, Systems biology: Looking beyond the genome

H. A. Abdelbary, A. M. Elkorany, and R. Bahgat, Utilizing deep learning for content-based community detection, 2014 Science and Information Conference, pp.777-784, 2014.

N. K. Ahmed, N. G. Duffield, J. Neville, and R. R. Kompella, Graph sample and hold: A framework for big-graph analytics, 2014.

J. Alber and J. Fiala, Geometric separation and exact solutions for the parameterized independent set problem on disk graphs, J. Algorithms, vol.52, issue.2, pp.134-151, 2004.

D. Alistarh, J. Iglesias, and M. Vojnovic, Streaming min-max hypergraph partitioning, Proceedings of the 28th International Conference on Neural Information Processing Systems, vol.2, pp.1900-1908, 2015.

T. Amjad, Y. Ding, A. Daud, J. Xu, and V. Malic, Topic-based heterogeneous rank, Scientometrics, vol.104, issue.1, pp.313-334, 2015.
DOI : 10.1007/s11192-015-1601-y

R. Andersen, F. R. Chung, L. , and K. J. , Local Graph Partitioning using PageRank Vectors, Proc. of the IEEE Symposium on Foundations of Computer Science (FOCS), pp.475-486, 2006.
DOI : 10.1109/focs.2006.44

K. Andreev and H. Räcke, Balanced graph partitioning, Proceedings of the Sixteenth Annual ACM Symposium on Parallelism in Algorithms and Architectures, SPAA '04, pp.120-124, 2004.
DOI : 10.1007/s00224-006-1350-7
URL : http://www.dcs.warwick.ac.uk/~harry/pdf/balanced_partition_journal.pdf

. Apache and . Giraph,

S. Aridhi, A. Montresor, and Y. Velegrakis, BLADYG: A Graph Processing Framework for Large Dynamic Graphs, Big Data Research, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01577882

B. Bahmani, K. Chakrabarti, and D. Xin, Fast Personalized PageRank on MapReduce, Proc. of the ACM SIGMOD International Conference on Management of Data (SIGMOD), pp.973-984, 2011.

B. Bahmani, A. Chowdhury, and A. Goel, Fast Incremental and Personalized PageRank, Proc. of the VLDB Endowment (PVLDB), vol.4, pp.173-184, 2010.

B. Bahmani, R. Kumar, M. Mahdian, and E. Upfal, Pagerank on an evolving graph, Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '12, pp.24-32, 2012.

A. Barabási, A. , and R. , Emergence of scaling in random networks, Science, vol.286, issue.5439, pp.509-512, 1999.

P. Barceló, L. Libkin, and J. L. Reutter, Querying graph patterns, Proceedings of the Thirtieth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS '11, pp.199-210, 2011.

S. T. Barnard and H. D. Simon, Fast multilevel implementation of recursive spectral bisection for partitioning unstructured problems, Concurrency: Practice and Experience, vol.6, issue.2, pp.101-117, 1994.
DOI : 10.1002/cpe.4330060203

A. Bertrand and M. Moonen, Distributed computation of the fiedler vector with application to topology inference in ad hoc networks. Signal Process, vol.93, pp.1106-1117, 2013.

F. Bourse, M. Lelarge, and M. Vojnovic, Balanced Graph Edge Partition, Proc. of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp.1456-1465, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01137951

T. Bui, C. Heigham, C. Jones, L. , and T. , Improving the performance of the kernighan-lin and simulated annealing graph bisection algorithms, Proceedings of the 26th ACM/IEEE Design Automation Conference, DAC '89, pp.775-778, 1989.

D. Cai, Z. Shao, X. He, X. Yan, H. et al., Mining hidden community in heterogeneous social networks, Proceedings of the 3rd International Workshop on Link Discovery, LinkKDD '05, pp.58-65, 2005.

