Heavy-tailed probability distributions in the World Wide Web
A practical guide to heavy tails
A random graph model for massive graphs
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
The degree sequence of a scale-free random graph process
Random Structures & Algorithms
IEEE Internet Computing
Heuristically Optimized Trade-Offs: A New Paradigm for Power Laws in the Internet
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Linked: How Everything Is Connected to Everything Else and What It Means
Linked: How Everything Is Connected to Everything Else and What It Means
Unveiling facebook: a measurement study of social network based applications
Proceedings of the 8th ACM SIGCOMM conference on Internet measurement
Similarity Management in Phonebook-Centric Social Networks
ICIW '09 Proceedings of the 2009 Fourth International Conference on Internet and Web Applications and Services
Similarity Distribution in Phonebook-Centric Social Networks
ICWMC '09 Proceedings of the 2009 Fifth International Conference on Wireless and Mobile Communications
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Social networks are becoming more and more popular nowadays. The increasing capabilities of mobile phones enable them to participate in such networks. The phonebooks in the mobile devices represent social relationships, that can be integrated in the social networks. Following we refer to this solution as phonebook-centric social networks. Such networks provide a synchronization mechanism between phonebooks of the users and the social network which allows detecting network members listed in the phonebooks (semi-) automatically. The detection of those members is based on the similarity of the personal data, e.g., similar name, same phone number, address, etc... Users can mark similarities between their phonebook contacts and members in the network. After this, if one of their contacts changes her or his personal detail, it will be propagated automatically into the phonebooks, after considering privacy settings. Synchronization time and energy consumption for synchronization on the mobiles and the scalability of the system strongly depend on the number of users and similarities. We have implemented a phone book-centric social network, called Phonebookmark and investigated the structure of the network. We experienced that the distribution of similarities follows a power law, as well as the distribution of the in- and out-degrees in the social network. In this paper we propose a model for estimating the total number of similarities and we show that this estimation applies very well to the historical data of Phonebookmark.