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Bibliography
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``CDlib`` was developed for research purposes. Here you can find a(n almost) complete list of papers that contributed to the algorithms and methods it exposes.

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Algorithms
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- Girvan-Newman: Girvan, Michelle, and Mark EJ Newman. `Community structure in social and biological networks. <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC122977/>`_ Proceedings of the national academy of sciences 99.12 (2002): 7821-7826.
- EM: Newman, Mark EJ, and Elizabeth A. Leicht. `Mixture community and exploratory analysis in networks.  <https://www.pnas.org/content/104/23/9564/>`_ Proceedings of the National Academy of Sciences 104.23 (2007): 9564-9569.
- SCAN: Xu, X., Yuruk, N., Feng, Z., & Schweiger, T. A. (2007, August). `Scan: a structural clustering algorithm for networks. <http://www1.se.cuhk.edu.hk/~hcheng/seg5010/slides/p824-xu.pdf/>`_ In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 824-833)
- GDMP2: Chen, Jie, and Yousef Saad. `Dense subgraph extraction with application to community detection. <https://ieeexplore.ieee.org/document/5677532/>`_ IEEE Transactions on Knowledge and Data Engineering 24.7 (2012): 1216-1230.
- Spinglass: Reichardt, Jörg, and Stefan Bornholdt. `Statistical mechanics of community detection. <https://journals.aps.org/pre/abstract/10.1103/PhysRevE.74.016110/>`_ Physical Review E 74.1 (2006): 016110.
- Eigenvector: Newman, Mark EJ. `Finding community structure in networks using the eigenvectors of matrices. <https://journals.aps.org/pre/pdf/10.1103/PhysRevE.74.036104/>`_ Physical review E 74.3 (2006): 036104.
- AGDL: Zhang, W., Wang, X., Zhao, D., & Tang, X. (2012, October). `Graph degree linkage: Agglomerative clustering on a directed graph. <https://link.springer.com/chapter/10.1007/978-3-642-33718-5_31/>`_ In European Conference on Computer Vision (pp. 428-441). Springer, Berlin, Heidelberg.
- Louvain: Blondel, Vincent D., et al. `Fast unfolding of communities in large networks. <https://iopscience.iop.org/article/10.1088/1742-5468/2008/10/P10008/meta/>`_ Journal of statistical mechanics: theory and experiment 2008.10 (2008): P10008.
- Leiden: Traag, Vincent, Ludo Waltman, and Nees Jan van Eck. `From Louvain to Leiden: guaranteeing well-connected communities. <https://arxiv.org/abs/1810.08473/>`_ arXiv preprint arXiv:1810.08473 (2018).
- Rb_pots:
    1. Reichardt, J., & Bornholdt, S. (2006).  `Statistical mechanics of community detection. <https://journals.aps.org/pre/abstract/10.1103/PhysRevE.74.016110/>`_  Physical Review E, 74(1), 016110. 10.1103/PhysRevE.74.016110
    2. Leicht, E. A., & Newman, M. E. J. (2008).  `Community Structure in Directed Networks. <https://www.ncbi.nlm.nih.gov/pubmed/18517839/>`_  Physical Review Letters, 100(11), 118703. 10.1103/PhysRevLett.100.118703
- Rber_pots: Reichardt, J., & Bornholdt, S. (2006).  `Statistical mechanics of community detection. <https://journals.aps.org/pre/abstract/10.1103/PhysRevE.74.016110/>`_  Physical Review E, 74(1), 016110. 10.1103/PhysRevE.74.016110
- CPM: Traag, V. A., Van Dooren, P., & Nesterov, Y. (2011).  `Narrow scope for resolution-limit-free community detection. <https://journals.aps.org/pre/abstract/10.1103/PhysRevE.84.016114/>`_ Physical Review E, 84(1), 016114. 10.1103/PhysRevE.84.016114
