Bibliography

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.

Algorithms

• Girvan-Newman: Girvan, Michelle, and Mark EJ Newman. Community structure in social and biological networks. 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. 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. 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. IEEE Transactions on Knowledge and Data Engineering 24.7 (2012): 1216-1230.

• Spinglass: Reichardt, Jörg, and Stefan Bornholdt. Statistical mechanics of community detection. Physical Review E 74.1 (2006): 016110.

• Eigenvector: Newman, Mark EJ. Finding community structure in networks using the eigenvectors of matrices. 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. 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. 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. arXiv preprint arXiv:1810.08473 (2018).

• Rb_pots:

  1. Reichardt, J., & Bornholdt, S. (2006). Statistical mechanics of community detection. 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. Physical Review Letters, 100(11), 118703. 10.1103/PhysRevLett.100.118703

• Rber_pots: Reichardt, J., & Bornholdt, S. (2006). Statistical mechanics of community detection. 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. 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. 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. 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. Physical Review E 70(6), 2004

• Infomap: Rosvall M, Bergstrom CT (2008) Maps of random walks on complex networks reveal community structure. 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. Nucleic acids research 30.7 (2002): 1575-1584.

• Walktrap: Pons, Pascal, and Matthieu Latapy. Computing communities in large networks using random walks. 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. 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.

• DER: M. Kozdoba and S. Mannor, Community Detection via Measure Space Embedding, NIPS 2015

• FRC_FGSN: Kundu, S., & Pal, S. K. (2015). Fuzzy-rough community in social networks. Pattern Recognition Letters, 67, 145-152.

• SBM_dl: Tiago P. Peixoto, Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models , 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 ,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. 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. ACM Transactions on Knowledge Discovery from Data (TKDD), 9(1), 6.

• Angel: Rossetti, G. (2019) Exorcising the Demon: Angel, Efficient Node-Centric Community Discovery. 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. 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. 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 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 New Journal of Physics 11.3 (2009): 033015.

• Lais2: Baumes, Jeffrey, Mark Goldberg, and Malik Magdon-Ismail. Efficient identification of overlapping communities. International Conference on Intelligence and Security Informatics. Springer, Berlin, Heidelberg, 2005.

• Congo: Gregory, Steve. A fast algorithm to find overlapping communities in networks. 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. 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. 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. Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference on. IEEE, 2011.

• Multicom: Hollocou, Alexandre, Thomas Bonald, and Marc Lelarge. Multiple Local Community Detection. 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. 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. 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. 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>

Evaluation measures

• Omega: Gabriel Murray, Giuseppe Carenini, and Raymond Ng. 2012. Using the omega index for evaluating abstractive algorithms detection. 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. 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.

  2. Rossetti, G. (2017). : RDyn: graph benchmark handling algorithms dynamics. Journal of Complex Networks. 5(6), 893-912.

• Adjusted_rand_index: Hubert, L., & Arabie, P. (1985). Comparing partitions. 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. Journal of Machine Learning Research, 11(Oct), 2837-2854.

• Variation_of_information: Meila, M. (2007). Comparing clusterings - an information based distance. 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.. 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. New Journal of Physics, 11(3), 033015.

• Newman_girvan_modularity: Newman, M.E.J. & Girvan, M. Finding and evaluating algorithms structure in networks. Physical Review E 69, 26113(2004).

• Erdos_renyi_modularity: Erdos, P., & Renyi, A. (1959). On random graphs I. 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. Physical review E, 77(3), 036109.

• Z_modularity: Miyauchi, Atsushi, and Yasushi Kawase. Z-score-based modularity for algorithms detection in networks. PloS one 11.1 (2016): e0147805.

• Surprise & Significance: Traag, V. A., Aldecoa, R., & Delvenne, J. C. (2015). Detecting communities using asymptotical surprise .. 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. Proceedings of the National Academy of Sciences, 101(9), 2658-2663.

