This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Ahmed, N.K., Neville, J., & Kompella, R. (2014). Network sampling: From static to streaming graphs. ACM Trans. Knowl. Discov. Data, 8(2), 1–56.AhmedN.K.NevilleJ.KompellaR.2014Network sampling: From static to streaming graphs8215610.1145/2601438Search in Google Scholar
Blagus, N., Šubelj, L., & Bajec, M. (2012). Self-similar scaling of density in complex real-world networks. Physica A, 391(8), 2794–2802.BlagusN.ŠubeljL.BajecM.2012Self-similar scaling of density in complex real-world networks39182794280210.1016/j.physa.2011.12.055Search in Google Scholar
Bu, Z., Wu, Z.A., Qian, L.Q., Cao, J., & Xu, G.D. (2014). A backbone extraction method with local search for complex weighted networks. In: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 85–88.BuZ.WuZ.A.QianL.Q.CaoJ.XuG.D.2014In:2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining858810.1109/ASONAM.2014.6921564Search in Google Scholar
Burt, R.S (1992). Structural holes: The social structure of competition. Harvard University Press.BurtR.S1992Harvard University Press10.4159/9780674029095Search in Google Scholar
Cao, J., Ding, C.L., & Shi, B.Y. (2019). Motif-based functional backbone extraction of complex networks. Physica A, 526, 121123.CaoJ.DingC.L.ShiB.Y.2019Motif-based functional backbone extraction of complex networks52612112310.1016/j.physa.2019.121123Search in Google Scholar
Carmi, S., Havlin, S., Kirkpatrick, S., Shavitt, Y., & Shir, E. (2007). A model of internet topology using k-shell decomposition. In: Proceedings of the National Academy of Sciences of the United States of America, 104(27), 11150–11154.CarmiS.HavlinS.KirkpatrickS.ShavittY.ShirE.2007A model of internet topology using k-shell decompositionIn:10427111501115410.1073/pnas.0701175104189613517586683Search in Google Scholar
Chen, D.B., Lü, L.Y., Shang, M.S., Zhang, Y.C., & Zhou, T. (2012). Identifying influential nodes in complex networks. Physica A, 391(4), 1777–1787.ChenD.B.LüL.Y.ShangM.S.ZhangY.C.ZhouT.2012Identifying influential nodes in complex networks39141777178710.1016/j.physa.2011.09.017Search in Google Scholar
Coscia, M., & Neffke, F. (2017). Network backboning with noisy data. In: 2017 IEEE 33rd International Conference on Data Engineering, 425–436.CosciaM.NeffkeF.2017In:2017 IEEE 33rd International Conference on Data Engineering42543610.1109/ICDE.2017.100Search in Google Scholar
Dai, L., Derudder, B., & Liu, X. (2018). Transport network backbone extraction: A comparison of techniques. Journal of Transport Geography, 69, 271–281.DaiL.DerudderB.LiuX.2018Transport network backbone extraction: A comparison of techniques6927128110.1016/j.jtrangeo.2018.05.012Search in Google Scholar
Foti, N.J., Hughes, J.M., & Rockmore, D.N. (2011). Nonparametric sparsification of complex multiscale networks. Plos One, 6(2), e16431.FotiN.J.HughesJ.M.RockmoreD.N.2011Nonparametric sparsification of complex multiscale networks62e1643110.1371/journal.pone.0016431303563321346815Search in Google Scholar
Ghalmane, Z., Cherifi, C., Cherifi, H., & Hassouni, M. (2020). A backbone extraction method for complex weighted networks, MARAMI.GhalmaneZ.CherifiC.CherifiH.HassouniM.2020MARAMISearch in Google Scholar
Grady, D., Thiemann, C., & Brockmann, D. (2011). Robust classification of salient links in complex networks. Nat Commun, 3(1), 199–202.GradyD.ThiemannC.BrockmannD.2011Robust classification of salient links in complex networks3119920210.1038/ncomms184722643891Search in Google Scholar
Granovetter, M. (1973). The Strength of Weak Ties. American Journal of Sociology, 78(6), 1360–1380.GranovetterM.1973The Strength of Weak Ties7861360138010.1086/225469Search in Google Scholar