A. Cardillo, J. Gómez-gardeñes, M. Zanin, M. Romance, D. Papo et al., Emergence of network features from multiplexity, Scientific Reports, vol.3, p.1344, 2013.
DOI : 10.1038/srep01344
URL : https://www.nature.com/articles/srep01344.pdf

U. V. Çatalyürek, C. Aykanat, and B. Uçar, On two-dimensional sparse matrix partitioning: Models, methods, and a recipe, SIAM J. Sci. Comput, vol.32, issue.2, pp.656-683, 2010.

T. F. Chan, P. Ciarlet, J. Szeto, and W. K. , On the optimality of the median cut spectral bisection graph partitioning method, SIAM Journal on Scientific Computing, vol.18, issue.3, pp.943-948, 1997.
URL : https://hal.archives-ouvertes.fr/hal-01010396

F. Chierichetti, R. Kumar, S. Lattanzi, M. Mitzenmacher, A. Panconesi et al., On Compressing Social Networks, Proc. of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp.219-228, 2009.
DOI : 10.1145/1557019.1557049
URL : http://www.eecs.harvard.edu/~michaelm/postscripts/kdd2009.pdf

E. Cho, S. A. Myers, and J. Leskovec, Friendship and mobility: User movement in location-based social networks, Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '11, pp.1082-1090, 2011.

C. Constantin, R. Dahimene, Q. Grossetti, D. Mouza, and C. , Finding Users of Interest in Micro-blogging Systems, International Conference on Extending Database Technology, EDBT, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01362686

R. Dahimene, C. Constantin, and C. Mouza, RecLand: A Recommender System for Social Networks, Proc. of the ACM International Conference on Conference on Information and Knowledge Management (CIKM), pp.2063-2065, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01126503

M. De-domenico, A. Lima, P. Mougel, and M. Musolesi, The anatomy of a scientific rumor, vol.3, p.2980, 2013.

M. De-domenico, M. A. Porter, and A. Arenas, Muxviz: a tool for multilayer analysis and visualization of networks, Journal of Complex Networks, vol.3, issue.2, pp.159-176, 2015.

X. Dong, P. Frossard, P. Vandergheynst, and N. Nefedov, Clustering with multi-layer graphs: A spectral perspective, IEEE Transactions on Signal Processing, vol.60, issue.11, pp.5820-5831, 2012.

X. Dong, P. Frossard, P. Vandergheynst, and N. Nefedov, Clustering with multi-layer graphs: A spectral perspective, Trans. Sig. Proc, vol.60, issue.11, pp.5820-5831, 2012.

X. Dong, P. Frossard, P. Vandergheynst, and N. Nefedov, Clustering on multi-layer graphs via subspace analysis on grassmann manifolds, IEEE Transactions on Signal Processing, vol.62, issue.4, pp.905-918, 2014.
DOI : 10.1109/globalsip.2013.6737060
URL : http://arxiv.org/pdf/1303.2221

D. Eppstein, G. L. Miller, and S. Teng, A deterministic linear time algorithm for geometric separators and its applications, Proceedings of the Ninth Annual Symposium on Computational Geometry, SCG '93, pp.99-108, 1993.
DOI : 10.1145/160985.161005

M. Faloutsos, P. Faloutsos, F. , and C. , On power-law relationships of the internet topology, SIGCOMM Comput. Commun. Rev, vol.29, issue.4, pp.251-262, 1999.
DOI : 10.1515/9781400841356.195
URL : http://repository.cmu.edu/cgi/viewcontent.cgi?article=1584&context=compsci

H. Fani, F. Zarrinkalam, E. Bagheri, and W. Du, Time-Sensitive Topic-Based Communities on Twitter, pp.192-204, 2016.

U. Feige and R. Krauthgamer, A polylogarithmic approximation of the minimum bisection, Proceedings 41st Annual Symposium on Foundations of Computer Science, pp.105-115, 2000.

