SAN 阅读笔记
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书末单元精校翻译:参考文献

参考文献(References)

译层说明:以下 289 条按原书字母序逐条保留。作者、年份、题名、期刊或会议、卷期、页码、ISBN 与 URL 均以原书为准;本层只对 PDF/OCR 文本层中的断行、控制字节和明显引号残渣作可追溯的标点/字符整理,不新增书目或 DOI。
  • Abbe, E. 2018. “Community detection and stochastic block models”. Foundations and Trends in Communications and Information Theory. 14(1–2), 1–162.
  • Abbe, E., A. S. Bandeira, and G. Hall. 2015. “Exact recovery in the stochastic block model”. IEEE Transactions on Information Theory. 62(1): 471–487.
  • Abbe, E., J. Fan, K. Wang, Y. Zhong, et al. 2020. “Entrywise eigenvector analysis of random matrices with low expected rank”. Annals of Statistics. 48(3): 1452–1474.
  • Adamic, L. A. and N. Glance. 2005. “The political blogosphere and the 2004 US election: Divided they blog”. In: Proceedings of the 3rd International Workshop on Link Discovery. 36–43.
  • Agirre, E. and A. Soroa. 2009. “Personalizing pagerank for word sense disambiguation”. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009). 33–41.
  • Akgün, M. K. and M. K. Tural. 2020. “k-step betweenness centrality”. Computational and Mathematical Organization Theory. 26(1): 55–87.
  • Albert, R., H. Jeong, and A.-L. Barabási. 2000. “Error and attack tolerance of complex networks”. Nature. 406(6794): 378–382.
  • Aldous, D. and J. Fill. 2002. Reversible Markov Chains and Random Walks on Graphs. Berkeley. URL: http://www.stat.berkeley.edu/~aldous/RWG/book.html.
  • Altman, A. and M. Tennenholtz. 2005. “Ranking systems: The PageRank axioms”. In: Proceedings of the 6th ACM Conference on Electronic Commerce. 1–8.
  • Amini, A. A., E. Levina, et al. 2018. “On semidefinite relaxations for the block model”. Annals of Statistics. 46(1): 149–179.
  • Andersen, R., F. Chung, and K. Lang. 2006. “Local graph partitioning using pagerank vectors”. In: 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS’06). IEEE. 475–486.
  • Arenas, A., A. Fernandez, and S. Gomez. 2008. “Analysis of the structure of complex networks at different resolution levels”. New Journal of Physics. 10(5): 053039.
  • Avrachenkov, K. E., J. A. Filar, and P. G. Howlett. 2013a. Analytic Perturbation Theory and its Applications. SIAM.
  • Avrachenkov, K. E., A. Y. Kondratev, V. V. Mazalov, and D. G. Rubanov. 2018a. “Network partitioning algorithms as cooperative games”. Computational Social Networks. 5(1): 1–28.
  • Avrachenkov, K. E., V. V. Mazalov, and B. T. Tsynguev. 2015. “Beta current flow centrality for weighted networks”. In: International Conference on Computational Social Networks. Springer. 216–227.
  • Avrachenkov, K., A. Bobu, and M. Dreveton. 2021a. “Higher-order spectral clustering for geometric graphs”. Journal of Fourier Analysis and Applications. 27(2): 1–29.
  • Avrachenkov, K., V. S. Borkar, A. Kadavankandy, and J. K. Sreedharan. 2018b. “Revisiting random walk based sampling in networks: Evasion of burn-in period and frequent regenerations”. Computational Social Networks. 5(1): 1–19.
  • Avrachenkov, K., V. S. Borkar, and K. Saboo. 2016a. “Distributed and asynchronous methods for semi-supervised learning”. In: International Workshop on Algorithms and Models for the Web-Graph. 34–46.
  • Avrachenkov, K., P. Chebotarev, and D. Rubanov. 2019. “Similarities on graphs: Kernels versus proximity measures”. European Journal of Combinatorics. 80: 47–56.
  • Avrachenkov, K., V. Dobrynin, D. Nemirovsky, S. K. Pham, and E. Smirnova. 2008a. “Pagerank based clustering of hypertext document collections”. In: Proceedings of the 31st ACM SIGIR. 873–874.
  • Avrachenkov, K. and M. Dreveton. 2019. “Almost exact recovery in label spreading”. In: International Workshop on Algorithms and Models for the Web-Graph. 30–43.
  • Avrachenkov, K. and M. Dreveton. 2020. “Almost exact recovery in noisy semi-supervised learning”. arXiv preprint arXiv:2007.14717.
  • Avrachenkov, K., M. Dreveton, and L. Leskelä. 2021b. “Recovering communities in temporal networks using persistent edges”. In: International Conference on Computational Data and Social Networks. Springer. 243–254.
  • Avrachenkov, K., R. v. d. Hofstad, and M. Sokol. 2014a. “Personalized pagerank with node-dependent restart”. In: International Workshop on Algorithms and Models for the Web-Graph. 23–33.
  • Avrachenkov, K., A. Kadavankandy, and N. Litvak. 2018c. “Mean field analysis of personalized PageRank with implications for local graph clustering”. Journal of Statistical Physics. 173(3–4): 895–916.
  • Avrachenkov, K., L. Leskelä, and M. Dreveton. 2022. “Community recovery in non-binary and temporal stochastic block models”. arXiv preprint arXiv:2008.04790.
  • Avrachenkov, K., N. Litvak, V. Medyanikov, and M. Sokol. 2013b. “Alpha current flow betweenness centrality”. In: International Workshop on Algorithms and Models for the Web-Graph. Springer. 106–117.
  • Avrachenkov, K., N. Litvak, D. Nemirovsky, E. Smirnova, and M. Sokol. 2011. “Quick detection of top-k personalized pagerank lists”. In: International Workshop on Algorithms and Models for the Web-Graph. Springer. 50–61.
  • Avrachenkov, K., N. Litvak, and K. S. Pham. 2008b. “A singular perturbation approach for choosing the PageRank damping factor”. Internet Mathematics. 5(1–2): 47–69.
  • Avrachenkov, K., N. Litvak, L. O. Prokhorenkova, and E. Suyargulova. 2014b. “Quick detection of high-degree entities in large directed networks”. In: 2014 IEEE International Conference on Data Mining. IEEE. 20–29.