- Significance_communities: Traag, V. A., Krings, G., & Van Dooren, P. (2013). `Significant scales in community structure. <https://www.nature.com/articles/srep02930/>`_ Scientific Reports, 3, 2930. `10.1038/srep02930 <http://doi.org/10.1038/srep02930>`
- Surprise_communities: Traag, V. A., Aldecoa, R., & Delvenne, J.-C. (2015).  `Detecting communities using asymptotical surprise. <https://journals.aps.org/pre/abstract/10.1103/PhysRevE.92.022816/>`_ Physical Review E, 92(2), 022816. 10.1103/PhysRevE.92.022816
- Greedy_modularity: Clauset, A., Newman, M. E., & Moore, C. `Finding community structure in very large networks. <http://ece-research.unm.edu/ifis/papers/community-moore.pdf/>`_ Physical Review E 70(6), 2004
- Infomap: Rosvall M, Bergstrom CT (2008) `Maps of random walks on complex networks reveal community structure. <https://www.pnas.org/content/105/4/1118/>`_ Proc Natl Acad SciUSA 105(4):1118–1123
- Markov_clustering: Enright, Anton J., Stijn Van Dongen, and Christos A. Ouzounis. `An efficient algorithm for large-scale detection of protein families. <https://www.ncbi.nlm.nih.gov/pubmed/11917018/>`_ Nucleic acids research 30.7 (2002): 1575-1584.
- Walktrap: Pons, Pascal, and Matthieu Latapy. `Computing communities in large networks using random walks. <http://jgaa.info/accepted/2006/PonsLatapy2006.10.2.pdf/>`_ J. Graph Algorithms Appl. 10.2 (2006): 191-218.
- Label_propagation: Raghavan, U. N., Albert, R., & Kumara, S. (2007). `Near linear time algorithm to detect community structures in large-scale networks. <http://www.leonidzhukov.net/hse/2017/networks/papers/raghavan2007.pdf/>`_ Physical review E, 76(3), 036106.
- Async_fluid: Ferran Parés, Dario Garcia-Gasulla, Armand Vilalta, Jonatan Moreno, Eduard Ayguadé, Jesús Labarta, Ulises Cortés, Toyotaro Suzumura T. `Fluid Communities: A Competitive and Highly Scalable Community Detection Algorithm. <https://link.springer.com/chapter/10.1007/978-3-319-72150-7_19/>`_
- DER: M. Kozdoba and S. Mannor, `Community Detection via Measure Space Embedding <https://papers.nips.cc/paper/5808-community-detection-via-measure-space-embedding/>`_, NIPS 2015
- FRC_FGSN: Kundu, S., & Pal, S. K. (2015). `Fuzzy-rough community in social networks. <https://www.sciencedirect.com/science/article/pii/S0167865515000537/>`_ Pattern Recognition Letters, 67, 145-152.
- SBM_dl:  Tiago P. Peixoto, `Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models <https://journals.aps.org/pre/abstract/10.1103/PhysRevE.89.012804/>`_ , Phys. Rev. E 89, 012804 (2014), DOI: 10.1103/PhysRevE.89.012804 [sci-hub, @tor], arXiv: 1310.4378.
- SBM_dl_nested: Tiago P. Peixoto, `Hierarchical block structures and high-resolution model selection in large networks <https://journals.aps.org/prx/abstract/10.1103/PhysRevX.4.011047/>`_ ,Physical Review X 4.1 (2014): 011047
- aslpa: Xie J, Szymanski B K, Liu X. Slpa: Uncovering Overlapping Communities in Social Networks via a Speaker-Listener Interaction Dynamic Process[C]. IEEE 11th International Conference on Data Mining Workshops (ICDMW). Ancouver, BC: IEEE, 2011: 344–349.
- belief: Zhang, Pan, and Cristopher Moore. “Scalable detection of statistically significant communities and hierarchies, using message passing for modularity.” Proceedings of the National Academy of Sciences 111.51 (2014): 18144-18149.
- chinesewhispers: Ustalov, D., Panchenko, A., Biemann, C., Ponzetto, S.P.: `Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction.` Computational Linguistics 45(3), 423–479 (2019)
- edmot: Li, Pei-Zhen, et al. “EdMot: An Edge Enhancement Approach for Motif-aware Community Detection.” Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019.
