Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Introduction to Stochastic Dynamic Programming: Probability and Mathematical
Introduction to Stochastic Dynamic Programming: Probability and Mathematical
Peer-to-Peer: Harnessing the Power of Disruptive Technologies
Peer-to-Peer: Harnessing the Power of Disruptive Technologies
IEEE Internet Computing
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
CRAWDAD: A Community Resource for Archiving Wireless Data at Dartmouth
IEEE Pervasive Computing
The FastTrack overlay: a measurement study
Computer Networks: The International Journal of Computer and Telecommunications Networking - Overlay distribution structures and their applications
A stochastic model for the evolution of the Web allowing link deletion
ACM Transactions on Internet Technology (TOIT)
Analysis of a campus-wide wireless network
Wireless Networks
The bittorrent p2p file-sharing system: measurements and analysis
IPTPS'05 Proceedings of the 4th international conference on Peer-to-Peer Systems
Norm convergence in populations of dynamically interacting agents
AICS'09 Proceedings of the 20th Irish conference on Artificial intelligence and cognitive science
The influence of random interactions and decision heuristics on norm evolution in social networks
Computational & Mathematical Organization Theory
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We present a stochastic model for a social network, where new actors may join the network, existing actors may become inactive and, at a later stage, reactivate themselves. Our model captures the evolution of the network, assuming that actors attain new relations or become active according to the preferential attachment rule. We derive the mean-field equations for this stochastic model and show that, asymptotically, the distribution of actors obeys a power-law distribution. In particular, the model applies to social networks such as wireless local area networks, where users connect to access points, and peer-to-peer networks where users connect to each other. As a proof of concept, we demonstrate the validity of our model empirically by analysing a public log containing traces from a wireless network at Dartmouth College over a period of three years. Analysing the data processed according to our model, we demonstrate that the distribution of user accesses is asymptotically a power-law distribution.