U. Feige, R. Krauthgamer, and K. Nissim, Approximating the minimum bisection size (extended abstract), Proceedings of the Thirty-second Annual ACM Symposium on Theory of Computing, STOC '00, pp.530-536, 2000.

A. E. Feldmann, Fast Balanced Partitioning Is Hard Even on Grids and Trees, pp.372-382, 2012.

C. M. Fiduccia and R. M. Mattheyses, A linear-time heuristic for improving network partitions, 19th Design Automation Conference, pp.175-181, 1982.

M. Fiedler, A property of eigenvectors of nonnegative symmetric matrices and its application to graph theory, Czechoslovak Mathematical Journal, vol.25, issue.4, pp.619-633, 1975.

M. Fiedler, Laplacian of graphs and algebraic connectivity, vol.25, pp.57-70, 1989.
DOI : 10.4064/-25-1-57-70
URL : https://www.impan.pl/shop/publication/transaction/download/product/106506?download.pdf

D. Fogaras and B. Racz, Towards Scaling Fully Personalized PageRank, WAW, pp.105-117, 2004.
DOI : 10.1007/978-3-540-30216-2_9

D. F. Gleich and C. Seshadhri, Vertex Neighborhoods, Low Conductance Cuts, and Good Seeds for Local Community Methods, Proc. of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp.597-605, 2012.
DOI : 10.1145/2339530.2339628
URL : http://www.cs.princeton.edu/~csesha/pubs/nbd-comm-conf.pdf

J. E. Gonzalez, Y. Low, H. Gu, D. Bickson, G. et al., PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs, Proc. of the USENIX Symposium on Operating Systems Design and Implementation (OSDI), pp.17-30, 2012.

J. E. Gonzalez, Y. Low, H. Gu, D. Bickson, G. et al., PowerGraph: Distributed Graph-parallel Computation on Natural Graphs, OSDI, pp.17-30, 2012.

M. Gori and A. Pucci, Itemrank: A random-walk based scoring algorithm for recommender engines, Proceedings of the 20th International Joint Conference on Artifical Intelligence, IJCAI'07, pp.2766-2771, 2007.

M. Grandjean, The digital humanities network on twitter: Following or being
URL : https://hal.archives-ouvertes.fr/hal-01527505

Q. Grossetti, C. Constantin, C. Du-mouza, T. , and N. , An Homophilybased Approach for Fast Post Recommendation in Microblogging Systems, International Conference on Extending Database Technology, EDBT, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01679120

A. Gubichev, S. Bedathur, S. Seufert, and G. Weikum, Fast and accurate estimation of shortest paths in large graphs, Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM '10, pp.499-508, 2010.

J. Han and J. Wen, Mining frequent neighborhood patterns in a large labeled graph, Proceedings of the 22Nd ACM International Conference on Information & Knowledge Management, CIKM '13, pp.259-268, 2013.

M. T. Heith and P. Raghavan, A cartesian parallel nested dissection algorithm, 1992.

B. Hendrickson and R. Leland, A multilevel algorithm for partitioning graphs, Proceedings of the 1995 ACM/IEEE Conference on Supercomputing, Supercomputing '95, 1995.
DOI : 10.1145/224170.224228

M. Jamali and M. Ester, TrustWalker: a Random Walk Model for Combining Trust-based and Item-based Recommendation, Proc. of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp.397-406, 2009.

D. Janke, S. Staab, and M. Thimm, On data placement strategies in distributed rdf stores, Proceedings of The International Workshop on Semantic Big Data, SBD '17, vol.1, pp.1-1, 2017.
DOI : 10.1145/3066911.3066915

G. Jeh and J. Widom, Simrank: A measure of structural-context similarity, Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '02, pp.538-543, 2002.

G. Jeh and J. Widom, Scaling Personalized Web Search, Proc. of the International World Wide Web Conference (WWW), pp.271-279, 2003.
DOI : 10.1145/775152.775191

U. Kang, H. Tong, J. Sun, C. Lin, F. et al., Gbase: An efficient analysis platform for large graphs, The VLDB Journal, vol.21, issue.5, pp.637-650, 2012.