Guan, J., Ren, J.Q., & Xing, L.Z. (2021). Measuring the nestedness of global production system based on bipartite network. In: The 9th International Conference on Complex Networks and Their Application, 547–558.GuanJ.RenJ.Q.XingL.Z.2021In:The 9th International Conference on Complex Networks and Their Application54755810.1007/978-3-030-65351-4_44Search in Google Scholar
Hennemann, S. (2013). Information-rich visualisation of dense geographical networks. Journal of Maps, 9, 68–75.HennemannS.2013Information-rich visualisation of dense geographical networks9687510.1080/17445647.2012.753850Search in Google Scholar
Hirsch, J.E. (2005). An index to quantify an individual's scientific research output. In: Proceedings of the National Academy of Sciences of the United States of America, 102, 16569–16572.HirschJ.E.2005An index to quantify an individual's scientific research outputIn:102165691657210.1073/pnas.0507655102128383216275915Search in Google Scholar
Itzkovitz, S., Levitt, R., Kashtan, N., Milo, R., Itzkovitz, M., & Alon, U. (2004). Coarse-graining and self-dissimilarity of complex networks. Physical Review E, 71, 016127.ItzkovitzS.LevittR.KashtanN.MiloR.ItzkovitzM.AlonU.2004Coarse-graining and self-dissimilarity of complex networks7101612710.1103/PhysRevE.71.01612715697678Search in Google Scholar
Kim, D.H., Noh, J.D., & Jeong, H. (2004). Scale-free trees: The skeletons of complex networks. Physical Review E, 70(4), 046126.KimD.H.NohJ.D.JeongH.2004Scale-free trees: The skeletons of complex networks70404612610.1103/PhysRevE.70.04612615600479Search in Google Scholar
Kitsak, M., Gallos, L.K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H.E., & Makse, H.A. (2010). Identification of influential spreaders in complex networks. Nature Phys., 6(11), 888–893.KitsakM.GallosL.K.HavlinS.LiljerosF.MuchnikL.StanleyH.E.MakseH.A.2010Identification of influential spreaders in complex networks61188889310.1038/nphys1746Search in Google Scholar
Li, Q., Zhou, T., Lü, L.Y., & Chen, D.B. (2014). Identifying influential spreaders by weighted LeaderRank. Physica A, 404, 47–55.LiQ.ZhouT.LüL.Y.ChenD.B.2014Identifying influential spreaders by weighted LeaderRank404475510.1016/j.physa.2014.02.041Search in Google Scholar
Lv, L.Y., Zhang, Y.C., Yeung, C.H., & Zhou, T. (2011). Leaders in social networks, the delicious case. Plos One, 6(6), e21202.LvL.Y.ZhangY.C.YeungC.H.ZhouT.2011Leaders in social networks, the delicious case66e2120210.1371/journal.pone.0021202312448521738620Search in Google Scholar
Malang, K., Wang, S.H., Phaphuangwittayakul, A., Lü, Y.Y., Yuan, H.N., & Zhang, X.Z. (2020). Identifying influential nodes of global terrorism network: A comparison for skeleton network extraction. Physica A, 545, 123769.MalangK.WangS.H.PhaphuangwittayakulA.LüY.Y.YuanH.N.ZhangX.Z.2020Identifying influential nodes of global terrorism network: A comparison for skeleton network extraction54512376910.1016/j.physa.2019.123769Search in Google Scholar
Marotta, L., Miccichè, S., Fujiwara, Y., Lyetomi, H., Aoyama, H., Gallegati, M., & Mantegna, R.N. (2015). Backbone of credit relationships in the Japanese credit market. EPJ Data Sci., 5(1), 1–14.MarottaL.MiccichèS.FujiwaraY.LyetomiH.AoyamaH.GallegatiM.MantegnaR.N.2015Backbone of credit relationships in the Japanese credit market5111410.2139/ssrn.2694005Search in Google Scholar
Nick, B., Lee, C., Cunningham, P., & Brandes, U. (2013). Simmelian backbones: Amplifying hidden homophily in facebook networks. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 525–532.NickB.LeeC.CunninghamP.BrandesU.2013In:Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining52553210.1145/2492517.2492569Search in Google Scholar
