy, S.: Extracting analyzing and visualizing triangle k-core motifs within networks. In: Proceedings of the ICDE, pp. 1049–1060 (2012) – reference: Chang, L., Lin, X., Qin, L., Yu, J.X., Zhang, W.: Index-based optimal algorithms for computing Steiner components with maximum connectivity. In: Proceedings of the SIGMOD, pp. 459–474 (2015) – reference: YuanLQinLLinXChangLZhangWDiversified top-k clique searchVLDB J.201625217119610.1007/s00778-015-0408-z – reference: LuceRDConnectivity and generalized cliques in sociometric group structurePsychometrika19501521691903597510.1007/BF02289199 – reference: MagnantiTLRaghavanSStrong formulations for network design problems with connectivity requirementsNetworks20054526179211641610.1002/net.200461067.68104 – reference: Wang, J., Cheng, J.: Truss decomposition in massive networks. PVLDB 5(9), 812–823 (2012) – reference: Zhang, Z., Yu, J.X., Qin, L., Chang, L., Lin, X.: I/O efficient: computing SCCs in massive graphs. In Proceedings of the SIGMOD, pp. 245–270 (2013) – reference: Cheng, J., Zhu, L., Ke, Y., Chu, S.: Fast algorithms for maximal clique enumeration with limited memory. In: Proceedings of the SIGKDD, pp. 1240–1248 (2012) – reference: SpirinVMirnyLAProtein complexes and functional modules in molecular networksProc. Natl. Acad. Sci.200310021121231212810.1073/pnas.2032324100 – reference: LuceRDPerryADA method of matrix analysis of group structurePsychometrika1949142951163597410.1007/BF02289146 – reference: Alvarez-HamelinJIDall’AstaLBarratAVespignaniAK-core decomposition of internet graphs: hierarchies, self-similarity and measurement biasesNetw. Heterog. Media200832371393239523810.3934/nhm.2008.3.3711145.68470 – reference: Zhou, R., Liu, C., Yu, J.X., Liang, W., Chen, B., Li, J.: Finding maximal k-edge-connected subgraphs from a large graph. In: Proceedings of the EDBT, pp. 480–491 (2012) – reference: JiaYHoberockJGarlandMHartJOn the visualization of social and other scale-free networksIEEE Trans. Vis. Comput. Graph.20081461285129210.1109/TVCG.2008.151 – reference: Alvarez-Hamelin, J.I., Dall’Asta, L., Barrat, A., Vespignani, A.: Large scale networks fingerprinting and visualization using the k-core decomposition. In: Advances in Neural Information Processing Systems, pp. 41–50 (2005) – reference: Cheng, J., Ke, Y., Chu, S., Ozsu. M.T.: Efficient core decomposition in massive networks. In: Proceedings of the ICDE, pp. 51–62 (2011) – reference: SeidmanSBNetwork structure and minimum degreeSoc. Netw.19835326928772129510.1016/0378-8733(83)90028-X – reference: NagamochiHIbarakiTA linear-time algorithm for finding a sparse k-connected spanning subgraph of a k-connected graphAlgorithmica199271–6583596115458910.1007/BF017587780763.05065 – reference: Chang, L., Yu, J.X., Qin, L., Lin, X., Liu, C., Liang, W.: Efficiently computing k-edge connected components via graph decomposition. In: Proceedings of the SIGMOD, pp. 205–216 (2013) – reference: WhiteDRHararyFThe cohesiveness of blocks in social networks: node connectivity and conditional densitySociol. Methodol.200131130535910.1111/0081-1750.00098 – reference: HartuvEShamirRA clustering algorithm based on graph connectivityInf. Process. Lett.2000764175181180767610.1016/S0020-0190(00)00142-30996.68525 – reference: Hu, X., Tao, Y., Chung, C.: Massive graph triangulation. In: Proceedings of the SIGMOD, pp. 325–336 (2013) – reference: Yan, X., Zhou, X., Han, J.: Mining closed relational graphs with connectivity constraints. In: Proceedings of the SIGKDD, pp. 324–333 (2005) – reference: MatsudaHIshiharaTHashimotoAClassifying molecular sequences using a linkage graph with their pairwise similaritiesTheor. Comput. Sci.19992102305325165545510.1016/S0304-3975(98)00091-70912.68218 – reference: Alvarez-Hamelin, J.I., Dall’Asta, L., Barrat, A., Vespignani, A.: How the k-core decomposition helps in understanding the internet topology. In: ISMA Workshop on the