On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Log-Logistic Software Reliability Growth Model
HASE '98 The 3rd IEEE International Symposium on High-Assurance Systems Engineering
Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Why is the internet traffic bursty in short time scales?
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
NSDI'06 Proceedings of the 3rd conference on Networked Systems Design & Implementation - Volume 3
Characterizing individual communication patterns
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Social Synchrony: Predicting Mimicry of User Actions in Online Social Media
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Power-Law Distributions in Empirical Data
SIAM Review
Using a model of social dynamics to predict popularity of news
Proceedings of the 19th international conference on World wide web
Surprising patterns for the call duration distribution of mobile phone users
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Proceedings of the 20th international conference on World wide web
Care to comment?: recommendations for commenting on news stories
Proceedings of the 21st international conference on World Wide Web
Modeling and predicting behavioral dynamics on the web
Proceedings of the 21st international conference on World Wide Web
Are web users really Markovian?
Proceedings of the 21st international conference on World Wide Web
Quantifying reciprocity in large weighted communication networks
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
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How often do individuals perform a given communication activity in the Web, such as posting comments on blogs or news? Could we have a generative model to create communication events with realistic inter-event time distributions (IEDs)? Which properties should we strive to match? Current literature has seemingly contradictory results for IED: some studies claim good fits with power laws; others with non-homogeneous Poisson processes. Given these two approaches, we ask: which is the correct one? Can we reconcile them all? We show here that, surprisingly, both approaches are correct, being corner cases of the proposed Self-Feeding Process (SFP). We show that the SFP (a) exhibits a unifying power, which generates power law tails (including the so-called "top-concavity" that real data exhibits), as well as short-term Poisson behavior; (b) avoids the "i.i.d. fallacy", which none of the prevailing models have studied before; and (c) is extremely parsimonious, requiring usually only one, and in general, at most two parameters. Experiments conducted on eight large, diverse real datasets (e.g., Youtube and blog comments, e-mails, SMSs, etc) reveal that the SFP mimics their properties very well.