文献类型：期刊文献

英文题名：Statistically validated mobile communication networks: the evolution of motifs in European and Chinese data

作者：Li, Ming-Xia[1,2];Palchykov, Vasyl[3,4,5];Jiang, Zhi-Qiang[1,2];Kaski, Kimmo[3];Kertesz, Janos[3,6];Micciche, Salvatore[7];Tumminello, Michele[8];Zhou, Wei-Xing[1,2];Mantegna, Rosario N.[6,7,9]

机构：[1]E China Univ Sci & Technol, Sch Sci, Sch Business, Shanghai 200237, Peoples R China;[2]E China Univ Sci & Technol, Res Ctr Econophys, Shanghai 200237, Peoples R China;[3]Aalto Univ, Dept Biomed Engn & Computat Sci, FI-00076 Aalto, Finland;[4]Natl Acad Sci Ukraine, Inst Condensed Matter Phys, UA-79011 Lvov, Ukraine;[5]Leiden Univ, Inst Lorentz, NL-2300 RA Leiden, Netherlands;[6]Cent European Univ, Ctr Network Sci, H-1051 Budapest, Hungary;[7]Univ Palermo, Dipartimento Fis & Chim, I-90128 Palermo, Italy;[8]Univ Palermo, Dipartimento Sci Econ Aziendali & Stat, I-90128 Palermo, Italy;[9]Cent European Univ, Dept Econ, H-1051 Budapest, Hungary

年份：2014

卷号：16

外文期刊名：NEW JOURNAL OF PHYSICS

收录：;EI(收录号：20143518120952);WOS:【SCI-EXPANDED(收录号:WOS:000341922200002)】；

基金：The authors thank L Barabasi for the European data. This work was partially supported by the National Natural Science Foundation of China (11205057), the Humanities and Social Sciences Fund of the Ministry of Education of China (09YJCZH040), the PhD Programs Foundation of the Ministry of Education of China (20120074120028), the Fok Ying Tong Education Foundation (132013), and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：complex networks; social systems; statistically validated networks; mobile call records; 3-motifs

摘要：Big data open up unprecedented opportunities for investigating complex systems, including society. In particular, communication data serve as major sources for computational social sciences, but they have to be cleaned and filtered as they may contain spurious information due to recording errors as well as interactions, like commercial and marketing activities, not directly related to the social network. The network constructed from communication data can only be considered as a proxy for the network of social relationships. Here we apply a systematic method, based on multiple-hypothesis testing, to statistically validate the links and then construct the corresponding Bonferroni network, generalized to the directed case. We study two large datasets of mobile phone records, one from Europe and the other from China. For both datasets we compare the raw data networks with the corresponding Bonferroni networks and point out significant differences in the structures and in the basic network measures. We show evidence that the Bonferroni network provides a better proxy for the network of social interactions than the original one. Using the filtered networks, we investigated the statistics and temporal evolution of small directed 3-motifs and concluded that closed communication triads have a formation time scale, which is quite fast and typically intraday. We also find that open communication triads preferentially evolve into other open triads with a higher fraction of reciprocated calls. These stylized facts were observed for both datasets.