Добавил:
ivanov666
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Приложение методов фрактального анализа, теории графов и анализа сложных сетей к исследованию временных рядов.pdf
Список литературы
94. Lu X., Tian J., Zhou Y., Li Z. Multifractal detrended fluctuation analysis
of the Chinese stock index futures market // Physica A : Statistical Mechanics and
its Applications. 2013. Vol. 392, № 6. P. 1452–1458.
95. Wang W., Liu K., Qin Z. Multifractal Analysis on the Return Series of
Stock Markets Using MF-DFA Method // International Conference on Informatics
and Semiotics in Organisations. Vol. 426. Berlin : Springer, 2014. P. 107–115.
96. Chen C., Wang Y. Understanding the multifractality in portfolio excess
returns // Physica A : Statistical Mechanics and its Applications. 2017. Vol. 466.
P. 346–355.
97. Mali P., Mukhopadhyay A. Multifractal characterization of gold market :
A multifractal detrended fluctuation analysis // Physica A : Statistical Mechanics
and its Applications. 2014. Vol. 413. P. 361–372.
98. Thompson J., Wilson J. Multifractal detrended fluctuation analysis : Practical applications to financial time series // Mathematics and Computers in Simulation. 2016. Vol. 126. P. 63–88.
99. Kantelhardt J., Zschiegner S., Koscielny-Bunde E., Havlin S., Bunde A.,
Stanley H. E. Multifractal detrended fluctuation analysis of nonstationary time
series // Physica A : Statistical Mechanics and its Applications. 2002. Vol. 316,
№ 1. P. 87–114.
100. Halsey T., Jensen M., Kadanoff L., Procaccia I., Shraiman B. Fractal
measures and their singularities : The characterization of strange sets // Nuclear
Physics B – Proceedings Supplements. 1987. Vol. 2. P. 501–511.
101. Sidorov S., Alexey F., Balash V., Korobov E. Long-range correlation
analysis of economic news flow intensity // Physica A : Statistical Mechanics and
its Applications. 2016. Vol. 444. P. 205–212.
102. Sidorov S., Alexey F., Balash V. Scale invariance of news flow intensity
time series // Nonlinear Phenomena in Complex Systems. 2016. Vol. 19, № 4.
P. 368–377.
103. Sidorov S., Faizliev A., Balash V. Long Range Correlation in Time Series
of News Sentiments // Lecture Notes in Engineering and Computer Science :
Proceedings of The World Congress on Engineering. London : Newswood Limited,
2017. Vol. II. P. 549–554.
104. Sidorov S., Faizliev A., Balash V. Measuring long-range correlations in
news flow intensity time series // International Journal of Modern Physics C. 2017.
Vol. 28, № 8. P. 1750103.
105. Grech D., Mazur Z. On the scaling ranges of detrended fluctuation analysis for long-term memory correlated short series of data // Physica A. 2013.
Vol. 392. P. 2384–2397.
106. Grech D., Mazur Z. Scaling range of power laws that originate from
fluctuation analysis // Physical Review E. 2013. May. Vol. 87. P. 052809.
107. Grech D., Mazur Z. Impact of Scaling Range on the Effectiveness of
Detrending Methods // Acta Physica Polonica A. 2015. Vol. 127, № 3-A. P. 59–65.
108. Simonsen I. Measuring anti-correlations in the Nordic electricity spot
market by wavelets // Physica A : Statistical Mechanics and its Applications. 2003.
Vol. 322. P. 597–606.
101
Приложение методов фрактального анализа, теории графов
109. Handbook of Sentiment Analysis in Finance / eds. G. Mitra, Y. Xiang.
London : John Wiley & Sons. 2016. 578 p.
110. 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. 2016. Vol. 54, № Supplement C. P. 94–100.
111. Batrinca B., Treleaven P. Social media analytics : a survey of techniques,
tools and platforms // AI & SOCIETY. 2015. Vol. 30, № 1. P. 89–116.
112. 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. 2015. Vol. 5, № 5. P. 255–263.
113. Khan W., Daud A., Nasir J., Amjad T. A survey on the state-of-the-art
machine learning models in the context of NLP // Kuwait Journal of Science. 2016.
Vol. 43, № 4. P. 95–113.
