American Institute of Mathematical Sciences

Advanced
March  2015, 10(1): 17-35. doi: 10.3934/nhm.2015.10.17

Link prediction in multiplex networks

Manisha Pujari 1, and  Rushed Kanawati 1,

1. 

SPC, UP13, LIPN, CNRS UMR 7030, 99 Av. J.B. Clément, 93430 Villetaneuse, France, France

Received  July 2014 Revised  December 2014 Published  February 2015

In this work we present a new approach for co-authorship link prediction based on leveraging information contained in general bibliographical multiplex networks. A multiplex network is a graph defined over a set of nodes linked by different types of relations. For instance, the multiplex network we are studying here is defined as follows : nodes represent authors and links can be one of the following types: co-authorship links, co-venue attending links and co-citing links. A supervised-machine learning based link prediction approach is applied. A link formation model is learned based on a set of topological attributes describing both positive and negative examples. While such an approach has been successfully applied in the context on simple networks, different options can be applied to extend it to multiplex networks. One option is to compute topological attributes in each layer of the multiplex. Another one is to compute directly new multiplex-based attributes quantifying the multiplex nature of dyads (potential links). These different approaches are studied and compared through experiments on real datasets extracted from the bibliographical database DBLP.
Keywords: link prediction, Complex network analysis, multiplex network, bibliographical networks., rank aggregation.
Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C3.
Citation: Manisha Pujari, Rushed Kanawati. Link prediction in multiplex networks. Networks & Heterogeneous Media, 2015, 10 (1) : 17-35. doi: 10.3934/nhm.2015.10.17
References:
[1]

L. Adamic and E. Adar, Friends and neighbors on the Web,, Social Networks, 25 (2003), 211.  doi: 10.1016/S0378-8733(03)00009-1.  Google Scholar

[2]

L. A. Adamic, O. Buyukkokten and E. Adar, A social network caught in the Web,, First Monday, 8 (2003), 1995.  doi: 10.5210/fm.v8i6.1057.  Google Scholar

[3]

C. C. Aggarwal, Y. Xie and P. S. Yu, A framework for dynamic link prediction in heterogeneous networks,, Statistical Analysis and Data Mining, 7 (2014), 14.  doi: 10.1002/sam.11198.  Google Scholar

[4]

J. A. Aslam and M. Montague, Models for metasearch,, in Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, (2001), 276.  doi: 10.1145/383952.384007.  Google Scholar

[5]

A.-L. Barabási and R. Albert, Emergence of scaling in random networks,, Science, 286 (1999), 509.  doi: 10.1126/science.286.5439.509.  Google Scholar

[6]

F. Battiston, V. Nicosia and V. Latora, Metrics for the analysis of multiplex networks,, , (2013).   Google Scholar

[7]

N. Benchettara, R. Kanawati and C. Rouveirol, Apprentissage supervisé pour la prédiction de nouveaux liens dans des réseaux sociaux bipartie,, in Actes da la 17iéme Rencontre de la société francophone de classification (SFC'2010), (2010), 63.   Google Scholar

[8]

M. Berlingerio, M. Coscia, F. Giannotti, A. Monreale and D. Pedreschi, Foundations of Multidimensional Network Analysis,, in Advances in Social Networks Analysis and Mining (ASONAM), (2011), 485.  doi: 10.1109/ASONAM.2011.103.  Google Scholar

[9]

M. Berlingerio, M. Coscia, F. Giannotti, A. Monreale and D. Pedreschi, Multidimensional networks: Foundations of structural analysis,, World Wide Web, 16 (2013), 567.  doi: 10.1007/s11280-012-0190-4.  Google Scholar

[10]

D. Black, R. Newing, I. McLean, A. McMillan and B. Monroe, The Theory of Committees and Elections by Duncan Black, and Revised Second Editions Committee Decisions with Complementary Valuation by Duncan Black,, 2nd edition, (1998).   Google Scholar

[11]

P. Brodka and P. Kazienko, Encyclopedia of Social Network Analysis and Mining,, chapter Multi-Layered Social Networks, (2014).   Google Scholar

