On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Structure and evolution of blogspace
Communications of the ACM - The Blogosphere
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
Structure and evolution of online social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient identification of overlapping communities
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
SNAKDD 2008 social network mining and analysis postworkshop report
ACM SIGKDD Explorations Newsletter
Social network aided multicast delivery scheme for human contact-based networks
Proceedings of the 1st Annual Workshop on Simplifying Complex Network for Practitioners
A link analysis model based on online social networks
WISM'11 Proceedings of the 2011 international conference on Web information systems and mining - Volume Part II
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We study the communication dynamics of Blog networks, focusing on the Russian section of LiveJournal as a case study. Communication (blogger-to-blogger links) in such online communication networks is very dynamic: over 60% of the links in the network are new from one week to the next, though the set of bloggers remains approximately constant. Two fundamental questions are: (i) what models adequately describe such dynamic communication behavior; and (ii) how does one detect the phase transitions, i.e. the changes that go beyond the standard high-level dynamics? We approach these questions through the notion of stable statistics. We give strong experimental evidence to the fact that, despite the extreme amount of communication dynamics, several aggregate statistics are remarkably stable. We use stable statistics to test our models of communication dynamics postulating that any good model should produce values for these statistics which are both stable and close to the observed ones. Stable statistics can also be used to identify phase transitions, since any change in a normally stable statistic indicates a substantial change in the nature of the communication dynamics. We describe models of the communication dynamics in large social networks based on the principle of locality of communication: a node's communication energy is spent mostly within its own "social area," the locality of the node.