Marlene Ida Damm
RWTH Aachen University
marlene.damm@rwth-aachen.de


Die folgende Tabelle soll einen Überblick über die bereits in vorherigen Kapiteln angesprochenen Algorithmentypen und Anwendungsfelder bieten. Die eingeordneten Referenzen zeigen an, in welchem Anwendungsfeld ein Algorithmus benutzt wird und welche grundlegenden Algorithmentypen dieser Algorithmus aufweist. Es ist möglich, dass eine Referenz in unterschiedlichen Bereichen eingeordnet wurde, da manche Algorithmen unterschiedliche Algorithmentypen vereinen oder in unterschiedlichen Anwendungsfeldern bereits verwendet wurden. Leere Zellen in der Tabelle zeigen an, dass in diesen Bereichen noch nicht geforscht wurde.

Die Tabelle ist im Rahmen einer Bachelorarbeit entstanden und wird automatisch aus einer citavi Datenbank des Lehrstuhls generiert. Neu veröffentlichte Literatur kann so schnell eingebunden werden.


Anwendungsfelder


Algorithmentypen
Statische NetzwerkeDynamische, evolutionäre NetzwerkeExperten IdentifikationGehirn-NetzwerkGen/Metabolismus-Netzwerkevorzeichenbehaftete NetzwerkeInternet of Thingsmehrschichtige, gewichtete Netzwerke mit AttributenPolitische/ Wirtschaftliche NetwerkeProtein NetzwerkeStreamsWissenschaftsnetwerkeImplementiert in WebOCD
Ameisenkolonie Optimierung(Noveiri et al., 2019)
(Zhou et al., 2015)
(Liu et al., 2019)
(Ke et al., 2013)
(Belkhiri et al., 2019)
(Belkhiri et al., 2017)
(Huang et al., 2017)
----(Cai et al., 2014)------(Ke et al., 2013)
Cliquen-basierte Algorithmen(Wan et al., 2008)
(Ma & Fan, 2020)
(Zhang et al., 2017)
(Ahn et al., 2010)
(Ke et al., 2013)
(Ming-Sheng et al., 2010)
(Havemann et al., 2011)
(Nguyen et al., 2011)
(Kumpula et al., 2008)
---(Adamcsek et al., 2006)----(Adamcsek et al., 2006)
(Fan et al., 2012)
--(Zhang et al., 2017)
(Ahn et al., 2010)
(Ke et al., 2013)
(Havemann et al., 2011)
(Nguyen et al., 2011)
Deep Learning(Tian et al., 2014)
(Palash Goyal, 2018)
(Wang et al., 2016)
--------(Palash Goyal, 2018)-(Palash Goyal, 2018)-
Distanz-basierte Algorithmen(Belkhiri et al., 2016)
(Chen et al., 2018)
(Jia et al., 2014)
(Zhu et al., 2014)
(Wu et al., 2015)
(Jia et al., 2017)
(Liu & Liu, 2010)
(Xiang et al., 2019)
(Liu et al., 2019)
(Wang et al., 2017)
(Kumar et al., 2017)
(Whang et al., 2016)
(Saha & Ghrera, 2015)
(Li et al., 2015)
--(Wang et al., 2017)(Wang et al., 2017)----(Wang et al., 2017)-(Šubelj & Bajec, 2013)
(Dilmaghani et al., 2019)
(Whang et al., 2016)
Dynamische Netzwerke Algorithmen(Seifikar et al., 2020)
(Cai et al., 2016)
(Cai et al., 2016)---(Cai et al., 2016)-------
Evolutionäre Algorihmen(Wu & Pan, 2015)
(Shi, Chuan and Cai, Yanan and Di Fu and Dong, Yuxiao and Wu, Bin, 2013)
(Ke et al., 2013)
(Cai et al., 2016)
(Bettinelli et al., 2015)
(Francisquini et al., 2017)
(Liu et al., 2014)
(Cai et al., 2016)
(Aston & Hu, 2017)
---(Cai et al., 2016)------(Ke et al., 2013)
(Liu et al., 2014)
Fluss-basierte Algorithmen(Bae et al., 2017)(Rosvall et al., 2014)
(Rosvall & Bergstrom, 2011)
-----------
Graph Embeddings(Nguyen & Tirthapura, 2018)
(Tian et al., 2014)
(Rossi et al., 2019)
(Keikha et al., 2018)
(Bhowmick et al., 2020)
(Grover & Leskovec, 2016)
(He et al., 2018)
(Tsitsulin et al., 2018)
(Palash Goyal, 2018)
(Wang et al., 2016)
(Benedek Rozemberczki et al., 2019)
(Cavallari et al., 2017)
-------(Bai et al., 2019)(Palash Goyal, 2018)-(Bai et al., 2019)
(Palash Goyal, 2018)