[12]

P. Chebotarev and E. Shamis, The matrix-Forest theorem and measuring relations in small social groups,, Automation and Remote Control, 58 (1997), 1505.   Google Scholar

[13]

Y. Chevaleyre, U. Endriss, J. Lang and N. Maudet, A short introduction to computational social choice,, in SOFSEM 2007: Theory and Practice of Computer Science, (2007), 51.  doi: 10.1007/978-3-540-69507-3_4.  Google Scholar

[14]

D. A. Davis, R. Lichtenwalter and N. V. Chawla, Multi-relational link prediction in heterogeneous information networks,, in 2011 International Conference on Advances in Social Networks Analysis and Mining (ASONAM), (2011), 281.  doi: 10.1109/ASONAM.2011.107.  Google Scholar

[15]

J.-C. de Borda, Memoire sur les Elections au Scrutin,, 1781., ().   Google Scholar

[16]

Y. Dong, J. Tang, S. Wu, J. Tian, N. V. Chawla, J. Rao and H. Cao, Link prediction and recommendation across heterogeneous social networks,, in 2012 IEEE 12th International Conference on Data Mining (ICDM) (eds. M. J. Zaki, (2012), 181.  doi: 10.1109/ICDM.2012.140.  Google Scholar

[17]

C. Dwork, R.Kumar, M. Naor and D. Sivakumar, Rank aggregation methods for web,, in Proceedings of the 10th International Conference on World Wide Web, (2001), 613.  doi: 10.1145/371920.372165.  Google Scholar

[18]

C. Dwork, R. Kumar, M. Naor and D. Sivakumar, Rank aggregation, spam resistance, and social choice,, in WWW '01: Proceedings of 10th International Conference on World Wide Web, (2001), 613.   Google Scholar

[19]

F. Fouss, L. Yen, A. Pirotte and M. Saerens, An experimental investigation of graph kernels on a collaborative recommendation task,, in Sixth International Conference on Data Mining (ICDM'06), (2006), 863.  doi: 10.1109/ICDM.2006.18.  Google Scholar

[20]

S. Gao, L. Denoyer and P. Gallinari, Temporal link prediction by integrating content and structure information,, in Proceedings of the 20th ACM International Conference on Information and Knowledge Management - CIKM '11, (2011), 1169.  doi: 10.1145/2063576.2063744.  Google Scholar

[21]

M. A. Hasan, V. Chaoji, S. Salem and M. Zaki, Link prediction using supervised learning,, in Proc. of SDM 06 workshop on Link Analysis, (2006).   Google Scholar

[22]

Z. Huang, X. Li and H. Chen, Link prediction approach to collaborative filtering,, in Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries (eds. M. Marlino, (2005), 141.  doi: 10.1145/1065385.1065415.  Google Scholar

[23]

P. Jaccard, Étude comparative de la distribution florale dans une portion des alpes et des jura,, Bulletin de la Société Vaudoise des Sciences Naturelles, 37 (1901), 547.   Google Scholar

[24]

L. Katz, A new status index derived from socimetric analysis,, Psychmetrika, 18 (1953), 39.  doi: 10.1007/BF02289026.  Google Scholar

[25]

G. Kossinets, Effects of missing data in social networks,, Social Networks, 28 (2006), 247.  doi: 10.1016/j.socnet.2005.07.002.  Google Scholar

[26]

T.-T. Kuo, R. Yan, Y.-Y. Huang, P.-H. Kung and S.-D. Lin, Unsupervised link prediction using aggregative statistics on heterogeneous social networks,, in Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2013), 775.  doi: 10.1145/2487575.2487614.  Google Scholar

[27]

J. B. Lee and H. Adorna, Link prediction in a modified heterogeneous bibliographic network,, in ASONAM, (2012), 442.  doi: 10.1109/ASONAM.2012.78.  Google Scholar

[28]

D. Liben-Nowell and J. Kleinberg, The link prediction problem for social networks,, in Proceedings of the Twelfth International Conference on Information and Knowledge Management, (2003), 556.  doi: 10.1145/956863.956972.  Google Scholar

