Abstract
Evaluating a network partition just only via conventional quality metrics—such as modularity, conductance or normalized mutual of information—is usually insufficient. Indeed, global quality scores of a network partition or its clusters do not provide many ideas about their structural characteristics. Furthermore, quality metrics often fail to reach an agreement especially in networks whose modular structures are not very obvious. Evaluating the goodness of network partitions in function of desired structural properties is still a challenge. Here, we propose a methodology that allows one to expose structural information of clusters in a network partition in a comprehensive way, thus eventually helps one to compare communities identified by different community detection methods. This descriptive approach also helps to clarify the composition of communities in real-world networks. The methodology hence bring us a step closer to the understanding of modular structures in complex networks.
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References
Almeida, H., Guedes, D., Meira Jr., W., Zaki, M.J.: Is there a best quality metric for graph clusters? In: Gunopulos, D., et al. (eds.) ECML PKDD 2011, Part I. LNCS, vol. 6911, pp. 44–59. Springer, Heidelberg (2011)
Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286 (5439), 509–512 (1999)
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70, 066111 (2004)
Creusefond, J., Largillier, T., Peyronnet, S.: On the evaluation potential of quality functions in community detection for different contexts. In: Advances in Network Science: 12th International Conference and School, NetSci-X (2016)
Faloutsos, M., Faloutsos, P., Faloutsos, C.: On power-law relationships of the internet topology. In: Proceedings of the Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, pp. 251–262 (1999)
Fire, M., Tenenboim, L., Lesser, O., Puzis, R., Rokach, L., Elovici, Y.,: Link prediction in social networks using computationally efficient topological features. In: 2011 IEEE Third International Conference on Social Computing (SocialCom), pp. 73–80 (2011)
Girvan, M. and Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99 (12), 7821–7826 (2002)
Guimerà, R., Amaral, L.A.N.: Cartography of complex networks: modules and universal roles. J. Stat. Mech. Theory Exp. 2005, P02001 (2005)
Guimerà, R., Amaral, L.A.N.: Functional cartography of complex metabolic networks. Lett. Nat. 7028, 895–900 (2005)
Leskovec, J., Krevl, A.: SNAP Datasets: Stanford Large Network Dataset Collection. http://snap.stanford.edu/data (2014). Reference date: 13/12/2016
Newman, M.E., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69 (2), 026113 (2004)
Ravasz, E., Somera, A.L., Mongru, D.A., Oltvai, Z.N., Barabási, A.-L.: Hierarchical organization of modularity in metabolic networks. Science 297 (5586), 1551–1555 (2002)
Yang, J., Leskovec, J.: Defining and evaluating network communities based on ground-truth. Knowl. Inf. Syst. 42, 181–213 (2015). http://dx.doi.org/10.1007/s10115-013-0693-z
Yook, S.H., Jeong, H., Barabási, A.-L.: Modeling the Internet’s large-scale topology. Proc. Natl. Acad. Sci. 99, 13382–13386 (2002)
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Dao, VL., Bothorel, C., Lenca, P. (2017). Community Structures Evaluation in Complex Networks: A Descriptive Approach. In: Shmueli, E., Barzel, B., Puzis, R. (eds) 3rd International Winter School and Conference on Network Science . NetSci-X 2017. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-55471-6_2
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DOI: https://doi.org/10.1007/978-3-319-55471-6_2
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