WSEAS Transactions on Systems and Control


Print ISSN: 1991-8763
E-ISSN: 2224-2856

Volume 14, 2019

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of WSEAS Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.


Volume 14, 2019



The Evolution of Degree Distribution, Maximum Cliques and Maximum Independent Sets of Company Co-Mention Network over Time

AUTHORS: Vladimir A. Balash, Alexey R. Faizliev, Elena V. Korotkovskaya, Sergei V. Mironov, Fedor M. Smolov, Sergei P. Sidorov, Daniil A. Volkov

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ABSTRACT: The main subject of our research is the characteristics and features of the economic and finance news flow. In this paper we construct company co-mentions network as a graph in which nodes serve as the world’s largest companies mentioned in financial and economic news flow. We link two nodes if two companies were mentioned in the same news item. We construct company co-mention networks for 72 consecutive monthly periods to analyze the dynamics of the structural properties of the company co-mentions network over time. These structural properties are examined based on different graph characteristics such as the distribution of the degrees of the vertices in this graph as well as maximum clique and maximum independent sets sizes. Some conclusions are derived with respect to the dynamics of the evolution of the company comentions network over time.

KEYWORDS: - graph properties; social networks; degree distribution; market graph

REFERENCES:

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[2] Albert, R., Barabasi, A.L.: Statistical mechanics of complex networks. Reviews of Modern Physics 74, 47–97 (2002)

[3] Albert, R.: Scale-free networks in cell biology. Journal of Cell Science 118, 4947–4957 (2005)

[4] Arora, S., Safra, S.: Approximating clique is NP-complete. In: Proceedings of the 33rd IEEE symposium on foundations on computer science. pp. 2–13 (1992)

[5] Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)

[6] Batrinca, B., Treleaven, P.C.: Social media analytics: a survey of techniques, tools and platforms. AI & SOCIETY 30(1), 89–116 (Feb 2015)

[7] Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D. U.: Complex networks: Structure and dynamics. Physics Reports 424, 175–308 (2006)

[8] Bron, C., Kerbosch, J.: Algorithm 457: Finding all cliques of an undirected graph. Commun. ACM 16(9) (Sep 1973)

[9] Brown, M.L., Donovan, T.M., Mickey, R.M., Warrington, G.S., Schwenk, W.S., Theobald, D.M.: Predicting effects of future development on a territorial forest songbird: methodology matters. Landscape Ecology 33(1), 93–108 (2018)

[10] Daron, A., Kostas, B., Asuman, O.: Dynamics of information exchange in endogenous social networks. Theoretical Economics 9(1), 41–97 (2014)

[11] Dorogovtsev, S.N., Mendes, J.F.F.: Evolution of networks. Adv. Phys 51, 1079 (2002)

[12] Eppstein, D., Löffler, M., Strash, D.: Listing all maximal cliques in sparse graphs in nearoptimal time. CoRR abs/1006.5440 (2010)

[13] Eppstein, D., Löffler, M., Strash, D.: Listing all maximal cliques in large sparse real-world graphs. J. Exp. Algorithmics 18, 3.1:3.1– 3.1:3.21 (2013)

[14] Garey, M.R., Johnson, D.S.: Computers and Intractability; A Guide to the Theory of NPCompleteness. W. H. Freeman & Co., New York, NY, USA (1990)

[15] Gendreau, M., Picard, J.C., Zubieta, L.: An efficient implicit enumeration algorithm for the maximum clique problem. In: Eiselt, H.A., Pederzoli, G. (eds.) Advances in Optimization and Control. pp. 79–91. Springer Berlin Heidelberg, Berlin, Heidelberg (1988)

[16] Hástad, J.: Clique is hard to approximate within 𝑛𝑛(1−𝜀𝜀) . In: Acta Mathematica. pp. 627–636 (1996)

[17] Kalyagin, V., Koldanov, A., Koldanov, P., Pardalos, P., Zamaraev, V.: Measures of uncertainty in market network analysis. Physica A: Statistical Mechanics and its Applications 413, 59–70 (2014)

[18] 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 Journal of Science 43(4), 95–113 (2016)