[29]

D. Liben-Nowell and J. M. Kleinberg, The link-prediction problem for social networks,, JASIST, 58 (2007), 1019.   Google Scholar

[30]

R. N. Lichtenwalter, J. T. Lussier and N. V. Chawla, New perspectives and methods in link prediction,, in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '10, (2010), 243.  doi: 10.1145/1835804.1835837.  Google Scholar

[31]

R. Lichtnwalter and N. Chawla, Link prediction: Fair and effective evaluation,, in 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), (2012), 376.  doi: 10.1109/ASONAM.2012.68.  Google Scholar

[32]

N. Littlestone and M. K. Warmuth, Weighted majority algorithm,, Information and Computation, 108 (1994), 212.  doi: 10.1006/inco.1994.1009.  Google Scholar

[33]

Y.-T. Liu, T.-Y. Liu, T. Qin, Z.-M. Ma and H. Li, Supervised rank aggregation,, in Proceedings of the 16th International Conference on World Wide Web, (2007), 481.  doi: 10.1145/1242572.1242638.  Google Scholar

[34]

L. Lü and T. Zhou, Link prediction in complex networks: A survey,, Physica A: Statistical Mechanics and its Applications, 390 (2011), 1150.  doi: 10.1016/j.physa.2010.11.027.  Google Scholar

[35]

A. K. Menon and C. Eklan, Link prediction via matrix factorization,, in Machine Learning and Knowledge Discovery in Databases (eds. D. Gunopulos, (6912), 437.  doi: 10.1007/978-3-642-23783-6_28.  Google Scholar

[36]

A. H. Mohammad and Z. M. J., A survey of link prediction in social networks,, in Social Network Data Analysis (ed. C. C. Aggarwal), (2010), 243.   Google Scholar

[37]

M. Montague and J. A. Aslam, Condorcet fusion for improved retrieval,, in Proceedings of the Eleventh International Conference on Information and Knowledge Management, (2002), 538.  doi: 10.1145/584879.584881.  Google Scholar

[38]

M. E. J. Newman, Coauthorship networks and patterns of scientific collaboration,, Proceedings of the National Academy of Science of the United States (PNAS), 101 (2004), 5200.  doi: 10.1073/pnas.0307545100.  Google Scholar

[39]

Q. Ou, Y. D. Jin, T. Zhou, B. H. Wang and B. Q. Yin, Power-law strength-degree correlation from resource-allocation dynamics on weighted networks,, Phys. Rev. E, 75 (2007).  doi: 10.1103/PhysRevE.75.021102.  Google Scholar

[40]

M. Pujari and R. Kanawati, Supervised rank aggregation approach for link prediction in complex networks,, in WWW (Companion Volume) (eds. A. Mille, (2012), 1189.  doi: 10.1145/2187980.2188260.  Google Scholar

[41]

K. Subbian and P. Melville, Supervised rank aggregation for predicting influence in networks,, in Proceedings of the IEEE Conference on Social Computing (SocialCom-2011), (2011).   Google Scholar

[42]

Y. Sun, R. Barber, M. Gupta, C. C. Aggarwa and J. Han, Co-Author Relationship Prediction in Heterogeneous Bibliographic Networks,, in Advances on Social Network Analysis and Mining (ASONAM), (2011), 121.  doi: 10.1109/ASONAM.2011.112.  Google Scholar

[43]

C. Wang, V. Satuluri and S. Parthasarathy, Local Probabilistic Models for Link Prediction,, in IEEE International Conference on Data Mining (ICDM) (eds. Y. Shi and C. W. Clifton), (2007), 322.  doi: 10.1109/ICDM.2007.108.  Google Scholar

[44]

H. Young and A. Levenglick, A consistent extension of condorcet's election principle,, SIAM Journal on Applied Mathematics, 35 (1978), 285.  doi: 10.1137/0135023.  Google Scholar

[45]