-
Label Propagierung(Shahriari et al., 2015)
(Francisquini et al., 2017)
(Xie et al., 2011)
----(Shahriari et al., 2015)------(Shahriari et al., 2015)
(Xie et al., 2011)
Louvain Algorithmen(Blondel et al., 2008)
(Seifikar et al., 2020)
(Poulin & Théberge, 2019)
(Traag et al., 2019)
(Poulin & Théberge, 2019)
(Bhowmick et al., 2020)
(Held et al., 2016)----------(Blondel et al., 2008)
Minimum Spanning Tree-basierte Algorithmen(Basuchowdhuri et al., 2015)------------
nicht-negative Matrix Faktorisierung-------------
Optimierung der Modularität(Belkhiri et al., 2016)
(Zhang & Li, 2007)
(A. Tabrizi et al., 2013)
(Belkhiri et al., 2019)
(Belkhiri et al., 2017)
-----------(Zhang & Li, 2007)
Random Walk Algorithmen(Jin et al., 2011)
(Gleich & Kloster, 2016)
(Keikha et al., 2018)
(A. Tabrizi et al., 2013)
(Stanoev et al., 2011)
(Emmons & Mucha, 2019)
(Whang et al., 2016)
(Palash Goyal, 2018)
(Shahriari et al., 2015)
(Cai et al., 2011)
(Rosvall et al., 2014)
(Lambiotte et al., 2014)
(Sarantopoulos et al., 2019)
(Tshimula et al., 2019)
(Rosvall & Bergstrom, 2011)
---(Shahriari et al., 2015)--(Bai et al., 2019)(Palash Goyal, 2018)-(Bai et al., 2019)
(Palash Goyal, 2018)
(Stanoev et al., 2011)
(Whang et al., 2016)
(Shahriari et al., 2015)
Spektrales Clustering(van Lierde et al., 2020)
(Cheng et al., 2016)
(Mavroeidis, 2010)
(Zhang & Li, 2007)
(Belkin & Niyogi, 2001)
(Shen & Cheng, 2010)
(Anandkumar et al., 2013)
(Brandes & Lerner, 2010)
(Kawamoto & Kabashima, 2015)
-----------(Zhang & Li, 2007)
Spieltheoretische Algorithmen(Jonnalagadda & Kuppusamy, 2016)
(Basu & Maulik, 2015)
(Zhou et al., 2015)
(Chen et al., 2010)
(Jonnalagadda & Kuppusamy, 2016)
(Alvari et al., 2014)
(Rota Bulò & Pelillo, 2013)
---------(Basu & Maulik, 2015)-
Stochastische Block Modelle(Tandon et al., 2019)
(Prokhorenkova & Tikhonov, 2019)
(Brutz & Meyer, 2015)
(Zhai et al., 2019)
(Karrer & Newman, 2011)
(Yang & Leskovec, 2014)
(Brandes & Lerner, 2010)
(Gopalan & Blei, 2013)
(Matias & Miele, 2017)
(Yang et al., 2011)
(Ghasemian et al., 2016)
(Xu & Hero, 2014)
-----------
Probabilistic Mixture Algorithmen(Chen et al., 2014)----(Chen et al., 2014)------(Chen et al., 2014)

Referenzen

  1. Noveiri, E., Naderan, M., & Alavi, S. E. (2019). ACFC: ant colony with fuzzy clustering algorithm for community detection in social networks. Int. J. Ad Hoc Ubiquitous Comput, 31(1), 36. https://doi.org/10.1504/IJAHUC.2019.099636
  2. Zhou, X., Liu, Y., Zhang, J., Liu, T., & Di Zhang. (2015). An ant colony based algorithm for overlapping community detection in complex networks. Physica A: Statistical Mechanics and Its Applications, 427, 289–301. https://doi.org/10.1016/j.physa.2015.02.020
  3. Liu, R., Liu, J., & He, M. (2019). A multi-objective ant colony optimization with decomposition for community detection in complex networks. Transactions of the Institute of Measurement and Control, 41(9), 2521–2534. https://doi.org/10.1177/0142331218804002
  4. Ke, L., Zhang, Q., & Battiti, R. (2013). MOEA/D-ACO: a multiobjective evolutionary algorithm using decomposition and AntColony. IEEE Transactions on Cybernetics, 43(6), 1845–1859. https://doi.org/10.1109/TSMCB.2012.2231860
  5. Belkhiri, Y., Kamel, N., & Drias, H. (2019). Multi-swarm BSO Algorithm with Local Search for Community Detection Problem in Complex Environment. In Computational Collective Intelligence (Vol. 11684, pp. 321–332). Springer International Publishing. https://doi.org/10.1007/978-3-030-28374-2_28