• conductance: Shi, J., Malik, J.: Normalized cuts and image segmentation. Departmental Papers (CIS), 107 (2000)

• cut_ratio: Fortunato, S.: Community detection in graphs. 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. 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. 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. Proceedings of the National Academy of Sciences, 101(9), 2658-2663.

• normalized_cut: Shi, J., Malik, J.: Normalized cuts and image segmentation. Departmental Papers (CIS), 107 (2000)

• fraction_over_median_degree: Yang, J and Leskovec, J.: Defining and evaluating network communities based on ground-truth. 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. 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. 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. 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. 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. 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

• Cazabet, R., Boudebza, S., & Rossetti, G. (2020). Evaluating community detection algorithms for progressively evolving graphs. Journal of Complex Networks, 8(6), cnaa027.

• Rani, S., & Kumar, M. (2022). Ranking community detection algorithms for complex social networks using multilayer network design approach. International Journal of Web Information Systems, 18(5/6), 310-341.

• Tariq, R., Lavangnananda, K., Bouvry, P., & Mongkolnam, P. (2023). Partitioning Graph Clustering With User-Specified Density. IEEE Access, 11, 122273-122294.

• Pavel, A., Federico, A., Del Giudice, G., Serra, A., & Greco, D. (2021). Volta: adVanced mOLecular neTwork analysis. Bioinformatics, 37(23), 4587-4588.

• Krishna, V., Vasiliauskaite, V., & Antulov-Fantulin, N. (2022). Question routing via activity-weighted modularity-enhanced factorization. Social Network Analysis and Mining, 12(1), 155.

• Sahu, S., & Rani, T. S. (2022). A neighbour-similarity based community discovery algorithm. Expert Systems with Applications, 206, 117822.

• Aref, S., Chheda, H., & Mostajabdaveh, M. (2022). The Bayan algorithm: detecting communities in networks through exact and approximate optimization of modularity. arXiv preprint arXiv:2209.04562.

• Leventidis, A., Di Rocco, L., Gatterbauer, W., Miller, R. J., & Riedewald, M. (2023). DomainNet: Homograph Detection and Understanding in Data Lake Disambiguation. ACM Transactions on Database Systems, 48(3), 1-40.

• Rossetti, G. (2020). ANGEL: efficient, and effective, node-centric community discovery in static and dynamic networks. Applied Network Science, 5(1), 26.

• Citraro, S., & Rossetti, G. (2021). X-Mark: A benchmark for node-attributed community discovery algorithms. Social Network Analysis and Mining, 11(1), 99

• Kumar, M., Mishra, S., Singh, S. S., & Biswas, B. (2024). Community-enhanced Link Prediction in Dynamic Networks. ACM Transactions on the Web, 18(2), 1-32.

• Shrestha, A., Mielke, K., Nguyen, T. A., & Giabbanelli, P. J. (2022, December). Automatically explaining a model: Using deep neural networks to generate text from causal maps. In 2022 Winter Simulation Conference (WSC) (pp. 2629-2640). IEEE.

• Ye, Q., Xu, R., Li, D., Kang, Y., Deng, Y., Zhu, F., … & Hou, T. (2023). Integrating multi-modal deep learning on knowledge graph for the discovery of synergistic drug combinations against infectious diseases. Cell Reports Physical Science, 4(8).

• Peixoto, A. R., de Almeida, A., António, N., Batista, F., Ribeiro, R., & Cardoso, E. (2023). Unlocking the power of Twitter communities for startups. Applied Network Science, 8(1), 66.

• Hottenrott, H., & Lawson, C. (2022). What is behind multiple institutional affiliations in academia?. Science and public policy, 49(3), 382-402.

• Sarmiento, H., Bravo-Marquez, F., Graells-Garrido, E., & Poblete, B. (2022, May). Identifying and Characterizing New Expressions of Community Framing during Polarization. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 16, pp. 841-851).

• Mouronte-López, M. L., & Subirán, M. (2022). Modeling the interaction networks about the climate change on twitter: A characterization of its network structure. Complexity, 2022.