R. Kannan, S. Vempala, and A. Vetta, On clusterings: Good, bad and spectral, J. ACM, vol.51, issue.3, pp.497-515, 2004.

G. Karypis and V. Kumar, Multilevel graph partitioning schemes, Proc. 24th Intern. Conf. Par. Proc., III, pp.113-122, 1995.

G. Karypis and V. Kumar, A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs, SIAM J. Scientific Computing, vol.20, issue.1, pp.359-392, 1998.

G. Karypis and V. Kumar, Multilevel algorithms for multi-constraint graph partitioning, Supercomputing, 1998.SC98. IEEE/ACM Conference on, pp.28-28, 1998.

B. W. Kernighan and S. Lin, An Efficient Heuristic Procedure for Partitioning Graphs, The Bell System Technical Journal, vol.49, issue.2, pp.291-307, 1970.

B. W. Kernighan and S. Lin, An efficient heuristic procedure for partitioning graphs, The Bell System Technical Journal, vol.49, issue.2, pp.291-307, 1970.

S. Kundu and J. Misra, A linear tree partitioning algorithm, SIAM Journal on Computing, vol.6, issue.1, pp.151-154, 1977.

A. Kyrola, DrunkardMob: Billions of Random Walks on Just a PC, RecSys, pp.257-264, 2013.

A. Lancichinetti, S. Fortunato, R. , and F. , Benchmark graphs for testing community detection algorithms, Physical Review E, vol.78, issue.4, p.46110, 2008.

N. Lao, T. M. Mitchell, and W. W. Cohen, Random Walk Inference and Learning in A Large Scale Knowledge Base, EMNLP, pp.529-539, 2011.

K. Lee and L. Liu, Efficient data partitioning model for heterogeneous graphs in the cloud, 2013 SC-International Conference for High Performance Computing, Networking, Storage and Analysis (SC), pp.1-12, 2013.

J. Leskovec, K. J. Lang, A. Dasgupta, and M. W. Mahoney, Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters, 2008.

L. Li, R. Geda, A. B. Hayes, Y. Chen, P. Chaudhari et al., A simple yet effective balanced edge partition model for parallel computing, Proc. ACM Meas. Anal. Comput. Syst, vol.1, issue.1, p.21, 2017.

R. H. Li, J. X. Yu, M. , and R. , Efficient core maintenance in large dynamic graphs, IEEE Transactions on Knowledge and Data Engineering, vol.26, issue.10, pp.2453-2465, 2014.
DOI : 10.1109/tkde.2013.158
URL : http://arxiv.org/pdf/1207.4567

Y. Li, C. Constantin, and C. Mouza, Sgvcut: A vertex-cut partitioning tool for random walks-based computations over social network graphs, Proceedings of the 29th International Conference on Scientific and Statistical Database Management, SSDBM '17, vol.39, pp.1-39, 2017.
DOI : 10.1007/978-3-319-48743-4_22
URL : https://hal.archives-ouvertes.fr/hal-01515676

Y. Li, C. Constantin, and C. Mouza, Un partitionnement d'arêtes à base de blocs pour les algorithmes de marches aléatoires dans les grands graphes sociaux, vol.22, pp.89-113, 2017.

Y. Li, C. Constantin, and C. Mouza, A block-based edge partitioning for random walks algorithms over large social graphs, Proceedings of the 17th International Conference on Web Information Systems Engineering, vol.10042, pp.275-289, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01398189

Y. Lim, U. Kang, F. , and C. , Slashburn: Graph compression and mining beyond caveman communities, vol.26, pp.3077-3089, 2014.
DOI : 10.1109/tkde.2014.2320716

Y. Lim, W. Lee, H. Choi, and U. Kang, Mtp: discovering high quality partitions in real world graphs, World Wide Web, vol.20, issue.3, pp.491-514, 2017.
DOI : 10.1007/s11280-016-0393-1

Y. Low, D. Bickson, J. Gonzalez, C. Guestrin, A. Kyrola et al., Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud, Proc. VLDB Endow, vol.5, issue.8, pp.716-727, 2012.