  6. Belkhiri, Y., Kamel, N., Drias, H., & Yahiaoui, S. (2017). Bee Swarm Optimization for Community Detection in Complex Network. In Recent Advances in Information Systems and Technologies (Vol. 570, pp. 73–85). Springer International Publishing. https://doi.org/10.1007/978-3-319-56538-5_8
  7. Huang, F., Li, X., Zhang, S., Zhang, J., Chen, J., & Zhai, Z. (2017). Overlapping Community Detection for Multimedia Social Networks. IEEE Transactions on Multimedia, 19(8), 1881–1893. https://doi.org/10.1109/TMM.2017.2692650
  8. Cai, Q., Gong, M., Shen, B., Ma, L., & Jiao, L. (2014). Discrete particle swarm optimization for identifying community structures in signed social networks. Neural Networks : the Official Journal of the International Neural Network Society, 58, 4–13. https://doi.org/10.1016/j.neunet.2014.04.006
  9. Wan, L., Liao, J., & Zhu, X. (2008). CDPM: Finding and Evaluating Community Structure in Social Networks. Advanced Data Mining and Applications, Proceedings, 5139, 620–627.
  10. Ma, J., & Fan, J. (2020). Local Optimization for Clique-Based Overlapping Community Detection in Complex Networks. IEEE Access, 8, 5091–5103. https://doi.org/10.1109/ACCESS.2019.2962751
  11. Zhang, X., Wang, C., Su, Y., Pan, L., & Zhang, H.-F. (2017). A Fast Overlapping Community Detection Algorithm Based on Weak Cliques for Large-Scale Networks. IEEE Transactions on Computational Social Systems, 4(4), 218–230. https://doi.org/10.1109/TCSS.2017.2749282
  12. Ahn, Y.-Y., Bagrow, J. P., & Lehmann, S. (2010). Link communities reveal multiscale complexity in networks. Nature, 466(7307), 761–764. https://doi.org/10.1038/nature09182
  13. Ming-Sheng, S., Duan-Bing, C., & Tao, Z. (2010). Detecting Overlapping Communities Based on Community Cores in Complex Networks. Proceedings of the National Academy of Sciences, 27(5), 058901. https://doi.org/10.1088/0256-307X/27/5/058901
  14. Havemann, F., Heinz, M., Struck, A., & Gläser, J. (2011). Identification of overlapping communities and their hierarchy by locally calculating community-changing resolution levels. Journal of Statistical Mechanics: Theory and Experiment. https://doi.org/10.1088/1742-5468/2011/01/P01023
  15. Nguyen, N. P., Dinh, T. N., Thai, M. T., & Nguyen, D. T. (2011). Overlapping Community Structures and Their Detection on Social Networks. 2011 IEEE Third International Conference on Social Computing (SocialCom), 35–40. https://doi.org/10.1109/PASSAT/SocialCom.2011.16
  16. Kumpula, J. M., Kivela, M., Kaski, K., & Saramaki, J. (2008). Sequential algorithm for fast clique percolation. Physical Review, 78(2), -. https://doi.org/10.1103/PhysRevE.78.026109
  17. Adamcsek, B., Palla, G., Farkas, I. J., Derényi, I., & Vicsek, T. (2006). CFinder: locating cliques and overlapping modules in biological networks. Bioinformatics (Oxford, England), 22(8), 1021–1023. https://doi.org/10.1093/bioinformatics/btl039
  18. Fan, M., Wong, K.-C., Ryu, T., Ravasi, T., & Gao, X. (2012). SECOM: A Novel Hash Seed and Community Detection Based-Approach for Genome-Scale Protein Domain Identification. PLOS ONE, 7(6). https://doi.org/10.1371/journal.pone.0039475
  19. Tian, F., Gao, B., Cui, Q., Chen, E., & Liu, T.-Y. (Eds.). (2014). Learning Deep Representations for Graph Clustering. AAAI Press.