114. Sidorov S., Alexey F., Balash V., Gudkov A. Company Co-Mention Net-
work Analysis // Springer Proceedings in Mathematics and Statistics. 2018. Vol. 1.
P. 341–354.
115. Mantel N. The detection of disease clustering and a generalized regression approach // Cancer Research. 1967. Vol. 27, № 2. P. 209–220.
116. Hubert L. Assignment methods in combinatorial data analysis. New
York : Dekker, 1987. 326 p.
117. Krackhardt D. QAP partialling as a test of spuriousness // Social Networks. 1987. Vol. 9. P. 171–186.
118. Dekker D., Krackhardt D., Snijders T. Sensitivity of MRQAP Tests to
Collinearity and Autocorrelation Conditions // Psychometrika. 2007. Vol. 72, № 4.
P. 563–581.
119. Vahtera P., Buckley P., Aliyev M., Clegg J., Cross A. Influence of Social
Identity on Negative Perceptions in Global Virtual Teams // Journal of International
Management. 2017. Vol. 23, № 4. P. 367–381.
120. Atzmueller M., Schmidt A., Kloepper B., Arnu D. Hypgraphs : An approach for analysis and Assessment of graph-based and Sequential Hypotheses //
New Frontiers in Mining Complex Patterns / eds. A. Appice, M. Ceci, C. Loglisci,
E. Masciari, Z. W. Ra´s. Cham : Springer International Publishing, 2017. P. 231–
247.
121. Basov N., Lee J.-S., Antoniuk A. Social Networks and Construction of
Culture : A Socio-Semantic Analysis of Art Groups // Complex Networks & Their
Applications V / eds. H. Cherifi, S. Gaito, W. Quattrociocchi, A. Sala. Cham :
Springer International Publishing, 2017. P. 785–796.
122. 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 // Computers in Human Behavior. 2016. Vol. 65. P. 151–
162.
123. Samoilenko A., Karimi F., Edler D., Kunegis J., Strohmaier M. Linguistic
neighbourhoods : explaining cultural borders on Wikipedia through multilingual coediting activity // EPJ Data Science. 2016. Vol. 5. P. 1–20.
102
Список литературы
124. Haishu Q., Ying L., Xin O. Industrial association, common information
spill out and industry stock indexes co-movement // Systems Engineering – Theory
& Practice. 2016. Vol. 36, № 11. P. 2737.
125. Liu B. Web Data Mining. Chicago : Springer, 2007. 532 p.
126. Abbasi A., Altmann J. On the correlation between research performance
and social network analysis measures applied to research collaboration networks //
Hawaii International Conference on System Sciences. 2011. Vol. 1. P. 1–10.
127. Landherr A., Friedl B., Heidemann J. A critical review of centrality
measures in social networks // Business & Information Systems Engineering. 2010.
Vol. 2, № 6. P. 371–385.
128. Liu X., Bollen J., Nelson M. L., Van de Sompel H. Co-authorship net-
works in the digital library research community // Information Processing & Management. 2005. Vol. 41, № 6. P. 1462–1480.
129. Correa C., Crnovrsanin T., Ma K.-L. Visual reasoning about social net-
works using centrality sensitivity // IEEE Transactions on Visualization and Computer Graphics. 2012. Vol. 18, № 1. P. 106–120.
130. Tang J., Zhang D., Yao L. Social network extraction of academic re-
searchers // Seventh IEEE International Conference on Data Mining. Omaha :
IEEE, 2007. P. 292–301.
131. Said Y., Wegman E., Sharabati W., Rigsby J. Social networks of author-
coauthor relationships // Computational Statistics & Data Analysis, Elsevier. 2008.
Vol. 52, № 4. P. 2177–2184.
132. Freeman L. Centrality in networks : I. Conceptual clarification // Social
Networks. 1979. Vol. 1, № 3. P. 215–239.
133. Kas M., Carley R., Carley K. Monitoring social centrality for peer-topeer network protection // IEEE Communications Magazine. 2013. Vol. 51, № 12.
P. 155–161.
134. Umadevi V. Automatic Co-authorship Network Extraction and Discovery
of Central Authors // International Journal of Computer Applications. 2013. Vol. 74,
№ 4. P. 1–6.
135. Deng Q., Wang Z. Degree centrality in scientific collaboration supernetwork // International Conference on Information Science and Technology (ICIST).