• Akbaritabar, A. (2021). A quantitative view of the structure of institutional scientific collaborations using the example of Berlin. Quantitative Science Studies, 2(2), 753-777.

• Das, S., Devarapalli, R. K., & Biswas, A. (2024). Leveraging cascading information for community detection in social networks. Information Sciences, 120696.

• Xiao, J., Wang, Y. J., & Xu, X. K. (2021). Fuzzy community detection based on elite symbiotic organisms search and node neighborhood information. IEEE Transactions on Fuzzy Systems, 30(7), 2500-2514.

• Al-Debagy, O., & Martinek, P. (2022). Dependencies-based microservices decomposition method. International Journal of Computers and Applications, 44(9), 814-821.

• Frąszczak, D. (2022). RPaSDT—rumor propagation and source detection Toolkit. SoftwareX, 17, 100988.

• Aref, S., & Mostajabdaveh, M. (2024). Analyzing modularity maximization in approximation, heuristic, and graph neural network algorithms for community detection. Journal of Computational Science, 78, 102283.

• Mohammadmosaferi, K. K., & Naderi, H. (2021). AFIF: Automatically Finding Important Features in community evolution prediction for dynamic social networks. Computer Communications, 176, 66-80

• Monterde, B., Rojano, E., Córdoba-Caballero, J., Seoane, P., Perkins, J. R., Medina, M. Á., & Ranea, J. A. (2023). Integrating differential expression, co-expression and gene network analysis for the identification of common genes associated with tumor angiogenesis deregulation. Journal of Biomedical Informatics, 144, 104421

• Böhle, T., Kuehn, C., & Thalhammer, M. (2022). On the reliable and efficient numerical integration of the Kuramoto model and related dynamical systems on graphs. International Journal of Computer Mathematics, 99(1), 31-57.

• Xiao, J., Zou, Y. C., & Xu, X. K. (2023). A Metaheuristic-Based Modularity Optimization Algorithm Driven by Edge Directionality for Directed Networks. IEEE Transactions on Network Science and Engineering.

• Vilela, J., Martiniano, H., Marques, A. R., Santos, J. X., Asif, M., Rasga, C., … & Vicente, A. M. (2023). Identification of Neurotransmission and Synaptic Biological Processes Disrupted in Autism Spectrum Disorder Using Interaction Networks and Community Detection Analysis. Biomedicines, 11(11), 2971.

• Zhu, W., Sun, Y., Fang, R., & Xu, B. (2023). A Low-Memory Community Detection Algorithm with Hybrid Sparse Structure and Structural Information for Large-scale Networks. IEEE Transactions on Parallel and Distributed Systems.

• Das, S., & Biswas, A. (2024). TSInc: Tie strength based incremental community detection using information cascades. International Journal of Information Technology, 1-11.

• Rossetti, G. (2020). Exorcising the demon: angel, efficient node-centric community discovery. In Complex Networks and Their Applications VIII: Volume 1 Proceedings of the Eighth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019 8 (pp. 152-163). Springer International Publishing.

• Frąszczak, D., & Frąszczak, E. (2024). NetCenLib: A comprehensive python library for network centrality analysis and evaluation. SoftwareX, 26, 101699

• Adams, C., Bozhidarova, M., Chen, J., Gao, A., Liu, Z., Priniski, J. H., … & Brantingham, P. J. (2022, December). Knowledge graphs of the QAnon Twitter network. In 2022 IEEE International Conference on Big Data (Big Data) (pp. 2903-2912). IEEE.

• Böhle, T., Thalhammer, M., & Kuehn, C. (2022). Community integration algorithms (CIAs) for dynamical systems on networks. Journal of Computational Physics, 469, 111524.

• Soh Tsin Howe, J. (2021). Simulating subject communities in case law citation networks. Frontiers in Physics, 9, 665563.