  20. Palash Goyal, E. F. (2018). Graph Embedding Techniques, Applications, and Performance: A Survey (Number 151). https://doi.org/10.1016/j.knosys.2018.03.022
  21. Wang, D., Cu, P., & Zhu, W. (2016). Structural Deep Network Embedding. Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1225–1234. http://doi.acm.org/10.1145/2939672.2939753
  22. Belkhiri, Y., Kamel, N., & Drias, H. (2016). A New Betweenness Centrality Algorithm with Local Search for Community Detection in Complex Network. In N. T. Nguyen, B. Trawiński, H. Fujita, & T.-P. Hong (Eds.), Intelligent Information and Database Systems (Vol. 9622, pp. 268–276). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-49390-8_26
  23. Chen, L., Zhang, J., & Cai, L.-J. (2018). Overlapping community detection based on link graph using distance dynamics. International Journal of Modern Physics B, 32(03). https://doi.org/10.1142/S0217979218500157
  24. Jia, S., Gao, L., Gao, Y., & Wang, H. (2014). Anti-triangle centrality-based community detection in complex networks. IET Systems Biology, 8(3), 116–125. https://doi.org/10.1049/iet-syb.2013.0039
  25. Zhu, Y., Li, D., Xu, W., Wu, W., Fan, L., & Willson, J. (2014). Mutual-Relationship-Based Community Partitioning for Social Networks. IEEE Transactions on Emerging Topics in Computing, 2(4), 436–447. https://doi.org/10.1109/TETC.2014.2380391
  26. Wu, J., Hou, Y., Jiao, Y., Li, Y., Li, X., & Jiao, L. (2015). Density shrinking algorithm for community detection with path based similarity. Physica A: Statistical Mechanics and Its Applications, 433, 218–228. https://doi.org/10.1016/j.physa.2015.03.044
  27. Jia, S., Gao, L., Gao, Y., Nastos, J., Wen, X., Zhang, X., & Wang, H. (2017). Exploring triad-rich substructures by graph-theoretic characterizations in complex networks. Physica A: Statistical Mechanics and Its Applications, 468, 53–69. https://doi.org/10.1016/j.physa.2016.10.021
  28. Liu, J., & Liu, T. (2010). Coarse-grained diffusion distance for community structure detection in complex networks. Journal of Statistical Mechanics: Theory and Experiment, 2010(12). https://doi.org/10.1088/1742-5468/2010/12/P12030
  29. Xiang, B., Guo, K., Liu, Z., & Liao, Q. (2019). An Overlapping Community Detection Algorithm Based on Triangle Coarsening and Dynamic Distance. In Computer Supported Cooperative Work and Social Computing (Vol. 917, pp. 285–300). Springer Singapore. https://doi.org/10.1007/978-981-13-3044-5_21
  30. Liu, Z., Xiang, B., Guo, W., Chen, Y., Guo, K., & Zheng, J. (2019). Overlapping Community Detection Algorithm Based on Coarsening and Local Overlapping Modularity. IEEE Access, 7, 57943–57955. https://doi.org/10.1109/ACCESS.2019.2912182
  31. Wang, X. F., Liu, G., Li, J., & Nees, J. P. (2017). Locating Structural Centers: A Density-Based Clustering Method for Community Detection. PLOS ONE, 12(1). https://doi.org/10.1371/journal.pone.0169355
  32. Kumar, P., Gupta, S., & Bhasker, B. (2017). An upper approximation based community detection algorithm for complex networks. Decision Support Systems, 96, 103–118. https://doi.org/10.1016/j.dss.2017.02.010
  33. Whang, J. J., Gleich, D. F., & Dhillon, I. S. (2016). Overlapping Community Detection Using Neighborhood-Inflated Seed Expansion. IEEE Transactions on Knowledge and Data Engineering, 28(5), 1272–1284. https://doi.org/10.1109/TKDE.2016.2518687
  34. Saha, S., & Ghrera, S. (2015). Network Community Detection on Metric Space. Algorithms, 8(3), 680–696. https://doi.org/10.3390/a8030680