T. Zhou, L. Lu and Y.-C. Zhang, Predicting missing links via local information,, The European Physical Journal B, 71 (2009), 623.  doi: 10.1140/epjb/e2009-00335-8.  Google Scholar

show all references

References:
[1]

L. Adamic and E. Adar, Friends and neighbors on the Web,, Social Networks, 25 (2003), 211.  doi: 10.1016/S0378-8733(03)00009-1.  Google Scholar

[2]

L. A. Adamic, O. Buyukkokten and E. Adar, A social network caught in the Web,, First Monday, 8 (2003), 1995.  doi: 10.5210/fm.v8i6.1057.  Google Scholar

[3]

C. C. Aggarwal, Y. Xie and P. S. Yu, A framework for dynamic link prediction in heterogeneous networks,, Statistical Analysis and Data Mining, 7 (2014), 14.  doi: 10.1002/sam.11198.  Google Scholar

[4]

J. A. Aslam and M. Montague, Models for metasearch,, in Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, (2001), 276.  doi: 10.1145/383952.384007.  Google Scholar

[5]

A.-L. Barabási and R. Albert, Emergence of scaling in random networks,, Science, 286 (1999), 509.  doi: 10.1126/science.286.5439.509.  Google Scholar

[6]

F. Battiston, V. Nicosia and V. Latora, Metrics for the analysis of multiplex networks,, , (2013).   Google Scholar

[7]

N. Benchettara, R. Kanawati and C. Rouveirol, Apprentissage supervisé pour la prédiction de nouveaux liens dans des réseaux sociaux bipartie,, in Actes da la 17iéme Rencontre de la société francophone de classification (SFC'2010), (2010), 63.   Google Scholar

[8]

M. Berlingerio, M. Coscia, F. Giannotti, A. Monreale and D. Pedreschi, Foundations of Multidimensional Network Analysis,, in Advances in Social Networks Analysis and Mining (ASONAM), (2011), 485.  doi: 10.1109/ASONAM.2011.103.  Google Scholar

[9]

M. Berlingerio, M. Coscia, F. Giannotti, A. Monreale and D. Pedreschi, Multidimensional networks: Foundations of structural analysis,, World Wide Web, 16 (2013), 567.  doi: 10.1007/s11280-012-0190-4.  Google Scholar

[10]

D. Black, R. Newing, I. McLean, A. McMillan and B. Monroe, The Theory of Committees and Elections by Duncan Black, and Revised Second Editions Committee Decisions with Complementary Valuation by Duncan Black,, 2nd edition, (1998).   Google Scholar

[11]

P. Brodka and P. Kazienko, Encyclopedia of Social Network Analysis and Mining,, chapter Multi-Layered Social Networks, (2014).   Google Scholar

[12]

P. Chebotarev and E. Shamis, The matrix-Forest theorem and measuring relations in small social groups,, Automation and Remote Control, 58 (1997), 1505.   Google Scholar

[13]

Y. Chevaleyre, U. Endriss, J. Lang and N. Maudet, A short introduction to computational social choice,, in SOFSEM 2007: Theory and Practice of Computer Science, (2007), 51.  doi: 10.1007/978-3-540-69507-3_4.  Google Scholar

[14]

D. A. Davis, R. Lichtenwalter and N. V. Chawla, Multi-relational link prediction in heterogeneous information networks,, in 2011 International Conference on Advances in Social Networks Analysis and Mining (ASONAM), (2011), 281.  doi: 10.1109/ASONAM.2011.107.  Google Scholar

[15]

J.-C. de Borda, Memoire sur les Elections au Scrutin,, 1781., ().   Google Scholar

[16]

Y. Dong, J. Tang, S. Wu, J. Tian, N. V. Chawla, J. Rao and H. Cao, Link prediction and recommendation across heterogeneous social networks,, in 2012 IEEE 12th International Conference on Data Mining (ICDM) (eds. M. J. Zaki, (2012), 181.  doi: 10.1109/ICDM.2012.140.  Google Scholar

[17]

