Evaluation of Structural and Temporal Properties of Ego Networks for Data Availability in DOSNs

The large diffusion of Online Social Networks (OSNs) has influenced the way people interact with each other. OSNs present several drawbacks, one of the most important is the problem of privacy disclosures. Distributed Online Social Networks (DOSNs) have been proposed as a valid alternative solution to solve this problem. DOSNs are Online Social Networks implemented on a distributed platform, such as a P2P system or a mobile network. However, the decentralization of the control presents several challenges, one of the main ones is guaranteeing data availability without relying on a central server. To this aim, users’ data allocation strategies have to be defined and this requires the knowledge of both structural and temporal characteristics of ego networks which is a difficult task due to the lack of real datasets limiting the research in this field. The goal of this paper is the study of the behaviour of users in a real social network in order to define proper strategies to allocate the users’ data on the DOSN nodes. In particular, we present an analysis of the temporal affinity and the structure of communities and their evolution over the time by using a real Facebook dataset.

[1]  Martin Everett,et al.  Ego network betweenness , 2005, Soc. Networks.

[2]  Laura Ricci,et al.  The impact of user's availability on On-line Ego Networks: a Facebook analysis , 2016, Comput. Commun..

[3]  João Gama,et al.  Dynamic communities in evolving customer networks: an analysis using landmark and sliding windows , 2014, Social Network Analysis and Mining.

[4]  Krzysztof Rzadca,et al.  Decentralized Online Social Networks , 2010, Handbook of Social Network Technologies.

[5]  Réka Albert,et al.  Near linear time algorithm to detect community structures in large-scale networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Robin I. M. Dunbar Social Brain Hypothesis , 1998, Encyclopedia of Evolutionary Psychological Science.

[7]  Ralf Steinmetz,et al.  LifeSocial.KOM: A secure and P2P-based solution for online social networks , 2011, 2011 IEEE Consumer Communications and Networking Conference (CCNC).

[8]  C. Tappert,et al.  A Survey of Binary Similarity and Distance Measures , 2010 .

[9]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[10]  Jens F. Binder,et al.  Relationships and the social brain: integrating psychological and evolutionary perspectives. , 2012, British journal of psychology.

[11]  Thomas V. Pollet,et al.  Exploring variation in active network size: Constraints and ego characteristics , 2009, Soc. Networks.

[12]  Dino Pedreschi,et al.  DEMON: a local-first discovery method for overlapping communities , 2012, KDD.

[13]  Laura Ricci,et al.  Trusted Dynamic Storage for Dunbar-Based P2P Online Social Networks , 2014, OTM Conferences.

[14]  Antony I. T. Rowstron,et al.  Pastry: Scalable, Decentralized Object Location, and Routing for Large-Scale Peer-to-Peer Systems , 2001, Middleware.

[15]  Refik Molva,et al.  Safebook: A privacy-preserving online social network leveraging on real-life trust , 2009, IEEE Communications Magazine.

[16]  Karl Aberer,et al.  A Decentralized Online Social Network with Efficient User-Driven Replication , 2012, 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing.

[17]  Boleslaw K. Szymanski,et al.  Overlapping community detection in networks: The state-of-the-art and comparative study , 2011, CSUR.

[18]  Rajesh Sharma,et al.  SuperNova: Super-peers based architecture for decentralized online social networks , 2011, 2012 Fourth International Conference on Communication Systems and Networks (COMSNETS 2012).

[19]  Sonja Buchegger,et al.  PeerSoN: P2P social networking: early experiences and insights , 2009, SNS '09.