Abstract
In our research we form a network called company co-mention network. News analytics data have been employed to collect the companies co-mentioning. Each company acquires a certain value based on the amount of news in which the company was mentioned. A matrix containing the number of co-mentioning news between pairs of companies has been created for network analysis. Each company is presented as a node, and news mentioning two companies establishes a link between them. The network is constructed quite similarly to social networks or co-citation networks. The networked map of the companies is used to visualize the dependence structure of the economy by identifying groups of companies that are more central than others. The analysis carried out in the context of sectors of economy and territorial affiliation made it possible to identify key companies and to explore the similarity of the power law of vertices within sectors. QAP analysis between the co-mention network and the sector affiliation network was carried out to examine the ability of the sector affiliation network to predict the structure of the co-mention network.
This work was supported by the Russian Fund for Basic Research, project 18-37-00060.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abbasi, A., Altmann, J.: On the correlation between research performance and social network analysis measures applied to research collaboration networks. In: 44th Hawaii International Conference on System Sciences (HICSS), pp. 1–10. IEEE (2011)
Albert, R., Barabasi, A.L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74, 47–97 (2002)
An, J., Kwak, H.: What gets media attention and how media attention evolves over time: large-scale empirical evidence from 196 countries. In: Proceedings of the Eleventh International Conference on Web and Social Media, pp. 464–467. The AAAI Press, Palo Alto, Montreal, May 2017
Anthonisse, J.M.: The rush in a directed graph. Technical (1971)
Atzmueller, M., Schmidt, A., Kloepper, B., Arnu, D.: HypGraphs: an approach for analysis and assessment of graph-based and sequential hypotheses. In: Appice, A., Ceci, M., Loglisci, C., Masciari, E., Raś, Z.W. (eds.) NFMCP 2016. LNCS (LNAI), vol. 10312, pp. 231–247. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61461-8_15
Barnett, G.: A longitudinal analysis of the international telecommunication network, 1978–1996. Am. Behav. Sci. 44, 1638–1655 (2001)
Barnett, G., Danowski, J.: The structure of communication: a network analysis of the international communication association. Hum. Commun. Res. 19(2), 264–285 (1992)
Barnett, G., Salisbury, J.: Communication and globalization: a longitudinal analysis of the international telecommunication network. J. World Syst. Res. 2(16), 1–17 (1996)
Basov, N., Lee, J.S., Antoniuk, A.: Social networks and construction of culture: a socio-semantic analysis of art groups. In: Cherifi, H., Gaito, S., Quattrociocchi, W., Sala, A. (eds.) Complex Networks & Their Applications V. COMPLEX NETWORKS 2016, vol. 693, pp. 785–796. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-50901-3_62
Choi, E., Lee, K.C.: Relationship between social network structure dynamics and innovation: micro-level analyses of virtual cross-functional teams in a multinational B2B firm. Comput. Hum. Behav. 65, 151–162 (2016)
Coletto, M., Garimella, K., Gionis, A., Lucchese, C.: A motif-based approach for identifying controversy. In: Proceedings of the Eleventh International Conference on Web and Social Media, pp. 496–499. The AAAI Press, Palo Alto, Montreal, May 2017
Correa, C., Crnovrsanin, T., Ma, K.L.: Visual reasoning about social networks using centrality sensitivity. IEEE Trans. Vis. Comput. Graph. 18(1), 106–120 (2012)
Dekker, D., Krackhardt, D., Snijders, T.A.B.: Sensitivity of MRQAP tests to collinearity and autocorrelation conditions. Psychometrika 72(4), 563–581 (2007)
Deville, P., Song, C., Eagle, N., Blondel, V.D., Barabási, A.L., Wang, D.: Scaling identity connects human mobility and social interactions. Proc. Nat. Acad. Sci. 113(26), 7047–7052 (2016). http://www.pnas.org/content/113/26/7047
Granovetter, M.: The strength of weak ties. Am. J. Sociol. 78, 1360 (1973)
Guelzim, N., Bottani, S., Bourgine, P., Kepes, F.: Topological and causal structure of the yeast transciptional network. Nat. Genet. 31, 60–63 (2002)
Haishu, Q., Ying, L., Xin, O.: Industrial association, common information spill out and industry stock indexes co-movement. Syst. Eng. Theory Pract. 36(11), 2737 (2016)
Hubert, L.: Assignment Methods in Combinatorial Data Analysis. Dekker, New York (1987)
Jeong, H., Tombor, B., Albert, R., Oltvai, Z.N., Barabasi, A.L.: The large-scale organization of metabolic networks. Nature 407, 651–654 (2000)
Kim, H., Barnett, G.A.: Social network analysis using author co-citation data. In: AMCIS 2008 Proceedings, Paper 172, pp. 1–9 (2008)
