A Survey of Network Reconstruction on Social Network

Social network contains more and more sensitive information, which makes the analysis of it more valuable. Network reconstruction is an important method for network analysis which is worthy to further study. In order to have a deep comprehension of the methods and the classification of network reconstruction, we need to do a classification and analysis for it. In this paper, network reconstruction on social network is the main research direction. Firstly, we introduce some concepts of privacy, protection methods and attack methods for social networks. After that we lead to the concept and usage of network reconstruction. Secondly, the existing methods of network reconstruction are introduced and classified based on the sources of observation data: information from node and information from network structure. And then we classify its application. Finally, we analyze the problem and challenge of existing methods. Meanwhile, some research points are also summarized to indicate the direction for other researchers and future work.

[1]  Murti V. Salapaka,et al.  Network reconstruction of dynamical polytrees with unobserved nodes , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[2]  Xiangyu Liu,et al.  Protecting Sensitive Relationships against Inference Attacks in Social Networks , 2012, DASFAA.

[3]  Prateek Mittal,et al.  SecGraph: A Uniform and Open-source Evaluation System for Graph Data Anonymization and De-anonymization , 2015, USENIX Security Symposium.

[4]  Xuerui Yang,et al.  An Extensive MicroRNA-Mediated Network of RNA-RNA Interactions Regulates Established Oncogenic Pathways in Glioblastoma , 2011, Cell.

[5]  Linyuan Lu,et al.  Link Prediction in Complex Networks: A Survey , 2010, ArXiv.

[6]  Jie Wang,et al.  Knowledge and Information Systems REGULAR PAPER , 2006 .

[7]  Pierangela Samarati,et al.  Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression , 1998 .

[8]  Ke Wang,et al.  Privacy Risk in Graph Stream Publishing for Social Network Data , 2011, 2011 IEEE 11th International Conference on Data Mining.

[9]  Kevin Murphy,et al.  Modelling Gene Expression Data using Dynamic Bayesian Networks , 2006 .

[10]  Alain Y. Kibangou,et al.  Distributed network topology reconstruction in presence of anonymous nodes , 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).

[11]  Chid Apte,et al.  Proceedings of the 2008 SIAM International Conference on Data Mining , 2008 .

[12]  Divesh Srivastava,et al.  Prediction Promotes Privacy in Dynamic Social Networks , 2010, WOSN.

[13]  J. Collins,et al.  Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling , 2003, Science.

[14]  Galina V. Glazko,et al.  Statistical Inference and Reverse Engineering of Gene Regulatory Networks from Observational Expression Data , 2012, Front. Gene..

[15]  Michal Linial,et al.  Using Bayesian Networks to Analyze Expression Data , 2000, J. Comput. Biol..

[16]  Tao Zhou,et al.  Link prediction in weighted networks: The role of weak ties , 2010 .

[17]  Gianluca Bontempi,et al.  minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information , 2008, BMC Bioinformatics.

[18]  Leting Wu,et al.  Reconstruction from Randomized Graph via Low Rank Approximation , 2010, SDM.

[19]  M. Newman,et al.  Hierarchical structure and the prediction of missing links in networks , 2008, Nature.

[20]  Xing-Ming Zhao,et al.  Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information , 2012, Bioinform..

[21]  Lise Getoor,et al.  Preserving the Privacy of Sensitive Relationships in Graph Data , 2007, PinKDD.

[22]  G S Michaels,et al.  Cluster analysis and data visualization of large-scale gene expression data. , 1998, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[23]  I S Kohane,et al.  Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. , 1999, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[24]  Hu Jie.  Survey on feature dimension reduction for high-dimensional data , 2008 .

[25]  Amr El Abbadi,et al.  Anonymizing Edge-Weighted Social Network Graphs , 2009 .

[26]  K. Liu,et al.  Towards identity anonymization on graphs , 2008, SIGMOD Conference.

[27]  Laks V. S. Lakshmanan,et al.  Proceedings of the 2008 ACM SIGMOD international conference on Management of data , 2008, SIGMOD 2008.

[28]  Mariano J. Alvarez,et al.  Genome-wide Identification of Post-translational Modulators of Transcription Factor Activity in Human B-Cells , 2009, Nature Biotechnology.

[29]  Daniel Kifer,et al.  Injecting utility into anonymized datasets , 2006, SIGMOD Conference.

[30]  Hiroshi Mamitsuka,et al.  Mining from protein–protein interactions , 2012, WIREs Data Mining Knowl. Discov..

[31]  Clement T. Yu,et al.  Proceedings of the 2006 ACM SIGMOD international conference on Management of data , 2006, SIGMOD 2006.

[32]  Jian Pei,et al.  A brief survey on anonymization techniques for privacy preserving publishing of social network data , 2008, SKDD.

[33]  Chris Wiggins,et al.  ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context , 2004, BMC Bioinformatics.

[34]  Xiangtao Li,et al.  Structural Attack to Anonymous Graph of Social Networks , 2013 .

[35]  Mohammad Al Hasan,et al.  A Survey of Link Prediction in Social Networks , 2011, Social Network Data Analytics.

[36]  Jian Pei,et al.  Preserving Privacy in Social Networks Against Neighborhood Attacks , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[37]  Ting Chen,et al.  Modeling Gene Expression with Differential Equations , 1998, Pacific Symposium on Biocomputing.

[38]  Evimaria Terzi,et al.  Reconstructing Graphs from Neighborhood Data , 2012, ICDM.

[39]  Hong Shen,et al.  Reconstructing Data Perturbed by Random Projections When the Mixing Matrix Is Known , 2009, ECML/PKDD.

[40]  Diogo M. Camacho,et al.  Wisdom of crowds for robust gene network inference , 2012, Nature Methods.

[41]  S Fuhrman,et al.  Reveal, a general reverse engineering algorithm for inference of genetic network architectures. , 1998, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[42]  Ömer Egecioglu,et al.  Anónimos: An LP-Based Approach for Anonymizing Weighted Social Network Graphs , 2010, IEEE Transactions on Knowledge and Data Engineering.

[43]  D di Bernardo,et al.  Inference of gene networks from temporal gene expression profiles. , 2007, IET systems biology.

[44]  Francesc Comellas,et al.  Spectral reconstruction of complex networks , 2008 .

[45]  David R. Bickel.  Probabilities of spurious connections in gene networks: application to expression time series , 2005, Bioinform..

[46]  Evimaria Terzi,et al.  Reconstructing Graphs from Neighborhood Data , 2012, 2012 IEEE 12th International Conference on Data Mining.

[47]  Srinivasan Parthasarathy,et al.  Proceedings of the 2010 SIAM International Conference on Data Mining , 2010 .

[48]  Xiaowei Ying,et al.  Randomizing Social Networks: a Spectrum Preserving Approach , 2008, SDM.

[49]  Roger Guimerà,et al.  Missing and spurious interactions and the reconstruction of complex networks , 2009, Proceedings of the National Academy of Sciences.

[50]  Yang Jin.  A Privacy Preservation Method for High Dimensional Data Mining , 2013 .

[51]  Wen-Xu Wang,et al.  Noise bridges dynamical correlation and topology in coupled oscillator networks. , 2010, Physical review letters.

[52]  Peter J. Woolf,et al.  Learning transcriptional regulatory networks from high throughput gene expression data using continuous three-way mutual information , 2008, BMC Bioinformatics.

[53]  Jesper Tegnér,et al.  Reverse engineering gene networks using singular value decomposition and robust regression , 2002, Proceedings of the National Academy of Sciences of the United States of America.