C. Dwork, R.Kumar, M. Naor and D. Sivakumar, Rank aggregation methods for web,, in Proceedings of the 10th International Conference on World Wide Web, (2001), 613.  doi: 10.1145/371920.372165.  Google Scholar

[18]

C. Dwork, R. Kumar, M. Naor and D. Sivakumar, Rank aggregation, spam resistance, and social choice,, in WWW '01: Proceedings of 10th International Conference on World Wide Web, (2001), 613.   Google Scholar

[19]

F. Fouss, L. Yen, A. Pirotte and M. Saerens, An experimental investigation of graph kernels on a collaborative recommendation task,, in Sixth International Conference on Data Mining (ICDM'06), (2006), 863.  doi: 10.1109/ICDM.2006.18.  Google Scholar

[20]

S. Gao, L. Denoyer and P. Gallinari, Temporal link prediction by integrating content and structure information,, in Proceedings of the 20th ACM International Conference on Information and Knowledge Management - CIKM '11, (2011), 1169.  doi: 10.1145/2063576.2063744.  Google Scholar

[21]

M. A. Hasan, V. Chaoji, S. Salem and M. Zaki, Link prediction using supervised learning,, in Proc. of SDM 06 workshop on Link Analysis, (2006).   Google Scholar

[22]

Z. Huang, X. Li and H. Chen, Link prediction approach to collaborative filtering,, in Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries (eds. M. Marlino, (2005), 141.  doi: 10.1145/1065385.1065415.  Google Scholar

[23]

P. Jaccard, Étude comparative de la distribution florale dans une portion des alpes et des jura,, Bulletin de la Société Vaudoise des Sciences Naturelles, 37 (1901), 547.   Google Scholar

[24]

L. Katz, A new status index derived from socimetric analysis,, Psychmetrika, 18 (1953), 39.  doi: 10.1007/BF02289026.  Google Scholar

[25]

G. Kossinets, Effects of missing data in social networks,, Social Networks, 28 (2006), 247.  doi: 10.1016/j.socnet.2005.07.002.  Google Scholar

[26]

T.-T. Kuo, R. Yan, Y.-Y. Huang, P.-H. Kung and S.-D. Lin, Unsupervised link prediction using aggregative statistics on heterogeneous social networks,, in Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2013), 775.  doi: 10.1145/2487575.2487614.  Google Scholar

[27]

J. B. Lee and H. Adorna, Link prediction in a modified heterogeneous bibliographic network,, in ASONAM, (2012), 442.  doi: 10.1109/ASONAM.2012.78.  Google Scholar

[28]

D. Liben-Nowell and J. Kleinberg, The link prediction problem for social networks,, in Proceedings of the Twelfth International Conference on Information and Knowledge Management, (2003), 556.  doi: 10.1145/956863.956972.  Google Scholar

[29]

D. Liben-Nowell and J. M. Kleinberg, The link-prediction problem for social networks,, JASIST, 58 (2007), 1019.   Google Scholar

[30]

R. N. Lichtenwalter, J. T. Lussier and N. V. Chawla, New perspectives and methods in link prediction,, in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '10, (2010), 243.  doi: 10.1145/1835804.1835837.  Google Scholar

[31]

R. Lichtnwalter and N. Chawla, Link prediction: Fair and effective evaluation,, in 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), (2012), 376.  doi: 10.1109/ASONAM.2012.68.  Google Scholar

[32]

N. Littlestone and M. K. Warmuth, Weighted majority algorithm,, Information and Computation, 108 (1994), 212.  doi: 10.1006/inco.1994.1009.  Google Scholar

[33]

Y.-T. Liu, T.-Y. Liu, T. Qin, Z.-M. Ma and H. Li, Supervised rank aggregation,, in Proceedings of the 16th International Conference on World Wide Web, (2007), 481.  doi: 10.1145/1242572.1242638.  Google Scholar

[34]

L. Lü and T. Zhou, Link prediction in complex networks: A survey,, Physica A: Statistical Mechanics and its Applications, 390 (2011), 1150.  doi: 10.1016/j.physa.2010.11.027.  Google Scholar