  • Avrachenkov, K., N. Litvak, M. Sokol, and D. Towsley. 2014c. “Quick detection of nodes with large degrees”. Internet Mathematics. 10(1–2): 1–19.
  • Avrachenkov, K., A. Mishenin, P. Gonçalves, and M. Sokol. 2012. “Generalized optimization framework for graph-based semi-supervised learning”. In: Proceedings of the 2012 SIAM International Conference on Data Mining. SIAM. 966–974.
  • Avrachenkov, K., G. Neglia, and A. Tuholukova. 2016b. “Subsampling for chain-referral methods”. In: International Conference on Analytical and Stochastic Modeling Techniques and Applications. Springer. 17–31.
  • Avrachenkov, K., A. Piunovskiy, and Y. Zhang. 2018d. “Hitting times in Markov chains with restart and their application to network centrality”. Methodology and Computing in Applied Probability. 20(4): 1173–1188.
  • Avrachenkov, K., B. Ribeiro, and J. K. Sreedharan. 2016c. “Inference in OSNs via lightweight partial crawls”. Proceedings of ACM SIGMETRICS. 44(1): 165–177.
  • Avrachenkov, K., B. Ribeiro, and D. Towsley. 2010. “Improving random walk estimation accuracy with uniform restarts”. In: International Workshop on Algorithms and Models for the Web-Graph (WAW). Springer. 98–109.
  • Barabási, A.-L. 2016. Network Science. Cambridge University Press.
  • Barabási, A.-L. and R. Albert. 1999. “Emergence of scaling in random networks”. Science. 286(5439): 509–512.
  • Barucca, P., F. Lillo, P. Mazzarisi, and D. Tantari. 2018. “Disentangling group and link persistence in dynamic stochastic block models”. Journal of Statistical Mechanics: Theory and Experiment. 2018(12): 123407.
  • Bastian, M., S. Heymann, and M. Jacomy. 2009. “Gephi: An open source software for exploring and manipulating networks”. In: Proceedings of the International AAAI Conference on Web and Social Media, Vol. 3. No. 1. 361–362.
  • Batagelj, V. and U. Brandes. 2005. “Efficient generation of large random networks”. Physical Review E. 71(3): 036113.
  • Bavelas, A. 1950. “Communication patterns in task-oriented groups”. Journal of the Acoustical Society of America. 22(6): 725–730.
  • Belkin, M. and P. Niyogi. 2002. “Using manifold structure for partially labelled classification”. In: Proceedings of the 15th International Conference on Neural Information Processing Systems. Cambridge, MA, USA: MIT Press. 953–960.
  • Ben-David, S., T. Lu, and D. Pál. 2008. “Does unlabeled data provably help? Worst-case analysis of the sample complexity of semi-supervised learning”. In: Proceedings of Conference on Learning Theory.
  • Bergstrom, C. 2007. “Eigenfactor: Measuring the value and prestige of scholarly journals”. College & Research Libraries News. 68(5): 314–316.
  • Bergstrom, C. T., J. D. West, and M. A. Wiseman. 2008. “The EigenfactorTM metrics”. Journal of Neuroscience. 28(45): 11433–11434.
  • Bhattacharyya, S. and S. Chatterjee. 2020. “General community detection with optimal recovery conditions for multi-relational sparse networks with dependent layers”. arXiv preprint arXiv:2004.03480.
  • Bickel, P. J. and P. Sarkar. 2016. “Hypothesis testing for automated community detection in networks”. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 78(1): 253–273.
  • Billingsley, P. 1961. “Statistical methods in Markov chains”. Annals of Mathematical Statistics. 32(1): 12–40.
  • Blondel, V. D., J.-L. Guillaume, R. Lambiotte, and E. Lefebvre. 2008. “Fast unfolding of communities in large networks”. Journal of Statistical Mechanics: Theory and Experiment. 2008(10): P10008.
  • Bojchevski, A., J. Klicpera, B. Perozzi, A. Kapoor, M. Blais, B. Rózemberczki, M. Lukasik, and S. Günnemann. 2020. “Scaling Graph Neural Networks with approximate PageRank”. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2464–2473.
  • Boldi, P., F. Bonchi, C. Castillo, D. Donato, A. Gionis, and S. Vigna. 2008. “The query-flow graph: model and applications”. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management. 609–618.
  • Boldi, P. and S. Vigna. 2014. “Axioms for centrality”. Internet Mathematics. 10(3–4): 222–262.
  • Bollen, J., M. A. Rodriquez, and H. Van de Sompel. 2006. “Journal status”. Scientometrics. 69(3): 669–687.
  • Bollobás, B. 2001. Random Graphs. No. 73. Cambridge University Press.
  • Bollobás, B., O. Riordan, J. Spencer, and G. Tusnády. 2001. “The degree sequence of a scale-free random graph process”. Random Structures & Algorithms. 18(3): 279–290.
  • Bonacich, P. 1987. “Power and centrality: A family of measures”. American Journal of Sociology. 92(5): 1170–1182.
  • Bonacich, P. and P. Lloyd. 2001. “Eigenvector-like measures of centrality for asymmetric relations”. Social Networks. 23(3): 191–201.
  • Borassi, M. and E. Natale. 2019. “KADABRA is an adaptive algorithm for betweenness via random approximation”. Journal of Experimental Algorithmics (JEA). 24: 1–35.
  • Borgatti, S. P. 2005. “Centrality and network flow”. Social Networks. 27(1): 55–71.
  • Borgatti, S. P., M. G. Everett, and J. C. Johnson. 2018. Analyzing Social Networks. 2nd ed. SAGE.
  • Brandes, U. 2008. “On variants of shortest-path betweenness centrality and their generic computation”. Social Networks. 30(2): 136–145.
  • Brandes, U., D. Delling, M. Gaertler, R. Gorke, M. Hoefer, Z. Nikoloski, and D. Wagner. 2007. “On modularity clustering”. IEEE Transactions on Knowledge and Data Engineering. 20(2): 172–188.
  • Brandes, U. and D. Fleischer. 2005. “Centrality measures based on current flow”. In: Annual Symposium on Theoretical Aspects of Computer Science. Springer. 533–544.
  • Brauer, A. 1952. “Limits for the characteristic roots of a matrix. IV: Applications to stochastic matrices”. Duke Mathematical Journal. 19(1): 75–91.