Internet Topology, vol. 1 (2006) – reference: CarmiSHavlinSKirkpatrickSShavittYShirEA model of internet topology using k-shell decompositionProc. Natl Acad. Sci.200710427111501115410.1073/pnas.0701175104 – reference: YuanLQinLLinXChangLZhangWI/O efficient ecc graph decomposition via graph reductionPVLDB201697516527 – ident: 451_CR28 doi: 10.1145/1081870.1081908 – volume: 3 start-page: 371 issue: 2 year: 2008 ident: 451_CR7 publication-title: Netw. Heterog. Media doi: 10.3934/nhm.2008.3.371 – ident: 451_CR25 doi: 10.14778/2311906.2311909 – ident: 451_CR6 – volume: 15 start-page: 169 issue: 2 year: 1950 ident: 451_CR17 publication-title: Psychometrika doi: 10.1007/BF02289199 – volume: 45 start-page: 61 issue: 2 year: 2005 ident: 451_CR19 publication-title: Networks doi: 10.1002/net.20046 – volume: 14 start-page: 95 issue: 2 year: 1949 ident: 451_CR18 publication-title: Psychometrika doi: 10.1007/BF02289146 – ident: 451_CR33 doi: 10.1145/2723372.2723740 – ident: 451_CR10 doi: 10.1145/2463676.2465323 – ident: 451_CR4 doi: 10.1145/2505515.2505751 – volume: 4 start-page: 58 issue: 2 year: 2010 ident: 451_CR26 publication-title: PVLDB – volume: 7 start-page: 583 issue: 1–6 year: 1992 ident: 451_CR21 publication-title: Algorithmica doi: 10.1007/BF01758778 – volume: 210 start-page: 305 issue: 2 year: 1999 ident: 451_CR20 publication-title: Theor. Comput. Sci. doi: 10.1016/S0304-3975(98)00091-7 – volume: 22 start-page: 2283 issue: 18 year: 2006 ident: 451_CR11 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btl370 – volume: 14 start-page: 1285 issue: 6 year: 2008 ident: 451_CR16 publication-title: IEEE Trans. Vis. Comput. Graph. doi: 10.1109/TVCG.2008.151 – ident: 451_CR3 doi: 10.1145/775152.775227 – ident: 451_CR15 doi: 10.1145/2463676.2463704 – volume: 100 start-page: 12123 issue: 21 year: 2003 ident: 451_CR24 publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.2032324100 – ident: 451_CR12 doi: 10.1109/ICDE.2011.5767911 – ident: 451_CR1 doi: 10.1007/3-540-45995-2_51 – ident: 451_CR13 doi: 10.1145/2339530.2339724 – volume: 25 start-page: 171 issue: 2 year: 2016 ident: 451_CR29 publication-title: VLDB J. doi: 10.1007/s00778-015-0408-z – ident: 451_CR34 doi: 10.1145/2247596.2247652 – volume: 76 start-page: 175 issue: 4 year: 2000 ident: 451_CR14 publication-title: Inf. Process. Lett. doi: 10.1016/S0020-0190(00)00142-3 – volume: 5 start-page: 269 issue: 3 year: 1983 ident: 451_CR23 publication-title: Soc. Netw. doi: 10.1016/0378-8733(83)90028-X – ident: 451_CR22 doi: 10.1145/1081870.1081898 – volume: 31 start-page: 305 issue: 1 year: 2001 ident: 451_CR27 publication-title: Sociol. Methodol. doi: 10.1111/0081-1750.00098 – volume: 31 start-page: 1116 issue: 9 year: 1988 ident: 451_CR2 publication-title: Commun. ACM doi: 10.1145/48529.48535 – ident: 451_CR5 – volume: 9 start-page: 516 issue: 7 year: 2016 ident: 451_CR30 publication-title: PVLDB – ident: 451_CR31 doi: 10.1109/ICDE.2012.35 – ident: 451_CR32 doi: 10.1145/2463676.2463703 – ident: 451_CR9 doi: 10.1145/2723372.2746486 – volume: 104 start-page: 11150 issue: 27 year: 2007 ident: 451_CR8 publication-title: Proc. Natl Acad. Sci. doi: 10.1073/pnas.0701175104
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Snippet	The problem of computing k -edge connected components ( k - ECC s) of a graph G for a specific k is a fundamental graph problem and has been investigated... The problem of computing k-edge connected components (k- ECC s) of a graph G for a specific k is a fundamental graph problem and has been investigated...
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SubjectTerms	Algorithms Computation Computer memory Computer Science Cost analysis Data structures Database Management Decomposition Graph theory Input output analysis Regular Paper Searching Tillage
Title	I/O efficient ECC graph decomposition via graph reduction
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