WSEAS Transactions on Business and Economics

Print ISSN: 1109-9526
E-ISSN: 2224-2899

Volume 16, 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 16, 2019

Applying Co-mention Network Analysis for Event Detection

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

Download as PDF

ABSTRACT: This paper studies some characteristics and features of the economic and finance news flow. We consider a company co-mentions network as a graph in which nodes serve as the world’s largest companies mentioned in financial and economic news flow. Two companies (nodes) are linked if they were mentioned in the same news item. First, we analyze the dynamics of the structural properties of the company co-mentions network over time. Then we propose new method for event detection based on the company co-mention network. The idea behind the method is that more significant news should attract more attention and lead to an increase in the intensity of the news flow. A change in the intensity of co-mentions can be interpreted as a signal or marker of unexpected phenomena that may affect a relatively narrow or a wide range of economic actors. The analysis performed in the paper suggests that the decomposition of the co-mention matrices can be used to separate news signals. News corresponding to the stable part of the graph appear more often; respectively, they carry less information. The unexpected news revealed by the method described this paper deserves special consideration when making financial and investment decisions. The proposed approach to the selection of the event part can be used in the development of algorithms for detecting new events in the financial and economic sphere

KEYWORDS: finance and economics networks; degree distribution; market graph; event detection


[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] Aggarwal, C.C.: Mining text and social streams: A review. SIGKDD Explor. Newsl. 15(2), 9-19 (Jun 2014)

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

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

[5] 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)

[6] Balash V.A., Chekmareva A, Faizliev A.R., Sidorov S.P., Mironov S.V. and Volkov D.: Analysis of news flow dynamics based on the company co-mention network characteristics. Lecture Notes in Engineering Science. In Press

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

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

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

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

[11] 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)

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

[13] Dong, X., Mavroeidis, D., Calabrese, F., Frossard, P.: Multiscale event detection in social media. Data Min. Knowl. Discov. 29(5), 1374-1405 (Sep 2015)

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

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

[16] 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)

[17] 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)

[18] 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)

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

[20] Huang, Y., Li, Y., Shan, J.: Spatial-temporal event detection from geo-tagged tweets. ISPRS International Journal of Geo-Information 7(4) (2018)

[21] 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)

[22] 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)

[23] Kleinberg, J.: Bursty and hierarchical structure in streams. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 91-101. KDD '02, ACM, New York, NY, USA (2002)

[24] Kolchyna, O., Souza, T.T.P., Treleaven, P.C., Aste, T.: A framework for twitter events detection, differentiation and its application for retail brands. In: 2016 Future Technologies Conference (FTC). pp. 323-331 (Dec 2016)

[25] 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)

[26] 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)

[27] 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

[28] 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)

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

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

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

[32] 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)

[33] 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

[34] 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)

[35] Sidorov S.P., Faizliev A.R., Levshunov M., Chekmareva A., Gudkov A., Korobov E.: Graph-Based clustering approach for economic and financial event detection using news analytics data. In: Staab S., Koltsova O., Ignatov D. (eds) Social Informatics. SocInfo 2018. Lecture Notes in Computer Science, vol 11186. Springer, Cham: 271-280

[36] 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)

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

[38] Yang, Y., Pierce, T., Carbonell, J.: A study of retrospective and on-line event detection. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 28- 36. SIGIR '98, ACM, New York, NY, USA (1998)

[39] 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 Business and Economics, ISSN / E-ISSN: 1109-9526 / 2224-2899, Volume 16, 2019, Art. #3, pp. 18-24

Copyright Β© 2018 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0