- em: Newman, Mark EJ, and Elizabeth A. Leicht. Mixture community and exploratory analysis in networks. Proceedings of the National Academy of Sciences 104.23 (2007): 9564-9569.
- ga: Pizzuti, C. (2008). Ga-net: A genetic algorithm for community detection in social networks. In Inter conf on parallel problem solving from nature, pages 1081–1090.Springer.
- Demon:
    1. Coscia, M., Rossetti, G., Giannotti, F., & Pedreschi, D. (2012, August). `Demon: a local-first discovery method for overlapping communities. <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.721.1788&rep=rep1&type=pdf/>`_ In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 615-623). ACM.
    2. Coscia, M., Rossetti, G., Giannotti, F., & Pedreschi, D. (2014). `Uncovering hierarchical and overlapping communities with a local-first approach. <https://dl.acm.org/citation.cfm?id=2629511/>`_ ACM Transactions on Knowledge Discovery from Data (TKDD), 9(1), 6.
- Angel: Rossetti, G. (2019) `Exorcising the Demon: Angel, Efficient Node-Centric Community Discovery. <https://link.springer.com/chapter/10.1007/978-3-030-36687-2_13>`_ International Conference on Complex Networks and Their Applications. Springer, Cham.
- Node_perception: Sucheta Soundarajan and John E. Hopcroft. 2015. `Use of Local Group Information to Identify Communities in Networks. <https://dl.acm.org/citation.cfm?id=2737800.2700404/>`_ ACM Trans. Knowl. Discov. Data 9, 3, Article 21 (April 2015), 27 pages. DOI=http://dx.doi.org/10.1145/2700404
- Overlapping_seed_set_expansion: Whang, J. J., Gleich, D. F., & Dhillon, I. S. (2013, October). `Overlapping community detection using seed set expansion. <http://www.cs.utexas.edu/~inderjit/public_papers/overlapping_commumity_cikm13.pdf/>`_ In Proceedings of the 22nd ACM international conference on Conference on information & knowledge management (pp. 2099-2108). ACM.
- Kclique: Gergely Palla, Imre Derényi, Illés Farkas1, and Tamás Vicsek, `Uncovering the overlapping community structure of complex networks in nature and society <https://www.nature.com/articles/nature03607/>`_ Nature 435, 814-818, 2005, doi:10.1038/nature03607
- LFM: Lancichinetti, Andrea, Santo Fortunato, and János Kertész. `Detecting the overlapping and hierarchical community structure in complex networks <https://arxiv.org/abs/0802.1218/>`_ New Journal of Physics 11.3 (2009): 033015.
- Lais2: Baumes, Jeffrey, Mark Goldberg, and Malik Magdon-Ismail. `Efficient identification of overlapping communities. <https://link.springer.com/chapter/10.1007/11427995_3/>`_ International Conference on Intelligence and Security Informatics. Springer, Berlin, Heidelberg, 2005.
- Congo: Gregory, Steve. `A fast algorithm to find overlapping communities in networks. <https://link.springer.com/chapter/10.1007/978-3-540-87479-9_45/>`_ Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Berlin, Heidelberg, 2008.
- Conga: Gregory, Steve. `An algorithm to find overlapping community structure in networks. <https://link.springer.com/chapter/10.1007/978-3-540-74976-9_12/>`_ European Conference on Principles of Data Mining and Knowledge Discovery. Springer, Berlin, Heidelberg, 2007.
- Lemon: Yixuan Li, Kun He, David Bindel, John Hopcroft `Uncovering the small community structure in large networks: A local spectral approach. <https://dl.acm.org/citation.cfm?id=2736277.2741676/>`_ Proceedings of the 24th international conference on world wide web. International World Wide Web Conferences Steering Committee, 2015.
- SLPA:  Xie Jierui, Boleslaw K. Szymanski, and Xiaoming Liu. `Slpa: Uncovering overlapping communities in social networks via a speaker-listener interaction dynamic process. <https://ieeexplore.ieee.org/document/6137400/>`_ Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference on. IEEE, 2011.