  • Brémaud, P. 1999. Markov chains: Gibbs fields, Monte Carlo simulation, and queues, Vol. 31. Springer.
  • Brin, S. and L. Page. 1998. “The anatomy of a large-scale hypertextual web search engine”. Computer Networks and ISDN Systems. 30(1–7): 107–117.
  • Broido, A. D. and A. Clauset. 2019. “Scale-free networks are rare”. Nature Communications. 10(1): 1–10.
  • Calder, J., B. Cook, M. Thorpe, and D. Slepcev. 2020. “Poisson learning: Graph based semi-supervised learning at very low label rates”. In: Proceedings of International Conference on Machine Learning (ICML 2020). PMLR. 1306–1316.
  • Callaghan, T., P. J. Mucha, and M. A. Porter. 2007. “Random walker ranking for NCAA division IA football”. The American Mathematical Monthly. 114(9): 761–777.
  • Carley, K. M. and D. Skillicorn. 2005. “Special issue on analyzing large scale networks: The Enron corpus”. Computational & Mathematical Organization Theory. 11(3): 179–181.
  • Carrington, P. J., J. Scott, and S. Wasserman. 2005. Models and Methods in Social Network Analysis. Cambridge University Press.
  • Chandra, A. K., P. Raghavan, W. L. Ruzzo, R. Smolensky, and P. Tiwari. 1996. “The electrical resistance of a graph captures its commute and cover times”. Computational Complexity. 6(4): 312–340.
  • Chapelle, O., B. Schölkopf, and A. Zien. 2006. Semi-Supervised Learning. Adaptive Computation and Machine Learning. MIT Press.
  • Chen, M., Z. Wei, B. Ding, Y. Li, Y. Yuan, X. Du, and J.-R. Wen. 2020. “Scalable graph neural networks via bidirectional propagation”. Advances in Neural Information Processing Systems. 33: 14556–14566.
  • Chen, P., H. Xie, S. Maslov, and S. Redner. 2007. “Finding scientific gems with Google’s PageRank algorithm”. Journal of Informetrics. 1(1): 8–15.
  • Chen, Y., Y. Chi, J. Fan, C. Ma, et al. 2021. “Spectral methods for data science: A statistical perspective”. Foundations and Trends in Machine Learning. 14(5): 566–806.
  • Cherven, K. 2015. Mastering Gephi Network Visualization. Packt Publishing.
  • Chien, E., J. Peng, P. Li, and O. Milenkovic. 2020. “Adaptive universal generalized PageRank graph neural network”. arXiv preprint arXiv:2006.07988.
  • Chung, F. 2007. “The heat kernel as the pagerank of a graph”. Proceedings of the National Academy of Sciences. 104(50): 19735–19740.
  • Chung, F. and L. Lu. 2006. Complex Graphs and Networks (CBMS Regional Conference Series in Mathematics). Boston, MA, USA: American Mathematical Society. ISBN: 0821836579.
  • Clauset, A., M. E. Newman, and C. Moore. 2004. “Finding community structure in very large networks”. Physical Review E. 70(6): 066111.
  • Clauset, A., C. R. Shalizi, and M. E. Newman. 2009. “Power-law distributions in empirical data”. SIAM Review. 51(4): 661–703.
  • Clemente, G. P. and A. Cornaro. 2020. “A novel measure of edge and vertex centrality for assessing robustness in complex networks”. Soft Computing. 24(18): 13687–13704.
  • Cohen, E. and H. Kaplan. 2007. “Spatially-decaying aggregation over a network”. Journal of Computer and System Sciences. 73(3): 265–288.
  • Cooper, C., T. Radzik, and Y. Siantos. 2016. “Fast low-cost estimation of network properties using random walks”. Internet Mathematics. 12(4): 221–238.
  • Cozman, F. G., I. Cohen, and M. Cirelo. 2002. “Unlabeled data can degrade classification performance of generative classifiers”. In: Proceedings of Flairs-02. 327–331.
  • Dasgupta, A., R. Kumar, and D. Sivakumar. 2012. “Social sampling”. In: Proceedings of the 18th ACM SIGKDD. 235–243.
  • Davoodi, E., K. Kianmehr, and M. Afsharchi. 2013. “A semantic social network-based expert recommender system”. Applied Intelligence. 39(1): 1–13.
  • De Nooy, W., A. Mrvar, and V. Batagelj. 2018. Exploratory Social Network Analysis with Pajek: Revised and expanded edition for updated software. 3rd ed. Cambridge University Press.
  • Decelle, A., F. Krzakala, C. Moore, and L. Zdeborová. 2011. “Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications”. Physical Review E. 84(6): 066106.
  • Defferrard, M., X. Bresson, and P. Vandergheynst. 2016. “Convolutional neural networks on graphs with fast localized spectral filtering”. Advances in Neural Information Processing Systems. 29.
  • Dekker, A. 2005. “Conceptual distance in social network analysis”. Journal of Social Structure. 6(3): 31.
  • Demmel, J. W., O. A. Marques, B. N. Parlett, and C. Vömel. 2008. “Performance and accuracy of LAPACK’s symmetric tridiagonal eigensolvers”. SIAM Journal on Scientific Computing. 30(3): 1508–1526.
  • Dempster, A. P., N. M. Laird, and D. B. Rubin. 1977. “Maximum likelihood from incomplete data via the EM algorithm”. Journal of the Royal Statistical Society: Series B (Methodological). 39(1): 1–22.
  • Dhara, S., J. Gaudio, E. Mossel, and C. Sandon. 2022. “Spectral recovery of binary censored block models”. In: Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA). SIAM. 3389–3416.
  • Ding, Y., E. Yan, A. Frazho, and J. Caverlee. 2009. “PageRank for ranking authors in co-citation networks”. Journal of the American Society for Information Science and Technology. 60(11): 2229–2243.
  • Dodds, P. S., R. Muhamad, and D. J. Watts. 2003. “An experimental study of search in global social networks”. Science. 301(5634): 827–829.
  • Doreian, P., V. Batagelj, and A. Ferligoj. 2005. Generalized Blockmodeling. Cambridge University Press.
  • Draief, M. and L. Massoulié. 2010. Epidemics and Rumours in Complex Networks. Cambridge University Press.
  • Durrett, R. 2007. Random Graph Dynamics, Vol. 200. Cambridge University Press.
