Topological Structure and Interest Spectrum of the Group Interest Network

@inproceedings{Zhang2009TopologicalSA,
  title={Topological Structure and Interest Spectrum of the Group Interest Network},
  author={Ning Zhang},
  booktitle={Complex},
  year={2009}
}
  • Ning Zhang
  • Published in Complex 23 February 2009
  • Computer Science
In this paper, the behavior characteristics that the specifical campus group users accessing world wide web has been studied, the dynamic group interest network has been constructed, which was a para-bipartite graph and the topological structure had been discussed. Although the users’ visiting time is random and the web pages they visited are different but the interests of a majority of the campus group are accordant. The results indicate that the incoming degree distribution of the group… 

References

SHOWING 1-10 OF 29 REFERENCES

Ranking web sites with real user traffic

TLDR
The traffic-weighted Web host graph obtained from a large sample of real Web users is analyzed, finding that while search is directly involved in a surprisingly small fraction of user clicks, it leads to a much larger fraction of all sites visited.

Internet: Diameter of the World-Wide Web

TLDR
The World-Wide Web becomes a large directed graph whose vertices are documents and whose edges are links that point from one document to another, which determines the web's connectivity and consequently how effectively the authors can locate information on it.

The Structure and Function of Complex Networks

TLDR
Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

Human dynamics revealed through Web analytics

TLDR
This work analyzes properly anonymized logs detailing the access history to Emory University's Web site and finds that linear preferential linking, priority-based queuing, and the decay of interest for the contents of the pages are the essential ingredients to understand the way users navigate the Web.

On power-law relationships of the Internet topology

TLDR
These power-laws hold for three snapshots of the Internet, between November 1997 and December 1998, despite a 45% growth of its size during that period, and can be used to generate and select realistic topologies for simulation purposes.

The structure of scientific collaboration networks.

  • M. Newman
  • Physics
    Proceedings of the National Academy of Sciences of the United States of America
  • 2001
TLDR
It is shown that these collaboration networks form "small worlds," in which randomly chosen pairs of scientists are typically separated by only a short path of intermediate acquaintances.

A theory of web traffic

TLDR
According to the model, the competition between webpages for viewers pushes the web into a self-organized critical state, with a power law distribution of traffic intensity, and the most interesting webpages are in a near-critical state.

Statistical mechanics of complex networks

TLDR
A simple model based on these two principles was able to reproduce the power-law degree distribution of real networks, indicating a heterogeneous topology in which the majority of the nodes have a small degree, but there is a significant fraction of highly connected nodes that play an important role in the connectivity of the network.

Evolution of Networks: From Biological Nets to the Internet and WWW (Physics)

TLDR
The aim of the text is to understand networks and the basic principles of their structural organization and evolution, so even students without a deep knowledge of mathematics and statistical physics will be able to rely on this as a reference.

Scientific collaboration networks. I. Network construction and fundamental results.

  • M. Newman
  • Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2001
TLDR
Using computer databases of scientific papers in physics, biomedical research, and computer science, a network of collaboration between scientists in each of these disciplines is constructed, and a number of measures of centrality and connectedness in the same networks are studied.