[35]

A. K. Menon and C. Eklan, Link prediction via matrix factorization,, in Machine Learning and Knowledge Discovery in Databases (eds. D. Gunopulos, (6912), 437.  doi: 10.1007/978-3-642-23783-6_28.  Google Scholar

[36]

A. H. Mohammad and Z. M. J., A survey of link prediction in social networks,, in Social Network Data Analysis (ed. C. C. Aggarwal), (2010), 243.   Google Scholar

[37]

M. Montague and J. A. Aslam, Condorcet fusion for improved retrieval,, in Proceedings of the Eleventh International Conference on Information and Knowledge Management, (2002), 538.  doi: 10.1145/584879.584881.  Google Scholar

[38]

M. E. J. Newman, Coauthorship networks and patterns of scientific collaboration,, Proceedings of the National Academy of Science of the United States (PNAS), 101 (2004), 5200.  doi: 10.1073/pnas.0307545100.  Google Scholar

[39]

Q. Ou, Y. D. Jin, T. Zhou, B. H. Wang and B. Q. Yin, Power-law strength-degree correlation from resource-allocation dynamics on weighted networks,, Phys. Rev. E, 75 (2007).  doi: 10.1103/PhysRevE.75.021102.  Google Scholar

[40]

M. Pujari and R. Kanawati, Supervised rank aggregation approach for link prediction in complex networks,, in WWW (Companion Volume) (eds. A. Mille, (2012), 1189.  doi: 10.1145/2187980.2188260.  Google Scholar

[41]

K. Subbian and P. Melville, Supervised rank aggregation for predicting influence in networks,, in Proceedings of the IEEE Conference on Social Computing (SocialCom-2011), (2011).   Google Scholar

[42]

Y. Sun, R. Barber, M. Gupta, C. C. Aggarwa and J. Han, Co-Author Relationship Prediction in Heterogeneous Bibliographic Networks,, in Advances on Social Network Analysis and Mining (ASONAM), (2011), 121.  doi: 10.1109/ASONAM.2011.112.  Google Scholar

[43]

C. Wang, V. Satuluri and S. Parthasarathy, Local Probabilistic Models for Link Prediction,, in IEEE International Conference on Data Mining (ICDM) (eds. Y. Shi and C. W. Clifton), (2007), 322.  doi: 10.1109/ICDM.2007.108.  Google Scholar

[44]

H. Young and A. Levenglick, A consistent extension of condorcet's election principle,, SIAM Journal on Applied Mathematics, 35 (1978), 285.  doi: 10.1137/0135023.  Google Scholar

[45]

T. Zhou, L. Lu and Y.-C. Zhang, Predicting missing links via local information,, The European Physical Journal B, 71 (2009), 623.  doi: 10.1140/epjb/e2009-00335-8.  Google Scholar

[1]

Qiang Fu, Yanlong Zhang, Yushu Zhu, Ting Li. Network centralities, demographic disparities, and voluntary participation. Mathematical Foundations of Computing, 2020, 3 (4) : 249-262. doi: 10.3934/mfc.2020011
[2]

Shipra Singh, Aviv Gibali, Xiaolong Qin. Cooperation in traffic network problems via evolutionary split variational inequalities. Journal of Industrial & Management Optimization, 2020  doi: 10.3934/jimo.2020170
[3]

Yicheng Liu, Yipeng Chen, Jun Wu, Xiao Wang. Periodic consensus in network systems with general distributed processing delays. Networks & Heterogeneous Media, 2020  doi: 10.3934/nhm.2021002
[4]

Rajendra K C Khatri, Brendan J Caseria, Yifei Lou, Guanghua Xiao, Yan Cao. Automatic extraction of cell nuclei using dilated convolutional network. Inverse Problems & Imaging, 2021, 15 (1) : 27-40. doi: 10.3934/ipi.2020049
[5]

Editorial Office. Retraction: Honggang Yu, An efficient face recognition algorithm using the improved convolutional neural network. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 901-901. doi: 10.3934/dcdss.2019060
[6]