  35. Li, Y., Jia, C., & Yu, J. (2015). A parameter-free community detection method based on centrality and dispersion of nodes in complex networks. Physica A: Statistical Mechanics and Its Applications, 438, 321–334. https://doi.org/10.1016/j.physa.2015.06.043
  36. Šubelj, L., & Bajec, M. (2013). Model of Complex Networks Based on Citation Dynamics. Proceedings of the 22nd International Conference on World Wide Web, 527–530. https://doi.org/10.1145/2487788.2487987
  37. Dilmaghani, S., Brust, M. R., Piyatumrong, A., Danoy, G., & Bouvry, P. (2019). Link Definition Ameliorating Community Detection in Collaboration Networks. Frontiers in Big Data, 2, 115. https://doi.org/10.3389/fdata.2019.00022
  38. Seifikar, M., Farzi, S., & Barati, M. (2020). C-Blondel: An Efficient Louvain-Based Dynamic Community Detection Algorithm. IEEE Transactions on Computational Social Systems, 1–11. https://doi.org/10.1109/TCSS.2020.2964197
  39. Cai, Q., Ma, L., Gong, M., & Tian, D. (2016). A survey on network community detection based on evolutionary computation. International Journal of Bio-Inspired Computation, 8(2), 84. https://doi.org/10.1504/IJBIC.2016.076329
  40. Wu, P., & Pan, L. (2015). Multi-objective community detection based on memetic algorithm. PLOS ONE, 10(5), e0126845. https://doi.org/10.1371/journal.pone.0126845
  41. Shi, Chuan and Cai, Yanan and Di Fu and Dong, Yuxiao and Wu, Bin. (2013). A link clustering based overlapping community detection algorithm. Data & Knowledge Engineering, 87, 394–404. https://doi.org/10.1016/j.datak.2013.05.004
  42. Bettinelli, A., Hansen, P., & Liberti, L. (2015). Community detection with the weighted parsimony criterion. Journal of Systems Science and Complexity, 28(3), 517–545. https://doi.org/10.1007/s11424-015-2169-6
  43. Francisquini, R., Rosset, V., & Nascimento, M. C. V. (2017). GA-LP: A genetic algorithm based on Label Propagation to detect communities in directed networks. Expert Systems with Applications, 74, 127–138. https://doi.org/10.1016/j.eswa.2016.12.039
  44. Liu, C., Liu, J., & Jiang, Z. (2014). A multiobjective evolutionary algorithm based on similarity for community detection from signed social networks. IEEE Transactions on Cybernetics, 44(12), 2274–2287. https://doi.org/10.1109/TCYB.2014.2305974
  45. Aston, N., & Hu, W. (2017). Community Detection in Dynamic Social Networks. Communications and Network, 43, 124–136. https://doi.org/10.4236/cn.2014.62015
  46. Bae, S.-H., Halperin, D., West, J. D., Rosvall, M., & Howe, B. (2017). Scalable and Efficient Flow-Based Community Detection for Large-Scale Graph Analysis. ACM Transactions on Knowledge Discovery from Data, 11(3), 1–30. https://doi.org/10.1145/2992785
  47. Rosvall, M., Esquivel, A. V., Lancichinetti, A., West, J. D., & Lambiotte, R. (2014). Memory in network flows and its effects on spreading dynamics and community detection. Nature Communications, 5, 4630. https://doi.org/10.1038/ncomms5630
  48. Rosvall, M., & Bergstrom, C. T. (2011). Multilevel Compression of Random Walks on Networks Reveals Hierarchical Organization in Large Integrated Systems. PLOS ONE, 6(4). https://doi.org/10.1371/journal.pone.0018209
  49. Nguyen, T. D., & Tirthapura, S. (Eds.). (2018). V2V: Vector Embedding of a Graph and Applications. https://doi.org/10.1109/IPDPSW.2018.00182
  50. Rossi, R. A., Di Jin, Kim, S., Ahmed, N. K., Koutra, D., & Lee, J. B. (2019). From Community to Role-based Graph Embeddings. https://doi.org/10.1145/3397191
  51. Keikha, M. M., Rahgozar, M., & Asadpour, M. (2018). Community aware random walk for network embedding. Knowledge-Based Systems, 148, 47–54. https://doi.org/10.1016/j.knosys.2018.02.028