- Multicom: Hollocou, Alexandre, Thomas Bonald, and Marc Lelarge. `Multiple Local Community Detection. <https://hal.archives-ouvertes.fr/hal-01625444/document/>`_ ACM SIGMETRICS Performance Evaluation Review 45.2 (2018): 76-83.
- Big_clam: Yang, J., & Leskovec, J. (2013, February). `Overlapping community detection at scale: a nonnegative matrix factorization approach. <https://dl.acm.org/citation.cfm?id=2433471/>`_ In Proceedings of the sixth ACM international conference on Web search and data mining (pp. 587-596). ACM.
- damnf: Ye, Fanghua, Chuan Chen, and Zibin Zheng. “Deep autoencoder-like nonnegative matrix factorization for community detection.” Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018.
- egonet splitter: Epasto, Alessandro, Silvio Lattanzi, and Renato Paes Leme. “Ego-splitting framework: From non-overlapping to overlapping clusters.” Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017.
- nmnf: Wang, Xiao, et al. “Community preserving network embedding.” Thirty-first AAAI conference on artificial intelligence. 2017.
- nnsed: Sun, Bing-Jie, et al. “A non-negative symmetric encoder-decoder approach for community detection.” Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 2017.
- percomvc: Kasoro, Nathanaël, et al. “PercoMCV: A hybrid approach of community detection in social networks.” Procedia Computer Science 151 (2019): 45-52.
- wCommunity: Chen, D., Shang, M., Lv, Z., & Fu, Y. (2010). Detecting overlapping communities of weighted networks via a local algorithm. Physica A: Statistical Mechanics and its Applications, 389(19), 4177-4187.
- blmpa: Taguchi, Hibiki, Tsuyoshi Murata, and Xin Liu. BiMLPA: Community Detection in Bipartite Networks by Multi-Label Propagation. International Conference on Network Science. Springer, Cham, 2020.
- CPM bipartite: Barber, M. J. (2007). Modularity and community detection in bipartite networks. Physical Review E, 76(6), 066102. 10.1103/PhysRevE.76.066102
- syblinarity: Vasiliauskaite, V., Evans, T.S. Making communities show respect for order. Appl Netw Sci 5, 15 (2020). https://doi.org/10.1007/s41109-020-00255-5
- hierarchical_link_community: Ahn, Yong-Yeol, James P. Bagrow, and Sune Lehmann. `Link communities reveal multiscale complexity in networks. <https://www.nature.com/articles/nature09182/>`_ nature 466.7307 (2010): 761.
- Eva: Citraro, S., & Rossetti, G. (2019, December). Eva: Attribute-Aware Network Segmentation. In International Conference on Complex Networks and Their Applications (pp. 141-151). Springer, Cham.
- iLouvain: Combe D., Largeron C., Géry M., Egyed-Zsigmond E. “I-Louvain: An Attributed Graph Clustering Method”. <https://link.springer.com/chapter/10.1007/978-3-319-24465-5_16> In: Fromont E., De Bie T., van Leeuwen M. (eds) Advances in Intelligent Data Analysis XIV. IDA (2015). Lecture Notes in Computer Science, vol 9385. Springer, Cham
- tiles: Rossetti, G., Pappalardo, L., Pedreschi, D., & Giannotti, F. (2017). `Tiles: an online algorithm for community discovery in dynamic social networks. <https://link.springer.com/article/10.1007/s10994-016-5582-8/>`_ Machine Learning, 106(8), 1213-1241.
- lpam: Ponomarenko, A., Pitsoulis, L., and Shamshetdinov, M. (2021). `Overlapping community detection in networks based on link partitioning and partitioning around medoids` <https://arxiv.org/abs/1907.08731>

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Evaluation measures
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- Omega: Gabriel Murray, Giuseppe Carenini, and Raymond Ng. 2012. `Using the omega index for evaluating abstractive algorithms detection. <https://pdfs.semanticscholar.org/59d6/5d5aa09d789408fd9fd3c009a1b070ff5859.pdf/>`_ In Proceedings of Workshop on Evaluation Metrics and System Comparison for Automatic Summarization. Association for Computational Linguistics, Stroudsburg, PA, USA, 10-18.