  • Ellens, W., F. M. Spieksma, P. Van Mieghem, A. Jamakovic, and R. E. Kooij. 2011. “Effective graph resistance”. Linear Algebra and its Applications. 435(10): 2491–2506.
  • Ellson, J., E. R. Gansner, E. Koutsofios, S. C. North, and G. Woodhull. 2004. “Graphviz and dynagraph–static and dynamic graph drawing tools”. In: Graph Drawing Software. Springer. 127–148.
  • Erdős, P. and A. Rényi. 1959. “On random graphs”. Publicationes Mathematicae, Debrecen. 6: 290–297.
  • Erickson, B. H. 1979. “Some problems of inference from chain data”. Sociological Methodology. 10: 276–302.
  • Estrada, E. and N. Hatano. 2008. “Communicability in complex networks”. Physical Review E. 77(3): 036111.
  • Estrada, E. and J. A. Rodriguez-Velazquez. 2005. “Subgraph centrality in complex networks”. Physical Review E. 71(5): 056103.
  • Everett, M. G. and S. P. Borgatti. 1999. “The centrality of groups and classes”. Journal of Mathematical Sociology. 23(3): 181–201.
  • Fan, C., L. Zeng, Y. Ding, M. Chen, Y. Sun, and Z. Liu. 2019. “Learning to identify high betweenness centrality nodes from scratch: A novel graph neural network approach”. In: Proceedings of the 28th ACM CIKM’19. 559–568.
  • Fei, Y. and Y. Chen. 2019. “Achieving the bayes error rate in stochastic block model by sdp, robustly”. In: Conference on Learning Theory. PMLR. 1235–1269.
  • Feige, U. and E. Ofek. 2005. “Spectral techniques applied to sparse random graphs”. Random Structures & Algorithms. 27(2): 251–275.
  • Fiala, D. 2012. “Time-aware PageRank for bibliographic networks”. Journal of Informetrics. 6(3): 370–388.
  • Fiala, D., F. Rousselot, and K. Ježek. 2008. “PageRank for bibliographic networks”. Scientometrics. 76(1): 135–158.
  • Fortunato, S. 2010. “Community detection in graphs”. Physics Reports. 486(3–5): 75–174.
  • Fortunato, S. and M. Barthelemy. 2007. “Resolution limit in community detection”. Proceedings of the National Academy of Sciences. 104(1): 36–41.
  • Fournet, J. and A. Barrat. 2014. “Contact patterns among high school students”. PLOS ONE. 9(9): 1–17.
  • Fouss, F., K. Francoisse, L. Yen, A. Pirotte, and M. Saerens. 2012. “An experimental investigation of kernels on graphs for collaborative recommendation and semisupervised classification”. Neural Networks. 31: 53–72.
  • Fouss, F., A. Pirotte, J.-M. Renders, and M. Saerens. 2007. “Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation”. IEEE Transactions on knowledge and data engineering. 19(3): 355–369.
  • Freeman, L. C. 1977. “A set of measures of centrality based on betweenness”. Sociometry 35–41.
  • Freeman, L. C., S. P. Borgatti, and D. R. White. 1991. “Centrality in valued graphs: A measure of betweenness based on network flow”. Social networks. 13(2): 141–154.
  • Friedkin, N. E. 1991. “Theoretical foundations for centrality measures”. American Journal of Sociology. 96(6): 1478–1504.
  • Galhotra, S., A. Mazumdar, S. Pal, and B. Saha. 2018. “The geometric block model”. In: Thirty-Second AAAI Conference on Artificial Intelligence.
  • Gander, W., G. H. Golub, and U. Von Matt. 1989. “A constrained eigenvalue problem”. Linear Algebra and its Applications. 114: 815–839.
  • Garey, M. R., D. S. Johnson, and L. Stockmeyer. 1974. “Some simplified NP-complete problems”. In: Proceedings of the 6-th ACM Symposium on Theory of Computing. ACM. 47–63.
  • Gauvin, W., B. Ribeiro, D. Towsley, B. Liu, and J. Wang. 2010. “Measurement and gender-specific analysis of user publishing characteristics on myspace”. IEEE Network. 24(5): 38–43.
  • Getoor, L. 2005. “Link-based classification”. In: Advanced Methods for Knowledge Discovery from Complex Data. Springer, 189–207.
  • Ghasemian, A. 2019. “Limits of model selection, link prediction, and community detection”. PhD thesis. University of Colorado at Boulder.
  • Ghasemian, A., P. Zhang, A. Clauset, C. Moore, and L. Peel. 2016. “Detectability thresholds and optimal algorithms for community structure in dynamic networks”. Physical Review X. 6(3): 031005.
  • Gilbert, E. N. 1959. “Random graphs”. Annals of Mathematical Statistics. 30(4): 1141–1144.
  • Gjoka, M., M. Kurant, C. T. Butts, and A. Markopoulou. 2010. “Walking in facebook: A case study of unbiased sampling of OSNs”. In: Proceedings of IEEE Infocom 2010. 1–9.
  • Gleich, D. F. 2015. “PageRank beyond the Web”. SIAM Review. 57(3): 321–363.
  • Gleich, D. and M. Mahoney. 2014. “Anti-differentiating approximation algorithms: A case study with min-cuts, spectral, and flow”. In: International Conference on Machine Learning. PMLR. 1018–1025.
  • Goldenberg, A., A. X. Zheng, S. E. Fienberg, E. M. Airoldi, et al. 2010. “A survey of statistical network models”. Foundations and Trends® in Machine Learning. 2(2): 129–233.
  • González-Pereira, B., V. P. Guerrero-Bote, and F. Moya-Anegón. 2010. “A new approach to the metric of journals’ scientific prestige: The SJR indicator”. Journal of Informetrics. 4(3): 379–391.
  • Good, B. H., Y.-A. De Montjoye, and A. Clauset. 2010. “Performance of modularity maximization in practical contexts”. Physical Review E. 81(4): 046106.
  • Goodman, L. A. 1961. “Snowball sampling”. Annals of Mathematical Statistics: 148–170.
  • Gori, M., A. Pucci, V. Roma, and I. Siena. 2007. “Itemrank: A random-walk based scoring algorithm for recommender engines”. In: IJCAI. Vol. 7. 2766–2771.
  • Grady, L. J. and J. R. Polimeni. 2010. Discrete Calculus: Applied Analysis on Graphs for Computational Science. Vol. 3. Springer.