• Goodbrake, C., Beers, D., Thompson, T. B., Harrington, H. A., & Goriely, A. (2024). Brain chains as topological signatures for Alzheimer’s disease. Journal of Applied and Computational Topology, 1-42.

• Citraro, S., & Rossetti, G. (2020). Eva: Attribute-aware network segmentation. In Complex Networks and Their Applications VIII: Volume 1 Proceedings of the Eighth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019 8 (pp. 141-151). Springer International Publishing.

• Park, Y. J., & Li, D. (2024). Lower Ricci Curvature for Efficient Community Detection. arXiv preprint arXiv:2401.10124.

• Ghoroghchian, N., Dasarathy, G., & Draper, S. (2021, March). Graph community detection from coarse measurements: Recovery conditions for the coarsened weighted stochastic block model. In International Conference on Artificial Intelligence and Statistics (pp. 3619-3627). PMLR.

• Sun, P. G., Wu, X., Quan, Y., & Miao, Q. (2022). Rearranging’indivisible’blocks for community detection. IEEE Transactions on Knowledge and Data Engineering.

• Kumar, P., & Dohare, R. (2022). An interaction-based method for detecting overlapping community structure in real-world networks. International Journal of Data Science and Analytics, 14(1), 27-44.

• Vera, J., & Palma, W. (2021). The community structure of word co-occurrence networks: Experiments with languages from the Americas. Europhysics Letters, 134(5), 58002

• Rutkowski, E., Sargant, J., Houghten, S., & Brown, J. A. (2021, June). Evaluation of communities from exploratory evolutionary compression of weighted graphs. In 2021 IEEE Congress on Evolutionary Computation (CEC) (pp. 434-441). IEEE

• Jaguzović, M., Grbić, M., Ðukanović, M., & Matić, D. (2022, March). Identification of protein complexes by overlapping community detection algorithms: A comparative study. In 2022 21st International Symposium INFOTEH-JAHORINA (INFOTEH) (pp. 1-6). IEEE

• Gurukar, S., Venkatakrishnan, S. B., Ravindran, B., & Parthasarathy, S. (2023, November). PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks. In Proceedings of the International Conference on Advances in Social Networks Analysis and Mining (pp. 245-252).

• Prokop, P., Dráždilová, P., & Platoš, J. (2023, November). Hierarchical Overlapping Community Detection for Weighted Networks. In International Conference on Complex Networks and Their Applications (pp. 159-171). Cham: Springer Nature Switzerland.

• Hiel, S., Nicolaers, L., Vázquez, C. O., Mitrović, S., Baesens, B., & De Weerdt, J. (2022, November). Evaluation of Joint Modeling Techniques for Node Embedding and Community Detection on Graphs. In 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 403-410). IEEE.

• Das, S., & Biswas, A. (2021, June). Community detection in social networks using local topology and information exchange. In 2021 International Conference on Intelligent Technologies (CONIT) (pp. 1-7). IEEE

• Hassine, M. B., Jabbour, S., Kmimech, M., Raddaoui, B., & Graiet, M. (2023, August). A Non-overlapping Community Detection Approach Based on α-Structural Similarity. In International Conference on Big Data Analytics and Knowledge Discovery (pp. 197-211). Cham: Springer Nature Switzerland.

• Jeong, H., Kim, Y., Jung, Y. S., Kang, D. R., & Cho, Y. R. (2021). Entropy-based graph clustering of PPI networks for predicting overlapping functional modules of proteins. Entropy, 23(10), 1271.

• Ghoroghchian, N., Anguluri, R., Dasarathy, G., & Draper, S. C. (2022). Controllability of Coarsely Measured Networked Linear Dynamical Systems (Extended Version). arXiv preprint arXiv:2206.10569.

• Cruz, F., Monteiro, P. T., & Teixeira, A. S. (2023, March). Community Structure in Transcriptional Regulatory Networks of Yeast Species. In International Workshop on Complex Networks (pp. 38-49). Cham: Springer Nature Switzerland.