Nanjing : IEEE, 2011. P. 259–262.
136. Erkan G., Radev D. LexRank : Graph-based lexical centrality as salience
in text summarization // Journal of Artificial Intelligence Research (JAIR). 2004.
Vol. 22, № 1. P. 457–479.
137. 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. 2012. Vol. 1, № 3. P. 108–115.
138. Correa C., Crnovrsanin T., Ma K.-l., Keeton K. The derivatives of centrality and their applications in visualizing social networks // Citeseer. 2009. Vol. 1.
P. 1–8.
139. Borgatti S., Everett M. A graph-theoretic perspective on centrality //
Social Networks. 2006. Vol. 28, № 4. P. 466–484.
103
Приложение методов фрактального анализа, теории графов
140. Kincaid L. Communication network dynamics : Cohesion, centrality, and
cultural evolution // Progress in Communication Sciences. 1993. Vol. XII. P. 111–
133.
141. Estrada E., Rodriguez-Velazquez J. A. Subgraph centrality in complex
networks // Physical Review E. 2005. Vol. 71, № 5. P. 056–103.
142. Wang G., Shen Y., Luan E. A measure of centrality based on modularity
matrix // Progress in Natural Science. 2008. Vol. 18, № 8. P. 1043–1047.
143. G´omez D., Figueira J. R., Eus´ebio A. Modeling centrality measures in
social network analysis using bi-criteria network flow optimization problems //
European Journal of Operational Research. 2013. Vol. 226, № 2. P. 354–365.
144. Borgatti S. Centrality and AIDS // Connections. 1995. Vol. 18, № 1.
P. 112–114.
145. Bonacich P. Factoring and weighting approaches to status scores and
clique identification // Journal of Mathematical Sociology. 1972. Vol. 2. P. 113–
120.
146. Spizzirri L. Justification and Application of Eigenvector Centrality //
Algebra in Geography. 2011. Vol. 1. P. 1–12.
147. Bonacich P., Lloyd P. Eigenvector-like measures of centrality for asym-
metric relations // Social Networks. 2001. Vol. 23, № 3. P. 191–201.
148. Bonacich P. Power and Centrality : A Family of Measures // American
Journal of Sociology. 1987. Vol. 92, № 5. P. 1170–1182.
149. Bihari A., Pandia M. Key Author Analysis in Research Professionals’
Relationship Network Using Citation Indices and Centrality // Procedia Computer
Science. 2015. Vol. 57. P. 606–613.
150. Albert R., Barabasi A.-L. Statistical mechanics of complex networks //
Reviews of Modern Physics. 2002. Vol. 74. P. 47–97.
151. Newman M. The structure and function of complex networks // Siam
Review. 2003. Vol. 45. P. 167–256.
152. Lofdahl C., Stickgold E., Skarin B., Stewart I. Extending Generative
Models of Large Scale Networks // Procedia Manufacturing. 2015. 12. Vol. 3.
P. 3868–03875.
153. Jeong H., Tombor B., Albert R., Oltvai Z., Barabasi A.-L. The large-scale
organization of metabolic networks // Nature. 2000. Vol. 407. P. 651–654.
154. Guelzim N., Bottani S., Bourgine P., K´ep´es F. Topological and causal
structure of the yeast transciptional network // Nature Genetics. 2002. Vol. 31.
P. 60–63.
155. Lee T., Rinaldi N., Robert F., Odom D., Bar-Joseph Z., Gerber G.,
Hannett N., Harbison C., Thompson C., Simon I., Zeitlinger J., Jennings E., Murray H., Gordon D., Ren B., Wyrick J., Tagne J.-B., Volkert T., Fraenkel E., Young R.
Transcriptional regulatory networks in Saccharomyces cerevisiae // Science. 2002.
Vol. 298. P. 799–804.
156. Albert R., Albert I., Nakarado G. Structural vulnerability of the North
American power grid // Physical Review E. 2004. Vol. 69. P. 025103.
104
Список литературы
157. Amaral L., Scala A., Barthelemy M., Stanley H. Classes of behavior of
small-world networks // Proceedings of the National Academy of Sciences (USA).
2000. Vol. 97. P. 1149–1152.
158. Wagner A., Fell D. The small world inside large metabolic networks //
Proceedings of the Royal Society of London Series B Biological Sciences. 2001.