  • Guédon, O. and R. Vershynin. 2016. “Community detection in sparse networks via Grothendieck’s inequality”. Probability Theory and Related Fields. 165(3): 1025–1049.
  • Hagberg, A. A., D. A. Schult, and P. J. Swart. 2008. “Exploring Network Structure, Dynamics, and Function using NetworkX”. In: G. Varoquaux, T. Vaught, and J. Millman (eds.): Proceedings of the 7th Python in Science Conference. Pasadena, Ed. by G. Varoquaux, T. Vaught, and J. Millman. CA USA, 11–15.
  • Hajek, B., Y. Wu, and J. Xu. 2016a. “Achieving exact cluster recovery threshold via semidefinite programming”. IEEE Transactions on Information Theory. 62(5): 2788–2797.
  • Hajek, B., Y. Wu, and J. Xu. 2016b. “Achieving exact cluster recovery threshold via semidefinite programming: Extensions”. IEEE Transactions on Information Theory. 62(10): 5918–5937.
  • Hastings, W. K. 1970a. “Monte Carlo Sampling Methods using Markov Chains and their Applications”.
  • Hastings, W. K. 1970b. “Monte Carlo sampling methods using Markov chains and their applications”. Biometrika. 57: 97–109.
  • Heckathorn, D. D. 1997. “Respondent-driven sampling: A new approach to the study of hidden populations”. Social Problems. 44(2): 174–199.
  • Hein, M., J.-Y. Audibert, and U. v. Luxburg. 2007. “Graph Laplacians and their convergence on random neighborhood graphs”. Journal of Machine Learning Research. 8(6).
  • Hofstad, R. van der. 2016. Random Graphs and Complex Networks. Vol. 1. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press.
  • Holland, P. W. and S. Leinhardt. 1981. “An exponential family of probability distributions for directed graphs”. Journal of the American Statistical Association. 76(373): 33–50.
  • Holme, P., B. J. Kim, C. N. Yoon, and S. K. Han. 2002. “Attack vulnerability of complex networks”. Physical review E. 65(5): 056109.
  • Holme, P. and J. Saramäki. 2012. “Temporal networks”. Physics Reports. 519(3): 97–125.
  • Hopcroft, J. and D. Sheldon. 2008. “Manipulation-resistant reputations using hitting time”. Internet Mathematics. 5(1–2): 71–90.
  • Horn, R. A. and C. R. Johnson. 2012. Matrix Analysis. Cambridge University Press.
  • Hu, J., H. Qin, T. Yan, and Y. Zhao. 2020. “Corrected Bayesian information criterion for stochastic block models”. Journal of the American Statistical Association. 115(532): 1771–1783.
  • Hubbell, C. H. 1965. “An input-output approach to clique identification”. Sociometry 377–399.
  • Jackson, M. O. 2010. Social and Economic Networks. Princeton University Press.
  • Jackson, M. O. and A. Wolinsky. 1996. “A strategic model of social and economic networks”. Journal of Economic Theory. 71(1): 44–74.
  • Jamonnak, S., J. Kilgallin, C.-C. Chan, and E. Cheng. 2015. “Recommenddit: A Recommendation Service for Reddit Communities”. In: 2015 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE. 374–379.
  • Janson, S., T. Luczak, and A. Rucinski. 2011. Random Graphs. Vol. 45. John Wiley & Sons.
  • Jog, V. and P.-L. Loh. 2015. “Recovering communities in weighted stochastic block models”. In: 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton). 1308–1315.
  • Jung, A., A. O. Hero III, A. C. Mara, S. Jahromi, A. Heimowitz, and Y. C. Eldar. 2019. “Semi-supervised learning in network-structured data via total variation minimization”. IEEE Transactions on Signal Processing. 67(24): 6256–6269.
  • Karrer, B. and M. E. Newman. 2011. “Stochastic blockmodels and community structure in networks”. Physical Review E. 83(1): 016107.
  • Katz, L. 1953. “A new status index derived from sociometric analysis”. Psychometrika. 18(1): 39–43.
  • Keener, J. P. 1993. “The Perron–Frobenius theorem and the ranking of football teams”. SIAM Review. 35(1): 80–93.
  • Kendall, M. G. 1955. “Further contributions to the theory of paired comparisons”. Biometrics. 11(1): 43–62.
  • Kingma, D. P. and M. Welling. 2014. “Auto-Encoding Variational Bayes”. In: Proceedings of the 2nd International Conference on Learning Representations (ICLR).
  • Kipf, T. N. and M. Welling. 2017. “Semi-supervised classification with graph convolutional networks”. In: 5th International Conference on Learning Representations. ICLR.
  • Kivelä, M., A. Arenas, M. Barthelemy, J. P. Gleeson, Y. Moreno, and M. A. Porter. 2014. “Multilayer networks”. Journal of Complex Networks. 2(3): 203–271.
  • Kleinberg, J. M. 1999. “Authoritative sources in a hyperlinked environment”. Journal of ACM. 46(5): 604–632.
  • Kleinfeld, J. S. 2002. “The small world problem”. Society. 39(2): 61–66.
  • Klicpera, J., A. Bojchevski, and S. Günnemann. 2019. “Predict then Propagate: Graph Neural Networks meet Personalized PageRank”. In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6–9, 2019.
  • Knoke, D. and S. Yang. 2019. Social Network Analysis. SAGE Publications.
  • Kolaczyk, E. D., D. B. Chua, and M. Barthélemy. 2009. “Group betweenness and co-betweenness: Inter-related notions of coalition centrality”. Social Networks. 31(3): 190–203.
  • Kolaczyk, E. D. and G. Csárdi. 2020. Statistical Analysis of Network Data with R. 2nd ed. Springer.
  • Krioukov, D., F. Papadopoulos, M. Kitsak, A. Vahdat, M. Boguñá. 2010. “Hyperbolic geometry of complex networks”. Physical Review E. 82(3): 036106.
  • Krzakala, F., C. Moore, E. Mossel, J. Neeman, A. Sly, L. Zdeborová, and P. Zhang. 2013. “Spectral redemption in clustering sparse networks”. Proceedings of the National Academy of Sciences. 110(52): 20935–20940.
  • Kuhn, H. W. 1982. “Nonlinear programming: A historical view”. ACM SIGMAP Bulletin. (31): 6–18.