Krackardt, D.: Qap partialling as a test of spuriousness. Soc. Netw. 9(2), 171–186 (1987)
Landherr, A., Friedl, B., Heidemann, J.: A critical review of centrality measures in social networks. Bus. Inf. Syst. Eng. 2(6), 371–385 (2010)
Le, H., Shafiq, Z., Srinivasan, P.: Scalable news slant measurement using twitter. In: Proceedings of the Eleventh International Conference on Web and Social Media, pp. 584–587. The AAAI Press, Palo Alto, Montreal, May 2017
Lee, T.I., Rinaldi, N.J., Robert, F., Odom, D.T., Bar-Joseph, Z., Gerber, G.K., Hannett, N.M., Harbison, C.T., Thompson, C.M., Simon, I., Zeitlinger, J., Jennings, E.G., Murray, H.L., Gordon, D.B., Ren, B., Wyrick, J.J., Tagne, J.B., Volkert, T.L., Fraenkel, E., Gifford, D.K., Young, R.A.: Transcriptional regulatory networks in saccharomyces cerevisiae. Science 298(5594), 799–804 (2002)
Liu, B.: Web Data Mining. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-37882-2
Liu, X., Bollen, J., Nelson, M.L., de Sompel, V.: Co-authorship networks in the digital library research community. Inf. Process. Manag. 41(6), 1462–1480 (2005)
Mantel, N.: The detection of disease clustering and a generalized regression approach. Cancer Res. 27(2), 209–220 (1967)
Mitra, G., Mitra, L. (eds.): The Handbook of News Analytics in Finance. Wiley, Hoboken (2011)
Mitra, G., Yu, X. (eds.): Handbook of Sentiment Analysis in Finance (2016)
Onnela, J.P., Arbesman, S., Gonzalez, M.C., Barabasi, A.L., Christakis, N.A.: Geographic constraints on social network groups. PLoS ONE 6(4), 1–7 (2011). https://doi.org/10.1371/journal.pone.0016939
Ravasz, R., Barabasi, A.L.: Hierarchical organization in complex networks. Phys. Rev. E 67, 026112 (2003)
Ravasz, R., Somera, A.L., Mongru, D.A., Oltvai, Z.N., Barabasi, A.L.: Hierarchical organization of modularity in metabolic networks. Science 297, 1551–1555 (2002)
Said, Y.H., Wegman, E., Sharabati, W.K., Rigsby, J.: Social networks of author-coauthor relationships. Comput. Stat. Data Anal. 52(4), 2177–2184 (2008)
Samoilenko, A., Karimi, F., Edler, D., Kunegis, J., Strohmaier, M.: Linguistic neighbourhoods: explaining cultural borders on wikipedia through multilingual co-editing activity. EPJ Data Sci. 5, 1–20 (2016)
Sidorov, S.P., Faizliev, A.R., Balash, V.A., Gudkov, A.A., Chekmareva, A.Z., Anikin, P.K.: Company co-mention network analysis. Springer Proceedings in Mathematics and Statistics (2018, in press)
Sidorov, S., Faizliev, A., Balash, V.: Measuring long-range correlations in news flow intensity time series. Int. J. Mod. Phys. C 28(08), 1750103 (2017)
Sidorov, S., Faizliev, A., Balash, V.: Scale invariance of news flow intensity time series. Nonlinear Phenom. Complex Syst. 19(4), 368–377 (2016)
Sidorov, S., Faizliev, A., Balash, V.: Fractality and multifractality analysis of news sentiments time series. IAENG Int. J. Appl. Math. 48(1), 90–97 (2018)
Sidorov, S., Faizliev, A., Balash, V., Korobov, E.: Long-range correlation analysis of economic news flow intensity. Phys. A 444, 205–212 (2016)
Sinatra, R., Wang, D., Deville, P., Song, C., Barabási, A.L.: Quantifying the evolution of individual scientific impact. Science 354(6312), aaf5239 (2016). http://science.sciencemag.org/content/354/6312/aaf5239
Tang, J., Zhang, D., Yao, L.: Social network extraction of academic researchers. In: Seventh IEEE International Conference on Data Mining, pp. 292–301. IEEE (2007)
Vahtera, P., Buckley, P.J., Aliyev, M., Clegg, J., Cross, A.R.: Influence of social identity on negative perceptions in global virtual teams. J. Int. Manag. 23(4), 367–381 (2017)
Wagner, A., Fell, D.A.: The small world inside large metabolic networks. Proc. R. Soc. Lond. B Biol. Sci. 268, 1803–1810 (2001)
Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994)
West, R., Pfeffer, J.: Armed conflicts in online news: a multilingual study. In: Proceedings of the Eleventh International Conference on Web and Social Media, pp. 309–318. The AAAI Press, Palo Alto, Montreal, May 2017
Yook, S.H., Oltvai, Z.N., Barabasi, A.L.: Functional and topological characterization of protein interaction networks. Proteomics 4, 928–942 (2004)
Zhang, A., Culbertson, B., Paritosh, P.: Characterizing online communities using coarse discourse structures. In: Proceedings of the Eleventh International Conference on Web and Social Media, pp. 357–366. The AAAI Press, Palo Alto, Montreal, May 2017
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Sidorov, S.P. et al. (2018). QAP Analysis of Company Co-mention Network. In: Bonato, A., Prałat, P., Raigorodskii, A. (eds) Algorithms and Models for the Web Graph. WAW 2018. Lecture Notes in Computer Science(), vol 10836. Springer, Cham. https://doi.org/10.1007/978-3-319-92871-5_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-92871-5_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92870-8
Online ISBN: 978-3-319-92871-5
eBook Packages: Computer ScienceComputer Science (R0)