[19] Kremnyov, O., Kalyagin, V.A.: Identification of cliques and independent sets in pearson and fechner correlations networks. In: Kalyagin, V.A., Koldanov, P.A., Pardalos, P.M. (eds.) Models, Algorithms and Technologies for Network Analysis. pp. 165–173. Springer International Publishing, Cham (2016)

[20] Latyshev, A., Koldanov, P.: Investigation of connections between pearson and fechner correlations in market network: Experimental study. In: Kalyagin, V.A., Koldanov, P.A., Pardalos, P.M. (eds.) Models, Algorithms and Technologies for Network Analysis. pp. 175– 182. Springer International Publishing, Cham (2016)

[21] Lofdahl, C., Stickgold, E., Skarin, B., Stewart, I.: Extending generative models of large scale networks. Procedia Manufacturing 3(Supplement C), 3868 – 3875, 6th International Conference on Applied Human Factors and Ergonomics (AHFE 2015) and the Affiliated Conferences, AHFE 2015

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[23] Mitra, G., Mitra, L. (eds.): The Handbook of News Analytics in Finance. John Wiley & Sons (2011)

[24] Mitra, G., Yu, X. (eds.): Handbook of Sentiment Analysis in Finance (2016)

[25] Newman, M.E.J.: The structure and function of complex networks. Siam Review 45, 167–256 (2003)

[26] Schuller, B., Mousa, A.E., Vryniotis, V.: Sentiment analysis and opinion mining: on optimal parameters and performances. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 5(5), 255–263 (2015)

[27] 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 247, 341-354 (2018)

[28] Sidorov, S.P., Faizliev, A.R., Balash, V.A., Gudkov, A.A., Chekmareva, A.Z., Levshunov, M., Mironov, S.V.: QAP analysis of company co-mention network. In: Bonato, A., Prałat, P., Raigorodskii, A. (eds.) Algorithms and Models for the Web Graph. pp. 83–98. Springer International Publishing, Cham (2018)

[29] Vizgunov, A., Goldengorin, B., Kalyagin, V., Koldanov, A., Koldanov, P., Pardalos, P.M.: Network approach for the russian stock market. Computational Management Science 11(1), 45– 55 (2014)

[30] Wu, Q., Hao, J.K.: Solving the winner determination problem via a weighted maximum clique heuristic. Expert Syst. Appl. 42(1), 355–365 (2015)

[31] Zhai, J., Cao, Y., Yao, Y., Ding, X., Li, Y.: Coarse and fine identification of collusive clique in financial market. Expert Systems with Applications 69, 225–238 (2017)1] Abello, J., Pardalos, P.M., Resende, M.G.C.: On maximum clique problems in very large graphs. In: External Memory Algorithms. pp. 119–130. American Mathematical Society (1999)

[2] Albert, R., Barabasi, A.L.: Statistical mechanics of complex networks. Reviews of Modern Physics 74, 47–97 (2002)

[3] Albert, R.: Scale-free networks in cell biology. Journal of Cell Science 118, 4947–4957 (2005)

[4] Arora, S., Safra, S.: Approximating clique is NP-complete. In: Proceedings of the 33rd IEEE symposium on foundations on computer science. pp. 2–13 (1992)

[5] Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)

[6] Batrinca, B., Treleaven, P.C.: Social media analytics: a survey of techniques, tools and platforms. AI & SOCIETY 30(1), 89–116 (Feb 2015)

[7] Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D. U.: Complex networks: Structure and dynamics. Physics Reports 424, 175–308 (2006)

[8] Bron, C., Kerbosch, J.: Algorithm 457: Finding all cliques of an undirected graph. Commun. ACM 16(9) (Sep 1973)

[9] Brown, M.L., Donovan, T.M., Mickey, R.M., Warrington, G.S., Schwenk, W.S., Theobald, D.M.: Predicting effects of future development on a territorial forest songbird: methodology matters. Landscape Ecology 33(1), 93–108 (2018)

[10] Daron, A., Kostas, B., Asuman, O.: Dynamics of information exchange in endogenous social networks. Theoretical Economics 9(1), 41–97 (2014)

[11] Dorogovtsev, S.N., Mendes, J.F.F.: Evolution of networks. Adv. Phys 51, 1079 (2002)

[12] Eppstein, D., Löffler, M., Strash, D.: Listing all maximal cliques in sparse graphs in nearoptimal time. CoRR abs/1006.5440 (2010)