  • Kumar, A., Y. Sabharwal, and S. Sen. 2004. “A simple linear time (1+ε)-approximation algorithm for k-means clustering in any dimensions”. In: Proceedings of 45-th IEEE Symposium on Foundations of Computer Science. IEEE. 454–462.
  • Landau, E. 1895. “Zur relativen Wertbemessung der Turnierresultate”. Deutsches Wochenschach. 11(366–369): 3.
  • Langville, A. N. and C. D. Meyer. 2012. Who’s# 1?: The Science of Rating and Ranking. Princeton University Press.
  • Le, C. M. and E. Levina. 2015. “Estimating the number of communities in networks by spectral methods”. arXiv preprint arXiv:1507.00827.
  • Le, C. M., E. Levina, and R. Vershynin. 2017. “Concentration and regularization of random graphs”. Random Structures & Algorithms. 51(3): 538–561.
  • LeCun, Y., C. Cortes, and C. J. Burges. 1998. The mnist database of handwritten digits. URL: http://yann.lecun.com/exdb/mnist/.
  • Lei, J. 2016. “A goodness-of-fit test for stochastic block models”. Annals of Statistics. 44(1): 401–424.
  • Lei, J. and A. Rinaldo. 2015. “Consistency of spectral clustering in stochastic block models”. Annals of Statistics. 43(1): 215–237.
  • Lima-Mendez, G. and J. van Helden. 2009. “The powerful law of the power law and other myths in network biology”. Molecular BioSystems. 5(12): 1482–1493.
  • Lu, Q. and L. Getoor. 2003. “Link-based classification”. In: Proceedings of the Twentieth International Conference on International Conference on Machine Learning. ICML’03. Washington, DC, USA: AAAI Press. 496–503.
  • Lusseau, D., K. Schneider, O. J. Boisseau, P. Haase, E. Slooten, and S. M. Dawson. 2003. “The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations”. Behavioral Ecology and Sociobiology. 54(4): 396–405.
  • Mai, X. and R. Couillet. 2018. “A random matrix analysis and improvement of semi-supervised learning for large dimensional data”. Journal of Machine Learning Research. 19(1): 3074–3100.
  • Mai, X. and R. Couillet. 2021. “Consistent Semi-Supervised Graph Regularization for High Dimensional Data”. Journal of Machine Learning Research. 22(94): 1–48.
  • Marchiori, M. and V. Latora. 2000. “Harmony in the small-world”. Physica A: Statistical Mechanics and its Applications. 285(3–4): 539–546.
  • Mariani, M. S., M. Medo, and Y.-C. Zhang. 2016. “Identification of milestone papers through time-balanced network centrality”. Journal of Informetrics. 10(4): 1207–1223.
  • Mastrandrea, R., J. Fournet, and A. Barrat. 2015. “Contact patterns in a high school: A comparison between data collected using wearable sensors, contact diaries and friendship surveys”. PLOS ONE. 10(9): 1–26.
  • Masuda, N. and R. Lambiotte. 2021. A Guide to Temporal Networks. 2nd ed. World Scientific.
  • Matias, C. and V. Miele. 2017. “Statistical clustering of temporal networks through a dynamic stochastic block model”. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 79(4): 1119–1141.
  • Mazalov, V. V., K. E. Avrachenkov, L. I. Trukhina, and B. T. Tsynguev. 2016. “Game-theoretic centrality measures for weighted graphs”. Fundamenta Informaticae. 145(3): 341–358.
  • Mazalov, V. V. and L. I. Trukhina. 2014. “Generating functions and the Myerson vector in communication networks”. Discrete Mathematics and Applications. 24(5): 295–303.
  • Mei, Q., D. Zhou, and K. Church. 2008. “Query suggestion using hitting time”. In: Proceedings of the 17th ACM CIKM’08. 469–478.
  • Metropolis, N., A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller . 1953. “Equation of state calculations by fast computing machines”. Journal of Chemical Physics. 21(6): 1087–1092.
  • Meyer, C. D. 2000. Matrix Analysis and Applied Linear Algebra. Vol. 71. SIAM.
  • Michalak, T. P., K. V. Aadithya, P. L. Szczepanski, B. Ravindran, and N. R. Jennings. 2013. “Efficient computation of the Shapley value for game-theoretic network centrality”. Journal of Artificial Intelligence Research. 46: 607–650.
  • Milgram, S. 1967. “The small world problem”. Psychology Today. 2(1): 60–67.
  • Moore, C. 2017. “The Computer Science and Physics of Community Detection: Landscapes, Phase Transitions, and Hardness”. Bulletin of EATCS. 1(121).
  • Moscato, V., A. Picariello, and G. Sperli. 2019. “Community detection based on game theory”. Engineering Applications of Artificial Intelligence. 85: 773–782.
  • Mossel, E., J. Neeman, and A. Sly. 2015. “Consistency thresholds for the planted bisection model”. In: Proceedings of the 47-th ACM Symposium on Theory of Computing. 69–75.
  • Mrvar, A. and V. Batagelj. 2016. “Analysis and visualization of large networks with program package Pajek”. Complex Adaptive Systems Modeling. 4(1): 1–8.
  • Myerson, R. B. 1977. “Graphs and cooperation in games”. Mathematics of Operations Research. 2(3): 225–229.
  • Namata, G., B. London, L. Getoor, and B. Huang. 2012. “Query-driven active surveying for collective classification”. In: 10th International Workshop on Mining and Learning with Graphs. Vol. 8.
  • Newman, M. 2018. Networks. 2nd ed. Oxford University Press,
  • Newman, M. E. 2001a. “Scientific collaboration networks. I. Network construction and fundamental results”. Physical Review E. 64(1): 016131.
  • Newman, M. E. 2001b. “Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality”. Physical Review E. 64(1): 016132.
  • Newman, M. E. 2004. “Fast algorithm for detecting community structure in networks”. Physical Review E. 69(6): 066133.
  • Newman, M. E. 2005a. “A measure of betweenness centrality based on random walks”. Social Networks. 27(1): 39–54.
  • Newman, M. E. 2005b. “Power laws, Pareto distributions and Zipf ’s law”. Contemporary Physics. 46(5): 323–351.
  • Newman, M. E. 2013. “Spectral methods for community detection and graph partitioning”. Physical Review E. 88(4): 042822.