• Sreevalsan-Nair, J., & Jakher, A. (2022). CAP-DSDN: Node Co-association Prediction in Communities in Dynamic Sparse Directed Networks and a Case Study of Migration Flow. In KDIR (pp. 63-74).

• Szufel, P. (2024, April). Towards Graph Clustering for Distributed Computing Environments. In International Workshop on Algorithms and Models for the Web-Graph (pp. 146-158). Cham: Springer Nature Switzerland.

• Jaiswal, R., & Ramanna, S. (2021). Detecting overlapping communities using ensemble-based distributed neighbourhood threshold method in social networks. Intelligent Decision Technologies, 15(2), 251-267.

• Salter-Duke, M. (2024). Tangled webs: A practical investigation of graph tangles (Doctoral dissertation, Open Access Te Herenga Waka-Victoria University of Wellington).

• Bharadwaj, A. G. (2023). Driving Reasoning Systems for Product Design and Flexible Robotic Manipulation Using 3D Design-Based Knowledge Graphs. North Carolina State University.

• Lei, G., Sheng, Y., Shaozi, L., & Qingshou, W. (2022). Hierarchical community‐discovery algorithm combining core nodes and three‐order structure model. Concurrency and Computation: Practice and Experience, 34(4), e6669.

• Stav, G. B. (2023). Network Analysis of the 3D Genome (Master’s thesis).

• Gibbs, C. P. (2023). Causality and clustering in complex settings (Doctoral dissertation, Colorado State University).

• Das, S., & Biswas, A. (2022, December). Towards Direct Comparison of Community Structures in Social Networks. In 2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS) (pp. 1-6). IEEE.

• Krathaus, A. (2023). Impacts of Social and Transportation Networks on Social Activity-Travel Participation: An Exploratory Analysis Using Location-Based Social Network Data (Master’s thesis, State University of New York at Buffalo)

• Svete, A., & Hostnik, J. (2020). It is not just about the melody: how Europe votes for its favorite songs. arXiv preprint arXiv:2002.06609.

• Rutkowski, E. (2022). Weighted Graph Compression using Genetic Algorithms.

• Zhou, X., Pan, Y., & Qin, J. (2022, April). Intelligent Control of Shield Tunneling from the Perspective of Complex Network. In International Conference on Green Building, Civil Engineering and Smart City (pp. 1226-1233). Singapore: Springer Nature Singapore.

• Rózemberczki, B. (2021). Graph mining on static, multiplex and attributed networks.

• Chatzi, I. (2024). Σύγκριση μεθόδων εντοπισμού κοινοτήτων για ανίχνευση botnets.

• Oostenbach, R. Fairness-Aware Analysis of Community Detection.

• AL-DYANI, W. Z. A. AN ENHANCED BINARY BAT AND MARKOV CLUSTERING ALGORITHMS TO IMPROVE EVENT DETECTION FOR HETEROGENEOUS NEWS TEXT DOCUMENTS.

• Campos, G. A., Ribeiro, J. M., Vieira, V. F., & Xavier, C. R. (2023, August). Estudo do impacto da seleção de sementes baseada em centralidade e em informações de comunidades sobrepostas. In Anais do XII Brazilian Workshop on Social Network Analysis and Mining (pp. 163-174). SBC.

• Akbaritabar, A. (2021). Quantitative View of the Structure of Institutional Scientific Collaborations Using the Examples of Halle, Jena and Leipzig. arXiv preprint arXiv:2101.05784.

• Barros, J. S. A. Facultad de Ingeniería Carrera de Ingeniería de Sistemas (Doctoral dissertation, Universidad de Cuenca).

• HUBERT, M. CRAWLING AND ANALYSING CODE REVIEW NETWORKS ON INDUSTRY AND OPEN SOURCE DATA.

• Svete, A., Hostnik, J., & Šubelj, L. (2020). Ne gre le za melodijo: kako Evropa glasuje za svoje najljubše skladbe. Uporabna Informatika, 28.