  52. Bhowmick, A. K., Meneni, K., Danisch, M., Guillaume, J.-L., & Mitra, B. (2020). LouvainNE: Hierarchical Louvain Method for High Quality and Scalable Network Embedding. In WSDM ’20: Proceedings of the 13th International Conference on Web Search and Data Mining (pp. 43–51). https://doi.org/10.1145/3336191.3371800
  53. Grover, A., & Leskovec, J. (2016). node2vec: Scalable Feature Learning for Networks. Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 855–864. http://doi.acm.org/10.1145/2939672.2939754
  54. He, D., Yang, X., Feng, Z., Chen, S., & Fogelman-Soulié, F. (2018). A Network Embedding-Enhanced Approach for Generalized Community Detection. In W. Liu, F. Giunchiglia, & B. Yang (Eds.), Knowledge Science, Engineering and Management (Vol. 11062, pp. 383–395). Springer. https://doi.org/10.1007/978-3-319-99247-1_34
  55. Tsitsulin, A., Mottin, D., Karras, P., & Müller, E. (2018). VERSE: Versatile Graph Embeddings from Similarity Measures. Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW ’18, 539–548. https://doi.org/10.1145/3178876.3186120
  56. Benedek Rozemberczki, Ryan Davies, Rik Sarkar, & Charles Sutton. (2019). GEMSEC: Graph Embedding with Self Clustering. ASONAM ’19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 65–72. https://doi.org/10.1145/3341161.3342890
  57. Cavallari, S., Zheng, V. W., Cai, H., Chang, K. C.-C., & Cambria, E. (2017). Learning Community Embedding with Community Detection and Node Embedding on Graphs. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management - CIKM ’17, 377–386. https://doi.org/10.1145/3132847.3132925
  58. Bai, J., Li, L., & Zeng, D. (2019). HiWalk: Learning node embeddings from heterogeneous networks. Information Systems, 81, 82–91. https://doi.org/10.1016/j.is.2018.11.008
  59. Shahriari, M., Krott, S., & Klamma, R. (2015). Disassortative Degree Mixing and Information Diffusion for Overlapping Community Detection in Social Networks (DMID). In A. Gangemi, S. Leonardi, & A. Panconesi (Eds.), Proceedings of the 24th International Conference on World Wide Web Companion (pp. 1369–1374). International World Wide Web Conferences Steering Committee.
  60. Xie, J., Szymanski, B. K., & Liu, X. (2011). SLPA: Uncovering Overlapping Communities in Social Networks via A Speaker-listener Interaction Dynamic Process. Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference On.
  61. Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. J. Stat. Mech. https://doi.org/10.1088/1742-5468/2008/10/P10008
  62. Poulin, V., & Théberge, F. (2019). Ensemble clustering for graphs: comparisons and applications. Applied Network Science, 4(1), 1–13. https://doi.org/10.1007/s41109-019-0162-z
  63. Traag, V. A., Waltman, L., & van Eck, N. J. (2019). From Louvain to Leiden: guaranteeing well-connected communities. Scientific Reports, 9(1), 5233. https://doi.org/10.1038/s41598-019-41695-z
  64. Poulin, V., & Théberge, F. (2019). Ensemble Clustering for Graphs. In L. M. Aiello (Ed.), Complex networks and their applications VII (Vol. 812, pp. 231–243). Springer. https://doi.org/10.1007/978-3-030-05411-3\textunderscore 19
  65. Held, P., Krause, B., & Kruse, R. (2016). Dynamic Clustering in Social Networks using Louvain and Infomap Method. https://arxiv.org/abs/1603.02413
  66. Basuchowdhuri, P., Roy, R., Anand, S., Srivastava, D. R., Majumder, S., & Saha, S. K. (2015). Spanning tree-based fast community detection methods in social networks. Innovations in Systems and Software Engineering, 11(3), 177–186. https://doi.org/10.1007/s11334-015-0246-6
  67. Zhang, Q., & Li, H. (2007). MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition. IEEE Transactions on Evolutionary Computation, 11(6), 712–731. https://doi.org/10.1109/TEVC.2007.892759