  • Newman, M. E. 2016. “Equivalence between modularity optimization and maximum likelihood methods for community detection”. Physical Review E. 94(5): 052315.
  • Newman, M. E. and M. Girvan. 2004. “Finding and evaluating community structure in networks”. Physical Review E. 69(2): 026113.
  • Ofori-Boateng, D., A. K. Dey, Y. R. Gel, and H. V. Poor. 2021. “Graph-theoretic analysis of power grid robustness”. Advanced Data Analytics for Power Systems: 175.
  • Orecchia, L. and Z. A. Zhu. 2014. “Flow-based algorithms for local graph clustering”. In: Proceedings of the 25th ACM-SIAM Symposium on Discrete Algorithms. 1267–1286.
  • Orponen, P. and S. E. Schaeffer. 2005. “Local clustering of large graphs by approximate Fiedler vectors”. In: International Workshop on Experimental and Efficient Algorithms. Springer. 524–533.
  • Ostuni, V. C., T. Di Noia, E. Di Sciascio, and R. Mirizzi. 2013. “Top-N recommendations from implicit feedback leveraging linked open data”. In: Proceedings of the 7th ACM Conference on Recommender systems. 85–92.
  • Pan, R. K. and J. Saramäki. 2011. “Path lengths, correlations, and centrality in temporal networks”. Physical Review E. 84(1): 016105.
  • Peixoto, T. P. 2014a. “Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models”. Physical Review E. 89(1): 012804.
  • Peixoto, T. P. 2014b. “The graph-tool Python library”. URL: https://graph-tool.skewed.de.
  • Peixoto, T. P. 2019. “Bayesian stochastic blockmodeling”. Advances in Network Clustering and Blockmodeling 289–332.
  • Penrose, M. 2003. Random Geometric Graphs. Vol. 5. Oxford University Press.
  • Pinski, G. and F. Narin. 1976. “Citation influence for journal aggregates of scientific publications: Theory, with application to the literature of physics”. Information Processing & Management. 12(5): 297–312.
  • Prell, C. 2012. Social network analysis: History, theory and methodology. SAGE.
  • Puterman, M. L. 2014. Markov decision processes: Discrete stochastic dynamic programming. John Wiley & Sons.
  • Ravi, S. and Q. Diao. 2016. “Large scale distributed semi-supervised learning using streaming approximation”. In: Artificial intelligence and statistics. PMLR. 519–528.
  • Reichardt, J. and S. Bornholdt. 2006. “Statistical mechanics of community detection”. Physical Review E. 74(1): 016110.
  • Ribeiro, B. and D. Towsley. 2010. “Estimating and sampling graphs with multidimensional random walks”. In: Proceedings of the 10th ACM SIGCOMM. 390–403.
  • Robert, C. and G. Casella. 2013. Monte Carlo statistical methods. Springer.
  • Rochat, Y. 2009. “Closeness centrality extended to unconnected graphs: The harmonic centrality index”. In: Applications of Social Network Analysis Conference (ASNA).
  • Rosvall, M., D. Axelsson, and C. T. Bergstrom. 2009. “The map equation”. European Physical Journal, Special Topics. 178(1): 13–23.
  • Rosvall, M. and C. T. Bergstrom. 2008. “Maps of random walks on complex networks reveal community structure”. Proceedings of the National Academy of Sciences. 105(4): 1118–1123.
  • Rueda, D. F., E. Calle, and J. L. Marzo. 2017. “Robustness comparison of 15 real telecommunication networks: Structural and centrality measurements”. Journal of Network and Systems Management. 25(2): 269–289.
  • Saade, A., F. Krzakala, and L. Zdeborová. 2014. “Spectral clustering of graphs with the Bethe Hessian”. Advances in Neural Information Processing Systems. 27.
  • Sabidussi, G. 1966. “The centrality index of a graph”. Psychometrika. 31(4): 581–603.
  • Saldana, D. F., Y. Yu, and Y. Feng. 2017. “How many communities are there?”. Journal of Computational and Graphical Statistics. 26(1): 171–181.
  • Salganik, M. J. and D. D. Heckathorn. 2004. “Sampling and estimation in hidden populations using respondent-driven sampling”. Sociological Methodology. 34(1): 193–240.
  • Sankararaman, A. and F. Baccelli. 2018. “Community detection on euclidean random graphs”. In: Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms. SIAM. 2181–2200.
  • Sapiezynski, P., A. Stopczynski, D. D. Lassen, and S. Lehmann. 2019. “Interaction data from the Copenhagen networks study”. Scientific Data. 6(1): 1–10.
  • Scarselli, F., M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini. 2008. “The graph neural network model”. IEEE transactions on neural networks. 20(1): 61–80.
  • Scott, J. and P. J. Carrington. 2011. The SAGE handbook of social network analysis. SAGE Publications.
  • Seeley, J. R. 1949. “The net of reciprocal influence: A problem in treating sociometric data”. Canadian Journal of Experimental Psychology. 3: 234.
  • Serre, D. 2010. Matrices. Springer-Verlag.
  • Shapley, L. S. 1953. “A Value for n-Person Games”. In: Contributions to the Theory of Games (AM-28), Volume II. Ed. by H. W. Kuhn and A. W. Tucker. Princeton University Press.
  • Sinha, R. and R. Mihalcea. 2007. “Unsupervised graph-based word sense disambiguation using measures of word semantic similarity”. In: International Conference on Semantic Computing (ICSC 2007). IEEE. 363–369.
  • Skibski, O. and J. Sosnowska. 2018. “Axioms for distance-based centralities”. In: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32. No. 1.
  • Smirnova, E., K. Avrachenkov, and B. Trousse. 2010. “Using web graph structure for person name disambiguation”. In: Proceedings of CLEF, Vol. 77. 80.
  • Solla Price, D. de. 1965. “Networks of Scientific Papers”. Science. 149: 510–515.
  • Solla Price, D. de. 1976. “A general theory of bibliometric and other cumulative advantage processes”. Journal of the American society for Information science. 27(5): 292–306.
  • Stankovic, L., D. Mandic, M. Dakovic, M. Brajovic, B. Scalzo, S. Li, and A. G. Constantinides. 2020. “Data Analytics on Graphs Part III: Machine Learning on Graphs, from Graph Topology to Applications”. Foundations and Trends in Machine Learning. 13: 332–530.