- F1: Rossetti, G., Pappalardo, L., & Rinzivillo, S. (2016). `A novel approach to evaluate algorithms detection internal on ground truth. <https://www.researchgate.net/publication/287204505_A_novel_approach_to_evaluate_community_detection_algorithms_on_ground_truth/>`_  In Complex Networks VII (pp. 133-144). Springer, Cham.
- NF1:
    1. Rossetti, G., Pappalardo, L., & Rinzivillo, S. (2016). `A novel approach to evaluate algorithms detection internal on ground truth. <https://www.researchgate.net/publication/287204505_A_novel_approach_to_evaluate_community_detection_algorithms_on_ground_truth/>`_
    2. Rossetti, G. (2017). : `RDyn: graph benchmark handling algorithms dynamics. Journal of Complex Networks. <https://academic.oup.com/comnet/article-abstract/5/6/893/3925036?redirectedFrom=PDF/>`_ 5(6), 893-912.
- Adjusted_rand_index: Hubert, L., & Arabie, P. (1985). `Comparing partitions. <https://link.springer.com/article/10.1007/BF01908075/>`_ Journal of classification, 2(1), 193-218.
- Adjusted_mutual_information: Vinh, N. X., Epps, J., & Bailey, J. (2010). `Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance. <http://jmlr.csail.mit.edu/papers/volume11/vinh10a/vinh10a.pdf/>`_ Journal of Machine Learning Research, 11(Oct), 2837-2854.
- Variation_of_information: Meila, M. (2007). `Comparing clusterings - an information based distance. <https://www.sciencedirect.com/science/article/pii/S0047259X06002016/>`_ Journal of Multivariate Analysis, 98, 873-895. doi:10.1016/j.jmva.2006.11.013
- Overlapping_normalized_mutual_information_MGH: McDaid, A. F., Greene, D., & Hurley, N. (2011). `Normalized mutual information to evaluate overlapping community finding algorithms. <https://arxiv.org/abs/1110.2515/>`_. arXiv preprint arXiv:1110.2515. Chicago
- Overlapping_normalized_mutual_information_LFK: Lancichinetti, A., Fortunato, S., & Kertesz, J. (2009). `Detecting the overlapping and hierarchical community structure in complex networks. <https://iopscience.iop.org/article/10.1088/1367-2630/11/3/033015/meta/>`_ New Journal of Physics, 11(3), 033015.
- Newman_girvan_modularity: Newman, M.E.J. & Girvan, M. `Finding and evaluating algorithms structure in networks. <https://www.ncbi.nlm.nih.gov/pubmed/14995526/>`_ Physical Review E 69, 26113(2004).
- Erdos_renyi_modularity: Erdos, P., & Renyi, A. (1959). `On random graphs I. <https://gnunet.org/sites/default/files/Erd%C5%91s%20%26%20R%C3%A9nyi%20-%20On%20Random%20Graphs.pdf/>`_ Publ. Math. Debrecen, 6, 290-297.
- Modularity_density: Li, Z., Zhang, S., Wang, R. S., Zhang, X. S., & Chen, L. (2008). `Quantitative function for algorithms detection. <https://www.sciencedirect.com/science/article/pii/S0020025516305059/>`_ Physical review E, 77(3), 036109.
- Z_modularity: Miyauchi, Atsushi, and Yasushi Kawase. `Z-score-based modularity for algorithms detection in networks. <https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0147805/>`_ PloS one 11.1 (2016): e0147805.
- Surprise & Significance: Traag, V. A., Aldecoa, R., & Delvenne, J. C. (2015). `Detecting communities using asymptotical surprise .. <https://link.aps.org/doi/10.1103/PhysRevE.92.022816/>`_ Physical Review E, 92(2), 022816.