Y. Low, J. Gonzalez, A. Kyrola, D. Bickson, C. Guestrin et al., Graphlab: A new framework for parallel machine learning, Proceedings of the TwentySixth Conference on Uncertainty in Artificial Intelligence, UAI'10, pp.340-349, 2010.

Y. Low, J. Gonzalez, A. Kyrola, D. Bickson, C. Guestrin et al., Distributed GraphLab: A Framework for Machine Learning in the Cloud, Proc. of the VLDB Endowment (PVLDB), vol.5, pp.716-727, 2012.

G. Malewicz, M. H. Austern, A. J. Bik, J. C. Dehnert, I. Horn et al., Pregel: a System for Large-scale Graph Processing, Proc. of the ACM Symposium on Principles of Distributed Computing (PODC), p.6, 2009.

D. Margo and M. Seltzer, A scalable distributed graph partitioner, Proc. VLDB Endow, vol.8, issue.12, pp.1478-1489, 2015.

H. Meyerhenke, B. Monien, and T. Sauerwald, A new diffusion-based multilevel algorithm for computing graph partitions, J. Parallel Distrib. Comput, vol.69, issue.9, pp.750-761, 2009.

G. L. Miller, S. Teng, and S. A. Vavasis, A unified geometric approach to graph separators, Proceedings of the 32Nd Annual Symposium on Foundations of Computer Science, SFCS '91, pp.538-547, 1991.

L. Muchnik, S. Pei, L. C. Parra, S. D. Reis, J. Andrade et al., Origins of power-law degree distribution in the heterogeneity of human activity in social networks, vol.3, p.1783, 2013.

M. Newman, A. Barabasi, and D. J. Watts, The Structure and Dynamics of Networks: (Princeton Studies in Complexity), 2006.

M. Newman, A. Barabasi, and D. J. Watts, The Structure and Dynamics of Networks: (Princeton Studies in Complexity), 2006.

X. Pan, D. Papailiopoulos, S. Oymak, B. Recht, K. Ramchandran et al., Parallel correlation clustering on big graphs, Proceedings of the 28th International Conference on Neural Information Processing Systems, NIPS'15, pp.82-90, 2015.

B. Pattabiraman, M. M. Patwary, A. H. Gebremedhin, W. Liao, C. et al., Fast Algorithms for the Maximum Clique Problem on Massive Sparse Graphs, pp.156-169, 2013.
DOI : 10.1007/978-3-319-03536-9_13
URL : http://cucis.ece.northwestern.edu/publications/pdf/PatPat13.pdf

B. Perozzi, R. Al-rfou, and S. Skiena, Deepwalk: Online learning of social representations, Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '14, pp.701-710, 2014.

F. Petroni, L. Querzoni, K. Daudjee, S. Kamali, and G. Iacoboni, Hdrf: Stream-based partitioning for power-law graphs, Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM '15, pp.243-252, 2015.

A. Pothen, H. D. Simon, and K. Liou, Partitioning sparse matrices with eigenvectors of graphs, SIAM J. Matrix Anal. Appl, vol.11, issue.3, pp.430-452, 1990.
DOI : 10.1137/0611030

A. Roy, L. Bindschaedler, J. Malicevic, and W. Zwaenepoel, Chaos: Scale-out Graph Processing from Secondary Storage, Proc. of the Symposium on Operating Systems Principles (SOSP), pp.410-424, 2015.

Y. Saad, Sparskit: a basic tool kit for sparse matrix computations-version 2, 1994.

H. P. Sajjad, A. H. Payberah, F. Rahimian, V. Vlassov, and S. Haridi, Boosting vertex-cut partitioning for streaming graphs, 2016 IEEE International Congress on Big Data, pp.1-8, 2016.