[13] Eppstein, D., Löffler, M., Strash, D.: Listing all maximal cliques in large sparse real-world graphs. J. Exp. Algorithmics 18, 3.1:3.1– 3.1:3.21 (2013)

[14] Garey, M.R., Johnson, D.S.: Computers and Intractability; A Guide to the Theory of NPCompleteness. W. H. Freeman & Co., New York, NY, USA (1990)

[15] Gendreau, M., Picard, J.C., Zubieta, L.: An efficient implicit enumeration algorithm for the maximum clique problem. In: Eiselt, H.A., Pederzoli, G. (eds.) Advances in Optimization and Control. pp. 79–91. Springer Berlin Heidelberg, Berlin, Heidelberg (1988)

[16] Hástad, J.: Clique is hard to approximate within 𝑛𝑛(1−𝜀𝜀) . In: Acta Mathematica. pp. 627–636 (1996)

[17] Kalyagin, V., Koldanov, A., Koldanov, P., Pardalos, P., Zamaraev, V.: Measures of uncertainty in market network analysis. Physica A: Statistical Mechanics and its Applications 413, 59–70 (2014)

[18] 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 Journal of Science 43(4), 95–113 (2016)

[19] Kremnyov, O., Kalyagin, V.A.: Identification of cliques and independent sets in pearson and fechner correlations networks. In: Kalyagin, V.A., Koldanov, P.A., Pardalos, P.M. (eds.) Models, Algorithms and Technologies for Network Analysis. pp. 165–173. Springer International Publishing, Cham (2016)

[20] Latyshev, A., Koldanov, P.: Investigation of connections between pearson and fechner correlations in market network: Experimental study. In: Kalyagin, V.A., Koldanov, P.A., Pardalos, P.M. (eds.) Models, Algorithms and Technologies for Network Analysis. pp. 175– 182. Springer International Publishing, Cham (2016)

[21] Lofdahl, C., Stickgold, E., Skarin, B., Stewart, I.: Extending generative models of large scale networks. Procedia Manufacturing 3(Supplement C), 3868 – 3875, 6th International Conference on Applied Human Factors and Ergonomics (AHFE 2015) and the Affiliated Conferences, AHFE 2015

[22] Manaman, H.S., Jamali, S., AleAhmad, A.: Online reputation measurement of companies based on user-generated content in online social networks. Computers in Human Behavior 54(Supplement C), 94 – 100 (2016)

[23] Mitra, G., Mitra, L. (eds.): The Handbook of News Analytics in Finance. John Wiley & Sons (2011)

[24] Mitra, G., Yu, X. (eds.): Handbook of Sentiment Analysis in Finance (2016)

[25] Newman, M.E.J.: The structure and function of complex networks. Siam Review 45, 167–256 (2003)

[26] Schuller, B., Mousa, A.E., Vryniotis, V.: Sentiment analysis and opinion mining: on optimal parameters and performances. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 5(5), 255–263 (2015)

[27] 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 247, 341-354 (2018)

[28] Sidorov, S.P., Faizliev, A.R., Balash, V.A., Gudkov, A.A., Chekmareva, A.Z., Levshunov, M., Mironov, S.V.: QAP analysis of company co-mention network. In: Bonato, A., Prałat, P., Raigorodskii, A. (eds.) Algorithms and Models for the Web Graph. pp. 83–98. Springer International Publishing, Cham (2018)

[29] Vizgunov, A., Goldengorin, B., Kalyagin, V., Koldanov, A., Koldanov, P., Pardalos, P.M.: Network approach for the russian stock market. Computational Management Science 11(1), 45– 55 (2014)

[30] Wu, Q., Hao, J.K.: Solving the winner determination problem via a weighted maximum clique heuristic. Expert Syst. Appl. 42(1), 355–365 (2015)

[31] Zhai, J., Cao, Y., Yao, Y., Ding, X., Li, Y.: Coarse and fine identification of collusive clique in financial market. Expert Systems with Applications 69, 225–238 (2017)

WSEAS Transactions on Systems and Control, ISSN / E-ISSN: 1991-8763 / 2224-2856, Volume 14, 2019, Art. #12, pp. 97-103


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