Vol. 268. P. 1803–1810.
159. Anthonisse J. The rush in a directed graph // Journal of Computational
Physics. 1971. Vol. 1. P. 1–10.
160. Granovetter M. The strength of weak ties // American Journal of Sociology. 1973. Vol. 78. P. 1360.
161. Regan E., Somera A., Mongru D., Oltvai Z. Hierarchical organization of
modularity in metabolic networks // Science. 2002. Vol. 297. P. 1551–1555.
162. Regan E., Barabasi A.-L. Hierarchical organization in complex networks // Physical Review E. 2003. Vol. 67. P. 026112.
163. Yook S.-H., Oltvai Z., Barabasi A.-L. Functional and topological characterization of protein interaction networks // Proteomics. 2004. Vol. 4. P. 928–942.
164. Garey M., Johnson D. Computers and Intractability : A Guide to the
Theory of NP-Completeness. New York : W. H. Freeman & Co., 1990. 338 p.
165. Feige U., Goldwasser S., L´aszl´o L., Safra S., Szegedy M. Approximating
Clique is Almost NP-Complete (Preliminary Version) // Proceedings 32nd Annual
Symposium of Foundations of Computer Science. San Juan : IEEE, 1991. P. 2–12.
166. H´astad J. Clique is hard to approximate within n
(1−ε)
// Acta Mathemat-
ica. 1996. Vol. 1. P. 627–636.
167. Gendreau M., Picard J.-C., Zubieta L. An Efficient Implicit Enumeration
Algorithm for the Maximum Clique Problem // Advances in Optimization and
Control / eds. H. A. Eiselt, G. Pederzoli. Berlin ; Heidelberg : Springer Berlin
Heidelberg, 1988. P. 79–91.
168. Wu Q., Hao J.-K. Solving the Winner Determination Problem via a
Weighted Maximum Clique Heuristic // Expert Systems with Applications. 2015.
Vol. 42, № 1. P. 355–365.
169. Eppstein D., L¨offler M., Strash D. Listing All Maximal Cliques in Large
Sparse Real-World Graphs // Journal of Experimental Algorithmics. 2013. Vol. 18.
P. 3.1:3.1–3.1:3.21.
170. 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. 2018. Vol. 33,
№ 1. P. 93–108.
171. Acemoglu D., Bimpikis K., Ozdaglar A. Dynamics of information exchange in endogenous social networks // Theoretical Economics. 2014. Vol. 9,
№ 1. P. 41–97.
172. Kremnyov O., Kalyagin V. Identification of Cliques and Independent Sets
in Pearson and Fechner Correlations Networks // Models, Algorithms and Technologies for Network Analysis / eds. V. A. Kalyagin, P. A. Koldanov, P. M. Pardalos.
Cham : Springer International Publishing, 2016. P. 165–173.
105
Приложение методов фрактального анализа, теории графов
173. Latyshev A., Koldanov P. Investigation of Connections Between Pearson and Fechner Correlations in Market Network : Experimental Study // Models, Algorithms and Technologies for Network Analysis / eds. V. A. Kalyagin,
P. A. Koldanov, P. M. Pardalos. Cham : Springer International Publishing, 2016.
P. 175–182.
174. Zhai J., Cao Y., Yao Y., Ding X., Li Y. Coarse and fine identification
of collusive clique in financial market // Expert Systems with Applications. 2017.
Vol. 69. P. 225–238.
175. Vizgunov A., Goldengorin B., Kalyagin V., Koldanov A., Koldanov P.,
Pardalos P. Network approach for the Russian stock market // Computational Management Science. 2014. Vol. 11, № 1. P. 45–55.
176. Kalyagin V., Koldanov P., Pardalos P., Zamaraev V. Measures of un-
certainty in market network analysis // Physica A : Statistical Mechanics and its
Applications. 2014. Vol. 413. P. 59–70.
177. Bron C., Kerbosch J. Algorithm 457 : Finding All Cliques of an Undirected Graph // Communications ACM. 1973. Vol. 16, № 9. P. 575–577.
178. Eppstein D., L¨offler M., Strash D. Listing All Maximal Cliques in Sparse
Graphs in Near-optimal Time // Lect. Notes Comput. Sci.. 2010. Vol. 6506. P. 1–14.
