Z. Abbas, V. Kalavri, P. Carbone, and V. Vlassov, Streaming graph partitioning: an experimental study, Proceedings of the VLDB Endowment, vol.11, pp.1590-1603, 2018.

T. Amjad, A. Daud, S. Khan, R. A. Abbasi, and F. Imran, Prediction of rising stars from pakistani research communities, 14th International Conference on Emerging Technologies (ICET), pp.1-6, 2018.

P. Andlinger, Graph dbms increased their popularity by 500% within the last 2 years, 2015.

S. Aridhi, A. Montresor, and Y. Velegrakis, Bladyg: A graph processing framework for large dynamic graphs, Big Data Research, vol.9, pp.9-17, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01577882

T. Aynaud and J. Guillaume, Static community detection algorithms for evolving networks, 8th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, pp.513-519, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00492058

M. Baborska-narozny, E. Stirling, and F. Stevenson, Exploring the relationship between a'facebook group'and face-to-face interactions in'weak-tie'residential communities, Proceedings of the 7th 2016 International Conference on Social Media & Society, p.17, 2016.

U. Brandes, M. Gaertler, and D. Wagner, Experiments on graph clustering algorithms, European Symposium on Algorithms, pp.568-579, 2003.

J. M. Bull, Measuring synchronisation and scheduling overheads in openmp, Proceedings of First European Workshop on OpenMP, vol.8, p.49, 1999.

L. Cao and J. Krumm, From gps traces to a routable road map, Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems, pp.3-12, 2009.

L. Chang, W. Li, L. Qin, W. Zhang, and S. Yang, pscan: Fast and exact structural graph clustering, IEEE Transactions on Knowledge and Data Engineering, vol.29, issue.2, pp.387-401, 2017.

Y. Che, S. Sun, and Q. Luo, Parallelizing pruning-based graph structural clustering, Proceedings of the 47th International Conference on Parallel Processing, vol.77, pp.1-77, 2018.

I. S. Dhillon, Co-clustering documents and words using bipartite spectral graph partitioning, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp.269-274, 2001.

C. H. Ding, X. He, H. Zha, M. Gu, and H. D. Simon, A min-max cut algorithm for graph partitioning and data clustering, Proceedings IEEE International Conference on, pp.107-114, 2001.

B. Doerr and D. Johannsen, Adjacency list matchings: an ideal genotype for cycle covers, Proceedings of the 9th annual conference on Genetic and evolutionary computation, pp.1203-1210, 2007.

E. F. Dazevedo, M. R. Fahey, and R. T. Mills, Vectorized sparse matrix multiply for compressed row storage format, International Conference on Computational Science, pp.99-106, 2005.

P. Goyal and E. Ferrara, Graph embedding techniques, applications, and performance: A survey. Knowledge-Based Systems, vol.151, pp.78-94, 2018.

J. A. Hartigan and M. A. Wong, Algorithm as 136: A k-means clustering algorithm, Journal of the Royal Statistical Society. Series C (Applied Statistics), vol.28, issue.1, pp.100-108, 1979.

A. P. Iyer, A. Panda, S. Venkataraman, M. Chowdhury, A. Akella et al., Bridging the gap: towards approximate graph analytics, Proceedings of the 1st ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA), p.10, 2018.

Y. Kozawa, T. Amagasa, and H. Kitagawa, Gpu-accelerated graph clustering via parallel label propagation, Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp.567-576, 2017.

D. Lasalle and G. Karypis, Multi-threaded modularity based graph clustering using the multilevel paradigm, Journal of Parallel and Distributed Computing, vol.76, pp.66-80, 2015.

J. Leskovec and A. Krevl, {SNAP Datasets}:{Stanford} large network dataset collection, 2015.

S. Lim, S. Ryu, S. Kwon, K. Jung, and J. Lee, Linkscan*: Overlapping community detection using the link-space transformation, 2014 IEEE 30th International Conference on Data Engineering (ICDE), pp.292-303, 2014.

S. T. Mai, M. S. Dieu, I. Assent, J. Jacobsen, J. Kristensen et al., Scalable and interactive graph clustering algorithm on multicore cpus, 2017 IEEE 33rd International Conference on, pp.349-360, 2017.

A. Said, R. A. Abbasi, O. Maqbool, A. Daud, and N. R. Aljohani, Cc-ga: A clustering coefficient based genetic algorithm for detecting communities in social networks, Applied Soft Computing, vol.63, pp.59-70, 2018.

J. H. Seo and M. H. Kim, pm-scan: an i/o efficient structural clustering algorithm for large-scale graphs, Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp.2295-2298, 2017.

H. Shiokawa, Y. Fujiwara, and M. Onizuka, Scan++: efficient algorithm for finding clusters, hubs and outliers on large-scale graphs, Proceedings of the VLDB Endowment, vol.8, pp.1178-1189, 2015.

T. R. Stovall, S. Kockara, and R. Avci, Gpuscan: Gpu-based parallel structural clustering algorithm for networks, IEEE Transactions on Parallel and Distributed Systems, vol.26, issue.12, pp.3381-3393, 2015.

T. Takahashi, H. Shiokawa, and H. Kitagawa, Scan-xp: Parallel structural graph clustering algorithm on intel xeon phi coprocessors, Proceedings of the 2nd International Workshop on Network Data Analytics, 2017.

D. Wen, L. Qin, Y. Zhang, L. Chang, and X. Lin, Efficient structural graph clustering: an index-based approach, Proceedings of the VLDB Endowment, vol.11, pp.243-255, 2017.

S. White and P. Smyth, A spectral clustering approach to finding communities in graphs, Proceedings of the 2005 SIAM international conference on data mining, pp.274-285, 2005.

X. Xu, N. Yuruk, Z. Feng, and T. A. Schweiger, Scan: a structural clustering algorithm for networks, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.824-833, 2007.

Y. Xu, V. Olman, and D. Xu, Clustering gene expression data using a graph-theoretic approach: an application of minimum spanning trees, Bioinformatics, vol.18, issue.4, pp.536-545, 2002.

H. Yin, A. R. Benson, J. Leskovec, and D. F. Gleich, Local higherorder graph clustering, Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.555-564, 2017.

K. R. ?alik and B. ?alik, Memetic algorithm using node entropy and partition entropy for community detection in networks, Information Sciences, vol.445, pp.38-49, 2018.

W. Zhao, G. Chen, and X. Xu, Anyscan: An efficient anytime framework with active learning for large-scale network clustering, 2017 IEEE International Conference on Data Mining (ICDM), pp.665-674, 2017.