C. Sakouhi, S. Aridhi, A. Guerrieri, S. Sassi, and A. Montresor, Dynamicdfep: A distributed edge partitioning approach for large dynamic graphs, Proceedings of the 20th International Database Engineering & Applications Symposium, IDEAS '16, pp.142-147, 2016.

S. Salihoglu and J. Widom, GPS: a Graph Processing System, Proc. of the Conference on Scientific and Statistical Database Management (SSDBM), vol.22, p.12, 2013.

H. Saran and V. V. Vazirani, Finding $k$ cuts within twice the optimal, SIAM J. Comput, vol.24, issue.1, pp.101-108, 1995.
DOI : 10.1109/sfcs.1991.185443
URL : http://www.cc.gatech.edu/%7Evazirani/k-cut.pdf

P. Sarkar and A. W. Moore, Fast Nearest-neighbor Search in Disk-resident Graphs, Proc. of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp.513-522, 2010.
DOI : 10.1145/1835804.1835871
URL : http://reports-archive.adm.cs.cmu.edu/anon/ml2010/CMU-ML-10-100.pdf

A. D. Sarma, S. Gollapudi, P. , and R. , Estimating PageRank on Graph Streams, Proc. of the ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS), pp.69-78, 2008.
DOI : 10.1145/1970392.1970397
URL : http://theory.stanford.edu/~rinap/papers/pagerankpods.pdf

S. E. Schaeffer, Survey: Graph clustering, Comput. Sci. Rev, vol.1, issue.1, pp.27-64, 2007.

C. Shi, Y. Li, J. Zhang, Y. Sun, Y. et al., A survey of heterogeneous information network analysis, IEEE Transactions on Knowledge and Data Engineering, vol.29, issue.1, pp.17-37, 2017.

H. Simon, Partitioning of unstructured problems for parallel processing, Parallel Methods on Large-scale Structural Analysis and Physics Applications, vol.2, pp.135-148, 1991.

J. Sun, H. Vandierendonck, and D. S. Nikolopoulos, Graphgrind: Addressing load imbalance of graph partitioning, Proceedings of the International Conference on Supercomputing, ICS '17, vol.16, pp.1-16, 2017.

Y. Sun and J. Han, Mining Heterogeneous Information Networks:Principles and Methodologies, 2012.

Y. Sun, J. Han, P. Zhao, Z. Yin, H. Cheng et al., Rankclus: Integrating clustering with ranking for heterogeneous information network analysis, Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, EDBT '09, pp.565-576, 2009.

Y. Sun, B. Norick, J. Han, X. Yan, P. S. Yu et al., Integrating meta-path selection with user-guided object clustering in heterogeneous information networks, Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '12, pp.1348-1356, 2012.

L. Takac and M. Zabovsky, Data Analysis in Public Social Networks. Present Day Trends of Innovations, pp.1-6, 2012.

J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan et al., Line: Large-scale information network embedding, Proceedings of the 24th International Conference on World Wide Web, WWW '15, pp.1067-1077, 2015.

Y. Tian, A. Balmin, S. A. Corsten, S. Tatikonda, and J. Mcpherson, From "think like a vertex" to "think like a graph, Proc. VLDB Endow, vol.7, pp.193-204, 2013.

C. Tsourakakis, C. Gkantsidis, B. Radunovic, and M. Vojnovic, Fennel: Streaming graph partitioning for massive scale graphs, Proceedings of the 7th ACM International Conference on Web Search and Data Mining, WSDM '14, pp.333-342, 2014.

J. C. Urschel and L. T. Zikatanov, Spectral bisection of graphs and connectedness, Linear Algebra and Its Applications, vol.449, pp.1-16, 2014.

