Towards the Dynamic Community Discovery in Decentralized Online Social Networks

The community structure is one of the most studied features of the Online Social Networks (OSNs). Community detection guarantees several advantages for both centralized and decentralized social networks. Decentralized Online Social Networks (DOSNs) have been proposed to provide more control over private data. Several challenges in DOSNs can be faced by exploiting communities. The detection of communities and the management of their evolution represents a hard process, especially in highly dynamic environments, where churn is a real problem. In this paper, we focus our attention on the analysis of dynamic community detection in DOSNs by studying a real Facebook dataset. We evaluate two different dynamic community discovery classes to understand which of them can be applied to a distributed environment. Results prove that the social graph has high instability and distributed solutions to manage the dynamism are needed and show that a Temporal Trade-off class is the most promising one.

[1]  Osmar R. Zaïane,et al.  MODEC - Modeling and Detecting Evolutions of Communities , 2011, ICWSM.

[2]  Laura Ricci,et al.  Distributed Coverage of Ego Networks in F2F Online Social Networks , 2016, 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld).

[3]  Mansoureh Takaffoli,et al.  Community Evolution Mining in Dynamic Social Networks , 2011 .

[4]  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.

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

[6]  Dino Pedreschi,et al.  Tiles: an online algorithm for community discovery in dynamic social networks , 2017, Machine Learning.

[7]  Nikita Borisov,et al.  Cachet: a decentralized architecture for privacy preserving social networking with caching , 2012, CoNEXT '12.

[8]  Pan Hui,et al.  Distributed community detection in delay tolerant networks , 2007, MobiArch '07.

[9]  Pascal Bouvry,et al.  SHARC: Community-based partitioning for mobile ad hoc networks using neighborhood similarity , 2010, 2010 IEEE International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM).

[10]  Jean-Loup Guillaume,et al.  Communities in Evolving Networks: Definitions, Detection, and Analysis Techniques , 2013 .

[11]  Sonja Buchegger,et al.  Implementing a P2P Social Network - Early Experiences and Insights from PeerSoN , 2009 .

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

[13]  Gustavo Alonso,et al.  Understanding replication in databases and distributed systems , 2000, Proceedings 20th IEEE International Conference on Distributed Computing Systems.

[14]  Karl Aberer,et al.  My3: A highly-available P2P-based online social network , 2011, 2011 IEEE International Conference on Peer-to-Peer Computing.

[15]  Peter V. Marsden,et al.  Egocentric and sociocentric measures of network centrality , 2002, Soc. Networks.

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

[17]  A. Barabasi,et al.  Quantifying social group evolution , 2007, Nature.

[18]  Andrea E. F. Clementi,et al.  Distributed community detection in dynamic graphs , 2013, Theor. Comput. Sci..

[19]  Giulio Rossetti,et al.  Dynamic Community Analysis in Decentralized Online Social Networks , 2017, Euro-Par Workshops.

[20]  Rémy Cazabet,et al.  Dynamic Community Detection , 2014, Encyclopedia of Social Network Analysis and Mining.

[21]  Ralf Steinmetz,et al.  LifeSocial.KOM: A P2P-Based Platform for Secure Online Social Networks , 2010, 2010 IEEE Tenth International Conference on Peer-to-Peer Computing (P2P).

[22]  Laura Ricci,et al.  DiDuSoNet: A P2P architecture for distributed Dunbar-based social networks , 2016, Peer-to-Peer Netw. Appl..

[23]  Dino Pedreschi,et al.  A classification for community discovery methods in complex networks , 2011, Stat. Anal. Data Min..

[24]  Laura Ricci,et al.  Evaluation of Structural and Temporal Properties of Ego Networks for Data Availability in DOSNs , 2018, Mob. Networks Appl..

[25]  Giulio Rossetti,et al.  Community Discovery in Dynamic Networks , 2017, ACM Comput. Surv..

[26]  Laura Ricci,et al.  P2P architectures for distributed online social networks , 2013, 2013 International Conference on High Performance Computing & Simulation (HPCS).

[27]  Osmar R. Zaïane,et al.  Community evolution prediction in dynamic social networks , 2014, 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014).

[28]  Derek Greene,et al.  Tracking the Evolution of Communities in Dynamic Social Networks , 2010, 2010 International Conference on Advances in Social Networks Analysis and Mining.

[29]  Christos Faloutsos,et al.  Graphs over time: densification laws, shrinking diameters and possible explanations , 2005, KDD '05.

[30]  Laura Ricci,et al.  Epidemic Diffusion of Social Updates in Dunbar-Based DOSN , 2014, Euro-Par Workshops.

[31]  Nancy A. Lynch,et al.  Impossibility of distributed consensus with one faulty process , 1985, JACM.

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