- average_internal_degree: Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., & Parisi, D. (2004). `Defining and identifying communities in networks. <https://www.pnas.org/content/101/9/2658.short/>`_ Proceedings of the National Academy of Sciences, 101(9), 2658-2663.
- conductance: Shi, J., Malik, J.: `Normalized cuts and image segmentation. <https://repository.upenn.edu/cgi/viewcontent.cgi?article=1101&context=cis_papers/>`_ Departmental Papers (CIS), 107 (2000)
- cut_ratio: Fortunato, S.: `Community detection in graphs. <https://www.sciencedirect.com/science/article/pii/S0370157309002841/>`_ Physics reports 486(3-5), 75–174 (2010)
- edges_inside: Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., & Parisi, D. (2004). `Defining and identifying communities in networks. <https://www.pnas.org/content/101/9/2658.short/>`_ Proceedings of the National Academy of Sciences, 101(9), 2658-2663.
- expansion: Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., & Parisi, D. (2004). `Defining and identifying communities in networks. <https://www.pnas.org/content/101/9/2658.short/>`_ Proceedings of the National Academy of Sciences, 101(9), 2658-2663.
- internal_edge_density: Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., & Parisi, D. (2004). `Defining and identifying communities in networks. <https://www.pnas.org/content/101/9/2658.short/>`_ Proceedings of the National Academy of Sciences, 101(9), 2658-2663.
- normalized_cut: Shi, J., Malik, J.: `Normalized cuts and image segmentation. <https://repository.upenn.edu/cgi/viewcontent.cgi?article=1101&context=cis_papers/>`_ Departmental Papers (CIS), 107 (2000)
- fraction_over_median_degree: Yang, J and Leskovec, J.: `Defining and evaluating network communities based on ground-truth. <https://link.springer.com/article/10.1007/s10115-013-0693-z/>`_ Knowledge and Information Systems 42(1), 181–213 (2015)
- max_odf: Flake, G.W., Lawrence, S., Giles, C.L., et al.: `Efficient identification of web communities. <https://courses.cs.washington.edu/courses/cse522/05au/communities-flake.pdf/>`_ In: KDD, vol. 2000, pp. 150–160 (2000)
- avg_odf: Flake, G.W., Lawrence, S., Giles, C.L., et al.: `Efficient identification of web communities. <https://courses.cs.washington.edu/courses/cse522/05au/communities-flake.pdf/>`_ In: KDD, vol. 2000, pp. 150–160 (2000)
- flake_odf: Flake, G.W., Lawrence, S., Giles, C.L., et al.: `Efficient identification of web communities. <https://courses.cs.washington.edu/courses/cse522/05au/communities-flake.pdf/>`_ In: KDD, vol. 2000, pp. 150–160 (2000)
- triangle_participation_ratio: Yang, J and Leskovec, J.: `Defining and evaluating network communities based on ground-truth. <https://link.springer.com/article/10.1007/s10115-013-0693-z/>`_ Knowledge and Information Systems 42(1), 181–213 (2015)
- link_modularity: Nicosia, V., Mangioni, G., Carchiolo, V., Malgeri, M.: `Extending the definition of modularity to directed graphs with overlapping communities. <https://iopscience.iop.org/article/10.1088/1742-5468/2009/03/P03024/meta/>`_ Journal of Statistical Mechanics: Theory and Experiment 2009(03), 03024 (2009)

----------------------
Researches using CDlib
----------------------

So far CDlib has been referenced in the following research works:

- Rezaei, M., Faramarzpour, M., Shobeiri, P., Seyedmirzaei, H., Sarasyabi, M. S., & Dabiri, S. (2023). A systematic review, meta-analysis, and network analysis of diagnostic microRNAs in glaucoma. European Journal of Medical Research, 28(1), 137.
- Bharadwaj, A. G., & Starly, B. (2022). Knowledge graph construction for product designs from large CAD model repositories. Advanced Engineering Informatics, 53, 101680.