  68. A. Tabrizi, S., Shakery, A., Asadpour, M., Abbasi, M., & Tavallaie, M. A. (2013). Personalized PageRank Clustering: A graph clustering algorithm based on random walks. Physica A: Statistical Mechanics and Its Applications, 392(22), 5772–5785. https://doi.org/10.1016/j.physa.2013.07.021
  69. Jin, D., Yang, B., Baquero, C., Liu, D., He, D., & Liu, J. (2011). Markov random walk under constraint for discovering overlapping communities in complex networks. Journal of Statistical Mechanics: Theory and Experiment, 05, -. https://doi.org/10.1088/1742-5468/2011/05/P05031
  70. Gleich, D. F., & Kloster, K. (2016). Seeded PageRank solution paths. European Journal of Applied Mathematics, 27(6), 812–845. https://doi.org/10.1017/S0956792516000280
  71. Stanoev, A., Smilkov, D., & Kocarev, L. (2011). Identifying communities by influence dynamics in social networks. Physical Review, 84(4). https://doi.org/10.1103/PhysRevE.84.046102
  72. Emmons, S., & Mucha, P. J. (2019). Map equation with metadata: Varying the role of attributes in community detection. Physical Review. E, 100(2-1), 022301. https://doi.org/10.1103/PhysRevE.100.022301
  73. Cai, B., Wang, H., & Zheng, H. (2011). An improved random walk based clustering algorithm for community detection in complex networks. 2011 IEEE International Conference on Systems, Man and Cybernetics - SMC, 2162–2167. https://doi.org/10.1109/ICSMC.2011.6083997
  74. Lambiotte, R., Delvenne, J.-C., & Barahona, M. (2014). Random Walks, Markov Processes and the Multiscale Modular Organization of Complex Networks. IEEE Transactions on Network Science and Engineering, 1(2), 76–90. https://doi.org/10.1109/TNSE.2015.2391998
  75. Sarantopoulos, I., Papatheodorou, D., Vogiatzis, D., Tzortzis, G., & Paliouras, G. (2019). TimeRank: A Random Walk Approach for Community Discovery in Dynamic Networks. In L. M. Aiello (Ed.), Complex networks and their applications VII (Vol. 812, pp. 338–350). Springer. https://doi.org/10.1007/978-3-030-05411-3_28
  76. Tshimula, J. M., Chikhaoui, B., & Wang, S. (2019). HAR-search: A Method to Discover Hidden Affinity Relationships in Online Communities. In F. Spezzano, W. Chen, & X. Xiao (Eds.), Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (pp. 176–183). ACM. https://doi.org/10.1145/3341161.3342888
  77. van Lierde, H., Chow, T. W. S., & Chen, G. (2020). Scalable Spectral Clustering for Overlapping Community Detection in Large-Scale Networks. IEEE Transactions on Knowledge and Data Engineering, 32(4), 754–767. https://doi.org/10.1109/TKDE.2019.2892096
  78. Cheng, J., Li, L., Leng, M., Lu, W., Yao, Y., & Chen, X. (2016). A divisive spectral method for network community detection. Journal of Statistical Mechanics: Theory and Experiment, 2016(3). https://doi.org/10.1088/1742-5468/2016/03/033403
  79. Mavroeidis, D. (2010). Accelerating spectral clustering with partial supervision. Data Min. Knowl. Discov., 21(2), 241–258. https://doi.org/10.1007/s10618-010-0191-9
  80. Belkin, M., & Niyogi, P. (2001). Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering. Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic, 585–591. http://dl.acm.org/citation.cfm?id=2980539.2980616
  81. Shen, H.-W., & Cheng, X.-Q. (2010). Spectral methods for the detection of network community structure: a comparative analysis. Journal of Statistical Mechanics: Theory and Experiment, 2010(10). https://doi.org/10.1088/1742-5468/2010/10/P10020
  82. Anandkumar, A., Ge, R., Hsu, D., & Kakade, S. M. (2013). A Tensor Spectral Approach to Learning Mixed Membership Community Models. ArXiv e-Prints.
