• Corpus ID: 171098

Evaluating and Optimising Models of Network Growth

  title={Evaluating and Optimising Models of Network Growth},
  author={Richard G. Clegg and Raul Landa and Uli Harder and Miguel Rio},
This paper presents a statistically sound method for measuring the accuracy with which a probabilistic model reflects the growth of a network, and a method for optimising parameters in such a model. The technique is data-driven, and can be used for the modeling and simulation of any kind of evolving network. The overall framework, a Framework for Evolving Topology Analysis (FETA), is tested on data sets collected from the Internet AS-level topology, social networking websites and a co… 
Measuring the Likelihood of Models for Network Evolution
The framework described in this paper gives the likelihood that the target network arose from the hypothesised model, and a null model (of random evolution) is proposed as a baseline for comparison.
A Survey of Statistical Network Models
An overview of the historical development of statistical network modeling is overviewed and a number of examples that have been studied in the network literature are introduced, and a subsequent discussion focuses on anumber of prominent static and dynamic network models and their interconnections.


Accurately modeling the Internet topology
  • Shi Zhou, R. Mondragón
  • Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2004
The positive-feedback preference (PFP) model is introduced which accurately reproduces many topological properties of the AS-level internet, including degree distribution, rich-club connectivity, the maximum degree, shortest path length, short cycles, disassortative mixing, and betweenness centrality.
Growth of the flickr social network
It is found that links tend to be created by users who already have many links, that users tend to respond to incoming links by creating links back to the source, and that users link to other users who are already close in the network.
Observing the evolution of internet as topology
The topology liveness problem is formulated and a solution based on the analysis of BGP data is proposed, which finds that the impact of transient routing dynamics on topology observation decreases exponentially over time, and that the real topology dynamics consist of a constant-rate birth process and a Constant-rate death process.
Network topologies: inference, modeling, and generation
The main objective of this survey is to familiarize the reader with research on network topology over the past decade, and study techniques for inference, modeling, and generation of the Internet topology at both the router and administrative levels.
Topology of evolving networks: local events and universality
A continuum theory is proposed that predicts the connectivity distribution of the network describing the professional links between movie actors as well as the scaling function and the exponents, in good agreement with numerical results.
Orbis: rescaling degree correlations to generate annotated internet topologies
This paper proposes techniques to generate annotated, Internet router graphs of different sizes based on existing observations of Internet characteristics and finds that the generated graphs match a variety of graph properties of observed topologies for a range of target graph sizes.
Emergence of scaling in random networks
A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
Towards capturing representative AS-level Internet topologies
The main objective in this paper is to quantify the completeness of Internet AS maps constructed from the Oregon route-views and to attempt to capture more representative AS-level Internet topology.
On distinguishing between Internet power law topology generators
  • T. Bu, D. Towsley
  • Computer Science
    Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies
  • 2002
This work proposes a variation of the recent incremental topology generator of R. Albert and A. Barabasi that is more successful at matching the power law exponent and the clustering behavior of the Internet.
A new look at the statistical model identification
The history of the development of statistical hypothesis testing in time series analysis is reviewed briefly and it is pointed out that the hypothesis testing procedure is not adequately defined as