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Short Overview Paper


Please use the following reference to cite KONECT.

[1] konect network dataset -- KONECT, April 2017. [ http ]
[2] Jérôme Kunegis. KONECT -- The Koblenz Network Collection. In Proc. Int. Conf. on World Wide Web Companion, pages 1343--1350, 2013. [ .pdf ]


KONECT Handbook of Network Analysis


Most information about KONECT can be found in the KONECT handbook. The handbook includes mathematical definitions of everything shown on the website, and more. It also contains a detailed description of the network taxonomy used in KONECT. All help texts on this website are in fact taken from this handbook.

Download: konect-handbook.pdf (1.14 MiB) [Last updated: Apr 27 2017]


Publications using KONECT


The following publications use KONECT datasets and the KONECT toolbox in their evaluations. If you want a publication added to this list, please report it to kunegis@uni-koblenz.de. Newer publications are listed first. The list also include papers written by the authors of KONECT using the KONECT datasets from before the name KONECT was adopted.

[1] Gualtiero B Colombo, Pete Burnap, Andrei Hodorog, and Jonathan Scourfield. Analysing the connectivity and communication of suicidal users on twitter. Computer communications, 73:291--300, 2016. [ http ]
[2] Gianlorenzo D'Angelo, Lorenzo Severini, and Yllka Velaj. On the maximum betweenness improvement problem. Electronic Notes in Theoretical Computer Science, 322:153--168, 2016. [ http ]
[3] Yuxiao Dong, Reid A Johnson, Jian Xu, and Nitesh V Chawla. Structural diversity and homophily: A study across more than one hundred large-scale networks. arXiv preprint arXiv:1602.07048, 2016. [ .pdf ]
[4] Alexandre Fender, Nahid Emad, Joe Eaton, and Serge Petiton. Accelerated hybrid approach for spectral problems arising in graph analytics. Procedia Computer Science, 80:2338--2347, 2016. [ http ]
[5] Emmanuel John and Ilya Safro. Single-and multi-level network sparsification by algebraic distance. arXiv preprint arXiv:1601.05527, 2016. [ .pdf ]
[6] Nathan Lapierre. A Distributed Method for Fast Force-Directed Layout of Large Scale-free Network Graphs. PhD thesis, 2016. [ .pdf ]
[7] Naoto Ozaki, Hiroshi Tezuka, and Mary Inaba. A simple acceleration method for the louvain algorithm. International Journal of Computer and Electrical Engineering, 8(3):207, 2016. [ .pdf ]
[8] Sebastian Schelter and Jérôme Kunegis. On the ubiquity of web tracking: Insights from a billion-page web crawl. arXiv preprint arXiv:1607.07403, 2016. [ .pdf ]
[9] Konstantin Avrachenkov, Bruno Ribeiro, and Jithin K Sreedharan. Bayesian inference of online social network statistics via lightweight random walk crawls. arXiv preprint arXiv:1510.05407, 2015. [ http ]
[10] Christian Bauckhage, Kristian Kersting, and Fabian Hadiji. Maximum entropy models of shortest path and outbreak distributions in networks. arXiv preprint arXiv:1501.04232, 2015. [ http ]
[11] Christian Bauckhage, Kristian Kersting, and Fabian Hadiji. Parameterizing the distance distribution of undirected networks. In Proceedings of UAI, 2015. [ .pdf ]
[12] Elisabetta Bergamini and Henning Meyerhenke. Fully-dynamic approximation of betweenness centrality. In Algorithms-ESA 2015, pages 155--166. Springer, 2015. [ http ]
[13] Fei Gao, Katarzyna Musial, Colin Cooper, and Sophia Tsoka. Link prediction methods and their accuracy for different social networks and network metrics. Scientific Programming, 2015:1, 2015. [ http ]
[14] Jannis Koch, Christian L Staudt, Maximilian Vogel, and Henning Meyerhenke. Complex network analysis on distributed systems—an empirical comparison. In 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 1169--1176. IEEE, 2015. [ .pdf ]
[15] Yanhong Wu, Wenbin Wu, Sixiao Yang, Youliang Yan, and Huamin Qu. Interactive visual summary of major communities in a large network. In 2015 IEEE Pacific Visualization Symposium (PacificVis), pages 47--54. IEEE, 2015. [ .pdf ]