L. G. Valiant, A bridging model for parallel computation, Commun. ACM, vol.33, issue.8, pp.103-111, 1990.

L. G. Valiant, A Bridging Model for Multi-core Computing, Proc. of the Annual European Symposium Algorithms-ESA, pp.13-28, 2008.

X. Wan and J. Yang, Multi-document summarization using cluster-based link analysis, Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '08, pp.299-306, 2008.

Y. Wang, X. Lin, and Q. Zhang, Towards metric fusion on multi-view data: A cross-view based graph random walk approach, Proceedings of the 22Nd ACM International Conference on Information & Knowledge Management, CIKM '13, pp.805-810, 2013.

J. J. Whang, D. F. Gleich, and I. S. Dhillon, Overlapping Community detection Using Seed set Expansion, Proc. of the ACM International Conference on Information and Knowledge Management (CIKM), pp.2099-2108, 2013.

C. Xie, L. Yan, W. Li, Z. ;. Zhang, M. Welling et al., Distributed power-law graph computing: Theoretical and empirical analysis, Advances in Neural Information Processing Systems, vol.27, pp.1673-1681, 2014.

R. S. Xin, J. E. Gonzalez, M. J. Franklin, and I. Stoica, GraphX: a Resilient Distributed Graph System on Spark, Proc. of the International SIGMOD Workshop on Graph Data Management Experiences and Systems (GRADES), p.2, 2013.

R. S. Xin, J. E. Gonzalez, M. J. Franklin, and I. Stoica, GraphX: A Resilient Distributed Graph System on Spark, GRADES, vol.2, pp.1-2, 2013.

D. Yan, J. Cheng, Y. Lu, and W. Ng, Blogel: A block-centric framework for distributed computation on real-world graphs, Proc. VLDB Endow, vol.7, pp.1981-1992, 2014.

J. Yan and P. Hsiao, A new fuzzy-clustering-based approach for twoway circuit partitioning, Proceedings of the 8th International Conference on VLSI Design, VLSID '95, p.359, 1995.

J. Yang, L. Chen, and J. Zhang, Fctclus: A fast clustering algorithm for heterogeneous information networks, PLoS ONE, vol.10, issue.6, p.130086, 2015.
DOI : 10.1371/journal.pone.0130086
URL : https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0130086&type=printable

J. Yang and J. Leskovec, Defining and evaluating network communities based on ground-truth, Knowl. Inf. Syst, vol.42, issue.1, pp.181-213, 2015.
DOI : 10.1145/2350190.2350193

S. Yang, X. Yan, B. Zong, and A. Khan, Towards Effective Partition Management for Large Graphs, Proc. of the ACM SIGMOD International Conference on Management of Data (SIGMOD), pp.517-528, 2012.

M. Zaharia, M. Chowdhury, T. Das, A. Dave, J. Ma et al., Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing, Proc. of the USENIX Symposium on Networked Systems Design and Implementation (NSDI), pp.15-28, 2012.

M. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker, and I. Stoica, Spark: Cluster computing with working sets, Proceedings of the 2Nd USENIX Conference on Hot Topics in Cloud Computing, HotCloud'10, pp.10-10, 2010.

C. Zhang, F. Wei, Q. Liu, Z. G. Tang, L. et al., Graph edge partitioning via neighborhood heuristic, Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '17, pp.605-614, 2017.

Y. Zhou and L. Liu, Social influence based clustering of heterogeneous information networks, Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '13, pp.338-346, 2013.

X. Zhu, W. Chen, W. Zheng, M. , and X. , Gemini: A computation-centric distributed graph processing system, 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), pp.301-316, 2016.

L. Zou, L. Chen, and M. T. Özsu, Distance-join: Pattern match query in a large graph database, Proc. VLDB Endow, vol.2, pp.886-897, 2009.

R. Zou and L. B. Holder, Frequent subgraph mining on a single large graph using sampling techniques, Proceedings of the Eighth Workshop on Mining and Learning with Graphs, MLG '10, pp.171-178, 2010.