Radicchi, F., Ramasco, J.J., & Fortunato, S. (2011). Information filtering in complex weighted networks. Physical Review E, 83(4), 046101–046101.RadicchiF.RamascoJ.J.FortunatoS.2011Information filtering in complex weighted networks83404610104610110.1103/PhysRevE.83.04610121599234Search in Google Scholar
Serrano, M.A., Boguna, M., & Vespignani, A. (2009). Extracting the multiscale backbone of complex weighted networks. In: Proceedings of the National Academy of Sciences of the United States of America, 106, 6483–6488.SerranoM.A.BogunaM.VespignaniA.2009Extracting the multiscale backbone of complex weighted networksIn:1066483648810.1073/pnas.0808904106267249919357301Search in Google Scholar
Siganos, G., Tauro, S.L., & Faloutsos, M. (2006). Jellyfish: A conceptual model for the AS internet topology. Journal of Communications and Networks, 8(3), 339–350.SiganosG.TauroS.L.FaloutsosM.2006Jellyfish: A conceptual model for the AS internet topology8333935010.1109/JCN.2006.6182774Search in Google Scholar
Toivonen, H., Zhou, F., Hartikainen, A., & Hinkka, A. (2012). Network compression by node and edge mergers. In: M.R. Berthold (Eds.), Bisociative Knowledge Discovery, Springer, Berlin, Heidelberg, 199–217.ToivonenH.ZhouF.HartikainenA.HinkkaA.2012Network compression by node and edge mergersIn:BertholdM.R.(Eds.),SpringerBerlin, Heidelberg19921710.1007/978-3-642-31830-6_14Search in Google Scholar
Xing, L.Z., Dong, X.L., Guan, J., & Qiao, X.Y. (2019). Betweenness centrality for similarity-weight network and its application to measuring industrial sectors’ pivotability on the global value chain. Physica A, 516, 19–36.XingL.Z.DongX.L.GuanJ.QiaoX.Y.2019Betweenness centrality for similarity-weight network and its application to measuring industrial sectors’ pivotability on the global value chain516193610.1016/j.physa.2018.10.004Search in Google Scholar
Xing, L.Z., & Han, Y. (2021). Extracting the backbone of global value chain from high-dimensional inter-country input-output network. In: The 9th International Conference on Complex Networks and Their Application, 559–570.XingL.Z.HanY.2021In:The 9th International Conference on Complex Networks and Their Application55957010.1007/978-3-030-65351-4_45Search in Google Scholar
Zhang, R.J., Stanley, H.E., & Ye, F.Y. (2018). Extracting h-backbone as a core structure in weighted networks. Sci Rep, 8(1), 1–7.ZhangR.J.StanleyH.E.YeF.Y.2018Extracting h-backbone as a core structure in weighted networks811710.1038/s41598-018-32430-1615650430254200Search in Google Scholar
Zhang, X.H., Zhang, Z.C., Zhang, H., Wang, Q., & Zhu, J. (2014). Extracting the globally and locally adaptive backbone of complex networks. Plos One, 9(6), e100428.ZhangX.H.ZhangZ.C.ZhangH.WangQ.ZhuJ.2014Extracting the globally and locally adaptive backbone of complex networks96e10042810.1371/journal.pone.0100428406108424936975Search in Google Scholar
Zhang, X.H., & Zhu, J. (2013). Skeleton of weighted social network. Physica A, 392(6), 1547–1556.ZhangX.H.ZhuJ.2013Skeleton of weighted social network39261547155610.1016/j.physa.2012.12.001Search in Google Scholar
Zhao, S.X., Zhang, P.L., J, L., Tan, A.M., & Ye, F.Y. (2014). Abstracting the core subnet of weighted networks based on link strengths. Journal of the Association for Information Science and Technology, 65(5), 984–994.ZhaoS.X.ZhangP.L.LJ.TanA.M.YeF.Y.2014Abstracting the core subnet of weighted networks based on link strengths65598499410.1002/asi.23030Search in Google Scholar
Zhou, Y., Cheng, H., & Yu, J.X. (2009). Graph clustering based on structural/attribute similarities. In: Proceedings of the VLDB Endowment, 718–729.ZhouY.ChengH.YuJ.X.2009In:Proceedings of the VLDB Endowment71872910.14778/1687627.1687709Search in Google Scholar