On approximate nesting of multiple social network graphs: a preliminary study

A fundamental problem in Social Network Analysis is how to move from single-layer to multi-layer, which provide a holistic view. User profiles resolution has received considerable attention since it allows to match users on different online social networks (OSNs). However, to the best of our knowledge, no study has focused on nesting operation for merging OSNs graphs. This work is a first step in the direction of defining the data model and the algorithm to perform approximate nesting of multiple OSNs graphs, based on user features. We provide initial experimental evidence based on synthetic data.

[1]  P. Slater.  Inconsistencies in a schedule of paired comparisons , 1961 .

[2]  Giacomo Bergami,et al.  A new Nested Graph Model for Data Integration , 2018 .

[3]  Bartunov Sergey,et al.  Joint Link-Attribute User Identity Resolution in Online Social Networks , 2012 .

[4]  Ibrahim Baggili,et al.  Forensic analysis of social networking applications on mobile devices , 2012, Digit. Investig..

[5]  Martin Vetterli,et al.  Where You Are Is Who You Are: User Identification by Matching Statistics , 2015, IEEE Transactions on Information Forensics and Security.

[6]  Gary Steri,et al.  A Survey of Techniques for the Identification of Mobile Phones Using the Physical Fingerprints of the Built-In Components , 2017, IEEE Communications Surveys & Tutorials.

[7]  Danilo Montesi,et al.  THoSP: an algorithm for nesting property graphs , 2018, GRADES/NDA@SIGMOD/PODS.

[8]  Danilo Montesi,et al.  A Cluster-based Approach of Smartphone Camera Fingerprint for User Profiles Resolution within Social Network , 2018, IDEAS.

[9]  Matteo Magnani,et al.  A Join Operator for Property Graphs , 2017, EDBT/ICDT Workshops.

[10]  Ali Dehghantanha,et al.  Investigating Social Networking applications on smartphones detecting Facebook, Twitter, LinkedIn and Google+ artefacts on Android and iOS platforms , 2016 .

[11]  Jure Leskovec,et al.  Defining and evaluating network communities based on ground-truth , 2012, Knowledge and Information Systems.

[12]  George H. L. Fletcher,et al.  gMark: Schema-Driven Generation of Graphs and Queries , 2015, IEEE Transactions on Knowledge and Data Engineering.

[13]  Reza Zafarani,et al.  User Identity Linkage across Online Social Networks: A Review , 2017, SKDD.

[14]  Miroslav Goljan,et al.  Digital camera identification from sensor pattern noise , 2006, IEEE Transactions on Information Forensics and Security.

[15]  Heiko Gimperlein,et al.  Metaplex Networks: Influence of the Exo-Endo Structure of Complex Systems on Diffusion , 2018, SIAM Rev..

[16]  Hassan Chafi,et al.  The LDBC Social Network Benchmark: Interactive Workload , 2015, SIGMOD Conference.

[17]  Petros Efstathopoulos,et al.  Utility-Driven Graph Summarization , 2018, Proc. VLDB Endow..

[18]  Steven Skiena,et al.  The Algorithm Design Manual , 2020, Texts in Computer Science.

[19]  Lior Rokach,et al.  Entity Matching in Online Social Networks , 2013, 2013 International Conference on Social Computing.

[20]  Matteo Magnani,et al.  Multilayer Social Networks , 2016 .