[16] Yanbo Zhou, An Zeng, and Wei-Hong Wang. Temporal effects in trend prediction: identifying the most popular nodes in the future. PloS one, 10(3):e0120735, 2015. [ http ]
[17] Jacopo Binchi, Emanuela Merelli, Matteo Rucco, Giovanni Petri, and Francesco Vaccarino. jHoles: A tool for understanding biological complex networks via clique weight rank persistent homology. Electronic Notes in Theoretical Computer Science, 306:5--18, 2014.
[18] Dimitris S. Papailiopoulos, Anastasios T. Kyrillidis, and Christos Boutsidis. Provable deterministic leverage score sampling. CoRR, abs/1404.1530, 2014.
[19] Julia Preusse, Jérôme Kunegis, Matthias Thimm, and Sergej Sizov. DecLiNe -- models for decay of links in networks, 2014. [ arXiv | http ]
[20] Aydın Buluç, henning Meyerhenke, Ilya Safro, Peter Sanders, and Christian Schulz. Recent advances in graph partitioning. Technical report, November 2013.
[21] Stephen J. Hardiman and Liran Katzir. Estimating clustering coefficients and size of social networks via random walk. In Proc. World Wide Web Conf., pages 539--550, 2013. [ http ]
[22] Yifan Hu. Current and future challenges in the visualization of large networks. Technical report, Encyclopedia of Social Network Analysis and Mining, to be published by Springer, 2013. [ .pdf ]
[23] Jérôme Kunegis, Damien Fay, and Christian Bauckhage. Spectral evolution in dynamic networks. Knowledge and Information Systems, 37(1):1--36, 2013. [ http ]
[24] Jérôme Kunegis, Marcel Blattner, and Christine Moser. Preferential attachment in online networks: Measurement and explanations. In Proc. Web Science Conf., pages 205--214, 2013. [ .pdf ]
[25] Jérôme Kunegis, Julia Preusse, and Felix Schwagereit. What is the added value of negative links in online social networks? In Proc. Int. World Wide Web Conf., pages 727--736, 2013. [ .pdf ]
[26] Julia Preusse, Jérôme Kunegis, Matthias Thimm, Thomas Gottron, and Steffen Staab. Structural dynamics of knowledge networks. In Proc. Int. Conf. on Weblogs and Social Media, pages 506--515, 2013. [ .pdf ]
[27] M. Beguerisse-Diaz, B. Vangelov, and M. Barahona. Finding role communities in directed networks using role-based similarity, markov stability and the relaxed minimum spanning tree. In Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE, pages 937--940, Dec 2013. [ DOI ]
[28] Jérôme Kunegis, Sergej Sizov, Felix Schwagereit, and Damien Fay. Diversity dynamics in online networks. In Proc. Conf. on Hypertext and Social Media, pages 255--264, 2012. [ .pdf ]
[29] Jérôme Kunegis. KONECT Cloud -- large scale network mining in the cloud. In Proc. Spring 2012 Future SOC Lab Day, HPI Technical Report Series, April 2012. [ .pdf ]
[30] Jérôme Kunegis and Julia Preusse. Fairness on the web: Alternatives to the power law. In Proc. Web Science Conf., pages 175--184, 2012. [ .pdf ]
[31] Jérôme Kunegis and Jörg Fliege. Predicting directed links using nondiagonal matrix decomposition. In Proc. Int. Conf. on Data Mining, pages 948--953, 2012. [ .pdf ]
[32] Jérôme Kunegis. On the Spectral Evolution of Large Networks. PhD thesis, University of Koblenz--Landau, 2011. [ .pdf ]
[33] Stephan Spiegel, Jan Clausen, Sahin Albayrak, and Jérôme Kunegis. Link prediction on evolving data using tensor factorization. In Proc. Workshop on Behavior Informatics, 2011. [ .pdf ]
[34] Jérôme Kunegis, Stephan Schmidt, Andreas Lommatzsch, and Jürgen Lerner. Spectral analysis of signed graphs for clustering, prediction and visualization. In Proc. SIAM Int. Conf. on Data Mining, pages 559--570, 2010. [ .pdf ]
[35] Jérôme Kunegis, Ernesto W. De Luca, and Sahin Albayrak. The link prediction problem in bipartite networks. In Proc. Int. Conf. in Information Processing and Management of Uncertainty in Knowledge-based Systems, pages 380--389, 2010. [ .pdf ]
[36] Jérôme Kunegis, Damien Fay, and Christian Bauckhage. Network growth and the spectral evolution model. In Proc. Int. Conf. on Information and Knowledge Management, pages 739--748, 2010. [ .pdf ]
[37] Jérôme Kunegis and Andreas Lommatzsch. Learning spectral graph transformations for link prediction. In Proc. Int. Conf. on Machine Learning, pages 561--568, 2009. [ .pdf ]