179. Abello J., Pardalos P., Resende M. On Maximum Clique Problems In
Very Large Graphs // External Memory Algorithms. American Mathematical Society. 1999. Vol. 1. P. 119–130.
180. Yang Y., Pierce T., Carbonell J. A Study of Retrospective and Online Event Detection // Proceedings of the 21st Annual International ACM SIGIR
Conference on Research and Development in Information Retrieval. New York,
USA : ACM, 1998. P. 28–36.
181. Kleinberg J. Bursty and Hierarchical Structure in Streams // Proceedings
of the Eighth ACM SIGKDD International Conference on Knowledge Discovery
and Data Mining. New York, USA : ACM, 2002. P. 91–101.
182. Sayyadi H., Hurst M., Maykov A. Event detection and tracking in social
streams // In Proceedings of the International Conference on Weblogs and Social
Media (ICWSM 2009). San Jose, California : AAAI, 2009. P. 311–314.
183. Becker H., Naaman M., Gravano L. Learning Similarity Metrics for
Event Identification in Social Media // Proceedings of the Third ACM International
Conference on Web Search and Data Mining. New York, USA : ACM, 2010.
P. 291–300.
184. Aggarwal C. Mining Text and Social Streams : A Review // ACM
SIGKDD Explorations Newsletter. 2014. Vol. 15, № 2. P. 9–19.
185. Aggarwal C., Subbian K. Event Detection in Social Streams // Proceedings of the 2012 SIAM International Conference on Data Mining. Anaheim,
California, USA : Disney’s Paradise Pier Hotel, 2012. P. 624–635.
186. Dong X., Mavroeidis D., Calabrese F., Frossard P. Multiscale Event
Detection in Social Media // Data Min. Knowl. Discov. 2015. Vol. 29, № 5.
P. 1374–1405.
106
Список литературы
187. Huang Y., Li Y., Shan J. Spatial-Temporal Event Detection from Geo-
Tagged Tweets // ISPRS International Journal of Geo-Information. 2018. Vol. 7,
№ 4. P. 1–21.
188. Sakaki T., Okazaki M., Matsuo Y. Tweet Analysis for Real-Time Event
Detection and Earthquake Reporting System Development // IEEE Transactions on
Knowledge and Data Engineering. 2013. Vol. 25, № 4. P. 919–931.
189. Kolchyna O., Souza T., Treleaven P., Aste T. A framework for Twit-
ter events detection, differentiation and its application for retail brands // Future
Technologies Conference (FTC). San Francisco : IEEE, 2016. P. 323–331.
190. Hasan M., Orgun M., Schwitter R. Real-time event detection from the
Twitter data stream using the TwitterNews+ Framework // Information Processing
& Management. 2019. Vol. 56, № 3. P. 1146–1165.
191. Atefeh F., Khreich W. A Survey of Techniques for Event Detection in
Twitter // Computational Intelligence. 2015. Vol. 31, № 1. P. 132–164.
192. Manning C., Raghavan P., Sch¨utze H. Introduction to Information Retrieval. New York, USA : Cambridge University Press, 2008. 544 p.
193. Sidorov S., Alexey F., Balash V. Fractality and Multifractality Analy-
sis of News Sentiments Time Series // IAENG International Journal of Applied
Mathematics. 2018. Vol. 48, № 1. P. 90–97.
Научное издание
Файзлиев Алексей Раисович,
Сидоров Сергей Петрович,
Балаш Владимир Алексеевич
ПРИЛОЖЕНИЕ МЕТОДОВ
ФРАКТАЛЬНОГО АНАЛИЗА,
ТЕОРИИ ГРАФОВ
И АНАЛИЗА СЛОЖНЫХ СЕТЕЙ
К ИССЛЕДОВАНИЮ
ВРЕМЕННЫХ РЯДОВ
Редактор Е. А. Малютина
Технический редактор Т. А. Трубникова
Подписано к использованию 25.12.2021. Формат 60 × 841/16.
Усл. печ. л. 6.3 (6.75). Объем данных 2 Мб.
Издательство Саратовского университета.
Корректор Т. К. Певная
Оригинал-макет подготовил И. А. Каргин
410012, Саратов, Астраханская, 83.