  • Szczepański, P. L., T. P. Michalak, and T. Rahwan. 2016. “Efficient algorithms for game-theoretic betweenness centrality”. Artificial Intelligence. 231: 39–63.
  • Traag, V. A., L. Waltman, and N. J. Van Eck. 2019. “From Louvain to Leiden: Guaranteeing well-connected communities”. Scientific Reports. 9(1): 1–12.
  • Veremyev, A., O. A. Prokopyev, and E. L. Pasiliao. 2017. “Finding groups with maximum betweenness centrality”. Optimization Methods and Software. 32(2): 369–399.
  • Vershynin, R. 2018. High-dimensional Probability: An Introduction with Applications in Data Science, Vol. 47. Cambridge University Press.
  • Vigna, S. 2016. “Spectral ranking”. Network Science. 4(4): 433–445.
  • Volz, E. and D. D. Heckathorn. 2008. “Probability based estimation theory for respondent driven sampling”. Journal of Official Statistics. 24(1): 79.
  • Von Luxburg, U. 2007. “A tutorial on spectral clustering”. Statistics and Computing. 17(4): 395–416.
  • Wagner, D. and F. Wagner. 1993. “Between min cut and graph bisection”. In: International Symposium on Mathematical Foundations of Computer Science. Springer. 744–750.
  • Wang, X. and I. Davidson. 2010. “Flexible constrained spectral clustering”. In: Proceedings of the 16th ACM SIGKDD. 563–572.
  • Wang, X., B. Qian, and I. Davidson. 2014. “On constrained spectral clustering and its applications”. Data Mining and Knowledge Discovery. 28(1): 1–30.
  • Was, T. and O. Skibski. 2018. “Axiomatization of the PageRank centrality”. In: Proceedings of IJCAI. 3898–3904.
  • Wasserman, S. and K. Faust (1994. Social Network Analysis: Methods and Applications. Cambridge University Press.
  • Watts, D. J. 2000. Small Worlds: The Dynamics of Networks between Order and Randomness. Princeton University Press.
  • Watts, D. J. and S. H. Strogatz. 1998. “Collective dynamics of ‘small-world’ networks”. Nature. 393(6684): 440–442.
  • Wei, T.-H. 1952. “Algebraic Foundations of Ranking Theory”. PhD thesis. University of Cambridge.
  • West, J. D., M. C. Jensen, R. J. Dandrea, G. J. Gordon, and C. T. Bergstrom. 2013. “Author-level Eigenfactor metrics: Evaluating the influence of authors, institutions, and countries within the social science research network community”. Journal of the American Society for Information Science and Technology. 64(4), 787–801.
  • White, S. and P. Smyth. 2003. “Algorithms for estimating relative importance in networks”. In: Proceedings of the 9-th ACM SIGKDD. 266–275.
  • Wu, Z., S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip. 2020. “A comprehensive survey on graph neural networks”. IEEE Transactions on Neural Networks and Learning Systems. 32(1): 4–24.
  • Xiao, H., K. Rasul, and R. Vollgraf. 2017. “Fashion-mnist: A novel image dataset for benchmarking machine learning algorithms”. arXiv preprint arXiv:1708.07747.
  • Xu, K. S. and A. O. Hero. 2014. “Dynamic stochastic blockmodels for time-evolving social networks”. IEEE Journal of Selected Topics in Signal Processing. 8(4): 552–562.
  • Xu, M., V. Jog, and P.-L. Loh. 2020. “Optimal rates for community estimation in the weighted stochastic block model”. Annals of Statistics. 48(1): 183–204.
  • Yan, E. and Y. Ding. 2009. “Applying centrality measures to impact analysis: A coauthorship network analysis”. Journal of the American Society for Information Science and Technology. 60(10), 2107–2118.
  • Yang, J. and J. Leskovec. 2015. “Defining and evaluating network communities based on ground-truth”. Knowledge and Information Systems. 42(1): 181–213.
  • Yang, S., F. B. Keller, and L. Zheng. 2016. Social Network Analysis: Methods and Examples. SAGE Publications.
  • Yoshida, Y. 2014. “Almost linear-time algorithms for adaptive betweenness centrality using hypergraph sketches”. In: Proceedings of the 20th ACM SIGKDD. 1416–1425.
  • Yu, Y., T. Wang, and R. J. Samworth. 2015. “A useful variant of the Davis–Kahan theorem for statisticians”. Biometrika. 102(2): 315–323.
  • Yule, G. U. 1925. “A mathematical theory of evolution, based on the conclusions of Dr. JC Willis, FR S”. Philosophical Transactions of the Royal Society of London. Series B. 213(402–410): 21–87.
  • Zachary, W. W. 1977. “An information flow model for conflict and fission in small groups”. Journal of Anthropological Research. 33(4): 452–473.
  • Zhang, A. Y., H. H. Zhou, et al. 2016. “Minimax rates of community detection in stochastic block models”. Annals of Statistics. 44(5): 2252–2280.
  • Zhang, L. and T. P. Peixoto. 2020. “Statistical inference of assortative community structures”. Physical Review Research. 2(4): 043271.
  • Zhang, Y. and K. Rohe. 2018. “Understanding regularized spectral clustering via graph conductance”. In: Advances in Neural Information Processing Systems. 10631–10640.
  • Zhou, D., O. Bousquet, T. N. Lal, J. Weston, and B. Schölkopf. 2004. “Learning with local and global consistency”. In: Advances in Neural Information Processing Systems. 321–328.
  • Zhou, J., G. Cui, S. Hu, Z. Zhang, C. Yang, Z. Liu, L. Wang, C. Li, and M. Sun. 2020. “Graph neural networks: A review of methods and applications”. AI Open. 1: 57–81.
  • Zhu, X. and Z. Ghahramani. 2002. “Learning from labeled and unlabeled data with label propagation”. Technical Report CMU-CALD-02-107. Carnegie Mellon University, Pittsburgh.
  • Zhu, X., Z. Ghahramani, and J. D. Lafferty. 2003. “Semi-supervised learning using Gaussian fields and harmonic functions”. In: Proceedings of the 20th International Conference on Machine Learning (ICML-03). 912–919.
  • Zhu, Z. A., S. Lattanzi, and V. Mirrokni. 2013. “A local algorithm for finding well-connected clusters”. In: International Conference on Machine Learning. PMLR. 396–404.