  83. Brandes, U., & Lerner, J. (2010). Structural Similarity: Spectral Methods for Relaxed Blockmodeling. Journal of Classification, 27(3), 279–306. https://doi.org/10.1007/s00357-010-9062-8
  84. Kawamoto, T., & Kabashima, Y. (2015). Limitations in the spectral method for graph partitioning: Detectability threshold and localization of eigenvectors. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics, 91(6), 062803. https://doi.org/10.1103/PhysRevE.91.062803
  85. Jonnalagadda, A., & Kuppusamy, L. (2016). A survey on game theoretic models for community detection in social networks. Social Network Analysis and Mining, 6(1), 5056. https://doi.org/10.1007/s13278-016-0386-1
  86. Basu, S., & Maulik, U. (2015). Community detection based on strong Nash stable graph partition. Social Network Analysis and Mining, 5(1), 046112. https://doi.org/10.1007/s13278-015-0299-4
  87. Zhou, L., Lü, K., Yang, P., Wang, L., & Kong, B. (2015). An approach for overlapping and hierarchical community detection in social networks based on coalition formation game theory. Expert Systems with Applications, 42(24), 9634–9646. https://doi.org/10.1016/j.eswa.2015.07.023
  88. Chen, W., Liu, Z., Sun, X., & Wang, Y. (2010). A game-theoretic framework to identify overlapping communities in social networks. Data Mining and Knowledge Discovery, 21(2), 224–240. https://doi.org/10.1007/s10618-010-0186-6
  89. Alvari, H., Hajibagheri, A., & Sukthankar, G. (2014). Community detection in dynamic social networks: A game-theoretic approach. 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM): ASONAM 2014, 101–107.
  90. Rota Bulò, S., & Pelillo, M. (2013). A game-theoretic approach to hypergraph clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(6), 1312–1327. https://doi.org/10.1109/TPAMI.2012.226
  91. Tandon, A., Albeshri, A., Thayananthan, V., Alhalabi, W., & Fortunato, S. (2019). Fast consensus clustering in complex networks. Physical Review. E, 99(4-1), 042301. https://doi.org/10.1103/PhysRevE.99.042301
  92. Prokhorenkova, L. O., & Tikhonov, A. (2019). Community Detection through Likelihood Optimization: In Search of a Sound Model. In L. Liu & R. White (Eds.), The World Wide Web Conference on - WWW ’19 (pp. 1498–1508). ACM Press. https://doi.org/10.1145/3308558.3313429
  93. Brutz, M., & Meyer, F. G. (2015). A flexible multiscale approach to overlapping community detection. Social Network Analysis and Mining, 5(1), P09008. https://doi.org/10.1007/s13278-015-0259-z
  94. Zhai, X., Zhou, W., Fei, G., Lu, C., Wen, S., & Hu, G. (2019). Edge-based stochastic network model reveals structural complexity of edges. Future Generation Computer Systems, 100, 1073–1087. https://doi.org/10.1016/j.future.2019.05.047
  95. Karrer, B., & Newman. (2011). Stochastic blockmodels and community structure in networks. PHYSICAL REVIEW E, 83(1), -.
  96. Yang, J., & Leskovec, J. (2014). Structure and Overlaps of Ground-Truth Communities in Networks. ACM Transactions on Intelligent Systems and Technology, 5(2), 1–35. https://doi.org/10.1145/2594454
  97. Gopalan, P. K., & Blei, D. M. (2013). Efficient discovery of overlapping communities in massive networks. Proceedings of the National Academy of Sciences of the United States of America, 110(36), 14534–14539. https://doi.org/10.1073/pnas.1221839110
  98. Matias, C., & Miele, V. (2017). Statistical clustering of temporal networks through a dynamic stochastic block model. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 79(4), 1119–1141. https://doi.org/10.1111/rssb.12200
  99. Yang, T., Chi, Y., Zhu, S., Gong, Y., & Jin, R. (2011). Detecting communities and their evolutions in dynamic social networks—a Bayesian approach. Machine Learning, 82(2), 157–189. https://doi.org/10.1007/s10994-010-5214-7
  100. Ghasemian, A., Zhang, P., Clauset, A., Moore, C., & Peel, L. (2016). Detectability Thresholds and Optimal Algorithms for Community Structure in Dynamic Networks. Physical Review X, 6(3). https://doi.org/10.1103/PhysRevX.6.031005
  101. Xu, K. S., & Hero, A. O. (2014). Dynamic Stochastic Blockmodels for Time-Evolving Social Networks. IEEE Journal of Selected Topics in Signal Processing, 8(4), 552–562. https://doi.org/10.1109/JSTSP.2014.2310294
  102. Chen, Y., Wang, X. L., Yuan, B., & Tang, B. Z. (2014). Overlapping community detection in networks with positive and negative links. Journal of Statistical Mechanics: Theory and Experiment, 2014(3), P03021. http://stacks.iop.org/1742-5468/2014/i=3/a=P03021