Yu-Jhe Huang, Zhong-Fu Huang, Jonq Juang, Yu-Hao Liang. Flocking of non-identical Cucker-Smale models on general coupling network. Discrete & Continuous Dynamical Systems - B, 2021, 26 (2) : 1111-1127. doi: 10.3934/dcdsb.2020155
[7]

Ningyu Sha, Lei Shi, Ming Yan. Fast algorithms for robust principal component analysis with an upper bound on the rank. Inverse Problems & Imaging, 2021, 15 (1) : 109-128. doi: 10.3934/ipi.2020067
[8]

Editorial Office. Retraction: Xiao-Qian Jiang and Lun-Chuan Zhang, Stock price fluctuation prediction method based on time series analysis. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 915-915. doi: 10.3934/dcdss.2019061
[9]

Guillaume Cantin, M. A. Aziz-Alaoui. Dimension estimate of attractors for complex networks of reaction-diffusion systems applied to an ecological model. Communications on Pure & Applied Analysis, 2021, 20 (2) : 623-650. doi: 10.3934/cpaa.2020283
[10]

Ziang Long, Penghang Yin, Jack Xin. Global convergence and geometric characterization of slow to fast weight evolution in neural network training for classifying linearly non-separable data. Inverse Problems & Imaging, 2021, 15 (1) : 41-62. doi: 10.3934/ipi.2020077
[11]

Xiaoxian Tang, Jie Wang. Bistability of sequestration networks. Discrete & Continuous Dynamical Systems - B, 2021, 26 (3) : 1337-1357. doi: 10.3934/dcdsb.2020165
[12]

Mingchao Zhao, You-Wei Wen, Michael Ng, Hongwei Li. A nonlocal low rank model for poisson noise removal. Inverse Problems & Imaging, , () : -. doi: 10.3934/ipi.2021003
[13]

Hua Shi, Xiang Zhang, Yuyan Zhang. Complex planar Hamiltonian systems: Linearization and dynamics. Discrete & Continuous Dynamical Systems - A, 2020  doi: 10.3934/dcds.2020406
[14]

D. R. Michiel Renger, Johannes Zimmer. Orthogonality of fluxes in general nonlinear reaction networks. Discrete & Continuous Dynamical Systems - S, 2021, 14 (1) : 205-217. doi: 10.3934/dcdss.2020346
[15]

Bernold Fiedler. Global Hopf bifurcation in networks with fast feedback cycles. Discrete & Continuous Dynamical Systems - S, 2021, 14 (1) : 177-203. doi: 10.3934/dcdss.2020344
[16]

Lars Grüne. Computing Lyapunov functions using deep neural networks. Journal of Computational Dynamics, 2020  doi: 10.3934/jcd.2021006
[17]

Pedro Aceves-Sanchez, Benjamin Aymard, Diane Peurichard, Pol Kennel, Anne Lorsignol, Franck Plouraboué, Louis Casteilla, Pierre Degond. A new model for the emergence of blood capillary networks. Networks & Heterogeneous Media, 2020  doi: 10.3934/nhm.2021001
[18]

Leslaw Skrzypek, Yuncheng You. Feedback synchronization of FHN cellular neural networks. Discrete & Continuous Dynamical Systems - B, 2020  doi: 10.3934/dcdsb.2021001
[19]

Meng Ding, Ting-Zhu Huang, Xi-Le Zhao, Michael K. Ng, Tian-Hui Ma. Tensor train rank minimization with nonlocal self-similarity for tensor completion. Inverse Problems & Imaging, , () : -. doi: 10.3934/ipi.2021001
[20]

Weisong Dong, Chang Li. Second order estimates for complex Hessian equations on Hermitian manifolds. Discrete & Continuous Dynamical Systems - A, 2020  doi: 10.3934/dcds.2020377

2019 Impact Factor: 1.053

Tools

Metrics

  • PDF downloads (393)
  • HTML views (0)
  • Cited by (0)

Other articles
by authors

[Back to Top]

Copyright © 2021 American Institute of Mathematical Sciences