Company Co-mention Network Analysis

  • S. P. SidorovEmail author
  • A. R. Faizliev
  • V. A. Balash
  • A. A. Gudkov
  • A. Z. Chekmareva
  • P. K. Anikin
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 247)


In network analysis, the importance of an object can be found by using different centrality metrics such that degree, closeness, betweenness, and so on. In our research we form a network, which we called company co-mention network. The network is constructed quite similar to social networks or co-citation networks. Each company is a node and news mentioning two companies establishes a link between them. Each company acquires a certain value based on the amount of news which is mentioned in the company. This research examines the network of companies by using companies’ co-mention news data. A matrix containing the number of co-mentioning news between pairs of companies is created for network analysis of companies, whose shares are traded on major financial markets. We used different types of SNA metrics (degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, frequency) to find a key company in the network. Moreover, it was shown that distribution of degrees and clustering-degree relations for our network follows the power law, although with nontypical indicators of degree exponent. News analytics data have been employed to collect the companies co-mentioning news data, and R packages have been used for network analysis as well as network visualization.


Network analysis News analytics Degree distribution SNA metrics 


  1. 1.
    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). IEEE pp. 1–10 (2011)Google Scholar
  2. 2.
    Albert, R.: Scale-free networks in cell biology. J. Cell Sci. 118, 4947–4957 (2005)CrossRefGoogle Scholar
  3. 3.
    Albert, R., Albert, I., Nakarado, G.: Structural vulnerability of the north American power grid. Phys. Rev. E 69, 025103(R) (2004)CrossRefGoogle Scholar
  4. 4.
    Albert, R., Barabasi, A.L.: Statistical mechanics of complex networks. Rev. Modern Phys. 74, 47–97 (2002)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Amaral, L.A.N., Scala, A., Barthelemy, M., Stanley, H.E.: Classes of behavior of small-world networks. Proc. Natl. Acad. Sci. (USA) 97, 1149 (2000)Google Scholar
  6. 6.
    Anthonisse, J.M.: The rush in a directed graph. Technical (1971)Google Scholar
  7. 7.
    Barnett, G.: A longitudinal analysis of the international telecommunication network. Am. Behav. Sci. 44, 1655–1938 (2001)CrossRefGoogle Scholar
  8. 8.
    Barnett, G., Danowski, J.A.: The structure of communication: a network analysis of the international communication association. Hum. Commun. Res. 19(2), 264–285 (1992)Google Scholar
  9. 9.
    Barnett, G., Park, H.: The structure of international internet hyperlinks and bilateral bandwidth. Annales des te‘le‘communications 60, 9–10, 1110–1127 (2005)Google Scholar
  10. 10.
    Barnett, G., Salisbury, J.: Communication and globalization: a longitudinal analysis of the international telecommunication network. J. World Syst. Res. 2(16), 1–17 (1996)Google Scholar
  11. 11.
    Batrinca, B., Treleaven, P.C.: Social media analytics: a survey of techniques, tools and platforms. AI & Society 30(1), 89–116 (2015)CrossRefGoogle Scholar
  12. 12.
    Bihari, A., Pandia, M.: Key author analysis in research professionals’ relationship network using citation indices and centrality. Procedia Comput. Sci. 57, 606–613 (2015)CrossRefGoogle Scholar
  13. 13.
    Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.U.: Complex networks: structure and dynamics. Phys. Rep. 424, 175–308 (2006)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Bonacich, P.: Factoring and weighting approaches to status scores and clique identification. J. Math. Soc. 2, 113–120 (1972)CrossRefGoogle Scholar
  15. 15.
    Bonacich, P., Lloyd, P.: Eigenvector-like measures of centrality for asymmetric relations. Soc. Netw. 23(3), 191–201 (2001)CrossRefGoogle Scholar
  16. 16.
    Borgatti, S.P.: Centrality and aids. Connections 18(1), 112–114 (1995)Google Scholar
  17. 17.
    Borgatti, S.P., Everett, M.G.: A graph-theoretic perspective on centrality. Soc. Netw. 28(4), 466–484 (2006)CrossRefGoogle Scholar
  18. 18.
    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)CrossRefGoogle Scholar
  19. 19.
    Correa, C.D., Crnovrsanin, T., Ma, K.L., Keeton, K.: The derivatives of centrality and their applications in visualizing social networks. Citeseer (2009)Google Scholar
  20. 20.
    Deng, Q., Wang, Z.: Degree centrality in scientific collaboration supernetwork. In: International Conference on Information Science and Technology (ICIST). IEEE pp. 259–262 (2011)Google Scholar
  21. 21.
    Ding, D., He, X.: Application of eigenvector centrality in metabolic networks. In: 2nd International Conference on Computer Engineering and Technology (ICCET). IEEE, vol. 1, pp. 1–89 (2010)Google Scholar
  22. 22.
    Dorogovtsev, S.N., Mendes, J.F.F.: Evolution of networks. Adv. Phys 51, 1079 (2002)CrossRefGoogle Scholar
  23. 23.
    Erkan, G., Radev, D.R.: LexRank: graph-based lexical centrality as salience in text summarization. J. Artif. Intell. Res. (JAIR) 22(1), 457–479 (2004)CrossRefGoogle Scholar