- Sieranoja, S., & Fränti, P. (2022). Adapting k-means for graph clustering. Knowledge and Information Systems, 64(1), 115-142.
- Roghani, H., & Bouyer, A. (2022). A fast local balanced label diffusion algorithm for community detection in social networks. IEEE Transactions on Knowledge and Data Engineering.
- Peng, J., Zhou, Y., & Wang, K. (2021). Multiplex gene and phenotype network to characterize shared genetic pathways of epilepsy and autism. Scientific reports, 11(1), 952.
- Citraro, S., & Rossetti, G. (2020). Identifying and exploiting homogeneous communities in labeled networks. Applied Network Science, 5(1), 55.
- Gomes Ferreira, C. H., Murai, F., Silva, A. P., Trevisan, M., Vassio, L., Drago, I., ... & Almeida, J. M. (2022). On network backbone extraction for modeling online collective behavior. Plos one, 17(9), e0274218.
- Yao, X., Wang, D., Yu, T., Luan, C., & Fu, J. (2023). A machining feature recognition approach based on hierarchical neural network for multi-feature point cloud models. Journal of Intelligent Manufacturing, 34(6), 2599-2610.
- Hottenrott, H., Rose, M. E., & Lawson, C. (2021). The rise of multiple institutional affiliations in academia. Journal of the Association for Information Science and Technology, 72(8), 1039-1058.
- Vilela, J., Asif, M., Marques, A. R., Santos, J. X., Rasga, C., Vicente, A., & Martiniano, H. (2023). Biomedical knowledge graph embeddings for personalized medicine: Predicting disease‐gene associations. Expert Systems, 40(5), e13181
- Frąszczak, D. (2023). Detecting rumor outbreaks in online social networks. Social Network Analysis and Mining, 13(1), 91.
- Pister, A., Buono, P., Fekete, J. D., Plaisant, C., & Valdivia, P. (2020). Integrating prior knowledge in mixed-initiative social network clustering. IEEE Transactions on Visualization and Computer Graphics, 27(2), 1775-1785.
- Mohammadmosaferi, K. K., & Naderi, H. (2020). Evolution of communities in dynamic social networks: An efficient map-based approach. Expert Systems with Applications, 147, 113221
- Amira, A., Derhab, A., Hadjar, S., Merazka, M., Alam, M. G. R., & Hassan, M. M. (2023). Detection and Analysis of Fake News Users’ Communities in Social Media. IEEE Transactions on Computational Social Systems.
- Yassin, A., Haidar, A., Cherifi, H., Seba, H., & Togni, O. (2023). An evaluation tool for backbone extraction techniques in weighted complex networks. Scientific Reports, 13(1), 17000.
- Sobolevsky, S., & Belyi, A. (2022). Graph neural network inspired algorithm for unsupervised network community detection. Applied Network Science, 7(1), 63.
- Oestreich, Marie, et al. "hCoCena: horizontal integration and analysis of transcriptomics datasets." Bioinformatics 38.20 (2022): 4727-4734.
- Rustamaji, H. C., Kusuma, W. A., Nurdiati, S., & Batubara, I. (2024). Community detection with greedy modularity disassembly strategy. Scientific Reports, 14(1), 4694.
- Aref, S., Mostajabdaveh, M., & Chheda, H. (2023, June). Heuristic modularity maximization algorithms for community detection rarely return an optimal partition or anything similar. In International Conference on Computational Science (pp. 612-626). Cham: Springer Nature Switzerland.
- Galan-Vasquez, E., & Perez-Rueda, E. (2021). A landscape for drug-target interactions based on network analysis. Plos one, 16(3), e0247018.
- Groza, V., Udrescu, M., Bozdog, A., & Udrescu, L. (2021). Drug repurposing using modularity clustering in drug-drug similarity networks based on drug–gene interactions. Pharmaceutics, 13(12), 2117.
- Zafarmand, M., Talebirad, Y., Austin, E., Largeron, C., & Zaïane, O. R. (2023). Fast local community discovery relying on the strength of links. Social Network Analysis and Mining, 13(1), 112
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