  24. 24.
    Estrada, E., Rodríguez-Velázquez, J.A.: Subgraph centrality in complex networks. Phys. Rev. E 71(5), 056–103 (2005)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Freeman, L.: Centrality in networks: I. Conceptual clarification. Soc. Netw. 1(3), 215–239 (1979)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Friedl, D.B., Heidemann, J.: A critical review of centrality measures in social networks. Bus. Inf. Syst. Eng. 2(6), 371–385 (2010)Google Scholar
  27. 27.
    Gómez, D., Figueira, J.R., Eusébio, A.: Modeling centrality measures in social network analysis using bi-criteria network flow optimization problems. Eur. J. Oper. Res. 226(2), 354–365 (2013)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Granovetter, M.: The strength of weak ties. Am. J. Soc. 78, 1360 (1973)CrossRefGoogle Scholar
  29. 29.
    Guelzim, N., Bottani, S., Bourgine, P., Kepes, F.: Topological and causal structure of the yeast transciptional network. Nat. Genet. 31, 60–63 (2002)CrossRefGoogle Scholar
  30. 30.
    Jeong, H., Tombor, B., Albert, R., Oltvai, Z.N., Barabasi, A.L.: The large-scale organization of metabolic networks. Nature 407, 651–654 (2000)CrossRefGoogle Scholar
  31. 31.
    Jin, J., Xu, K., Xiong, N., Liu, Y., Li, G.: Multi-index evaluation algorithm based on principal component analysis for node importance in complex networks. Networks IET 1(3), 108–115 (2012)CrossRefGoogle Scholar
  32. 32.
    Kas, M., Carley, L.R., Carley, K.M.: Monitoring social centrality for peer-to-peer network protection. IEEE Commun. Mag. 51(12), 155–161 (2013)CrossRefGoogle Scholar
  33. 33.
    Khan, W., Daud, A., Nasir, J.A., Amjad, T.: A survey on the state-of-the-art machine learning models in the context of NLP. Kuwait J. Sci. 43(4), 95–113 (2016)MathSciNetGoogle Scholar
  34. 34.
    Kincaid, D.: Communication network dynamics: cohesion, centrality, and cultural evolution. Prog. Commun. Sci. XII, 111–133 (1993)Google Scholar
  35. 35.
    Lee, T. I.: Transcriptional regulatory networks in saccharomyces cerevisiae. Science 298, 799–804 (2002)Google Scholar
  36. 36.
    Liu, B.: Web Data Mining. Springer (2007)Google Scholar
  37. 37.
    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)CrossRefGoogle Scholar
  38. 38.
    Lofdahl, C., Stickgold, E., Skarin, B., Stewart, I.: Extending generative models of large scale networks. Procedia Manufacturing 3 (Supplement C), 3868–3875. In: 6th International Conference on Applied Human Factors and Ergonomics (AHFE 2015) and the Affiliated Conferences, AHFE 2015Google Scholar
  39. 39.
    Manaman, H.S., Jamali, S., AleAhmad, A.: Online reputation measurement of companies based on user-generated content in online social networks. Comput. Hum. Behav. (Supplement C) 54, 94–100 (2016)Google Scholar
  40. 40.
    Mitra, G., Mitra, L. (eds.): The Handbook of News Analytics in Finance. Wiley (2011)Google Scholar
  41. 41.
    Mitra, G., Yu, X. (eds.): Handbook of Sentiment Analysis in Finance (2016)Google Scholar
  42. 42.
    Newman, M.E.J.: The structure and function of complex networks. SIAM Rev. 45, 167–256 (2003)MathSciNetCrossRefGoogle Scholar
  43. 43.
    Newman, M.E.: The mathematics of networks. The new palgrave encyclopedia of economics 2, 1–12 (2008)Google Scholar
  44. 44.
    Ravasz, R., Barabasi, A.L.: Hierarchical organization in complex networks. Phys. Rev. E 67, 026112 (2003)CrossRefGoogle Scholar
  45. 45.
    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)CrossRefGoogle Scholar
  46. 46.
    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). ElsevierGoogle Scholar
  47. 47.
    Schuller, B., Mousa, A.E., Vryniotis, V.: Sentiment analysis and opinion mining: on optimal parameters and performances. Wiley Interdiscip. Rev. Data Mining Knowl. Discov. 5(5), 255–263 (2015)CrossRefGoogle Scholar
  48. 48.
    Spizzirri, L.: Justification and application of eigenvector centralityGoogle Scholar
  49. 49.
    Tang, J., Zhang, D., Yao, L.: Social network extraction of academic researchers. In: Seventh IEEE International Conference on Data Mining. IEEE, pp. 292–301 (2007)Google Scholar
  50. 50.
    Umadevi, V.: Automatic co-authorship network extraction and discovery of central authors. Int. J. Comput. Appl. 74(4), 1–6 (2013)Google Scholar
  51. 51.
    Wagner, A., Fell, D.A.: The small world inside large metabolic networks. Proc. Royal Soc. Lond. Ser. B Biol. Sci. 268, 1803–1810 (2001)CrossRefGoogle Scholar
  52. 52.
    Wang, G., Shen, Y., Luan, E.: A measure of centrality based on modularity matrix. Prog. Nat. Sci. 18(8), 1043–1047 (2008)CrossRefGoogle Scholar
  53. 53.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press (1994)Google Scholar
  54. 54.
    Yook, S.H., Oltvai, Z.N., Barabasi, A.L.: Functional and topological characterization of protein interaction networks. Proteomics 4, 928–942 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • S. P. Sidorov
    • 1
    Email author
  • A. R. Faizliev
    • 1
  • V. A. Balash
    • 1
  • A. A. Gudkov
    • 1
  • A. Z. Chekmareva
    • 1
  • P. K. Anikin
    • 1
  1. 1.Saratov State UniversitySaratovRussian Federation

Personalised recommendations