2010
DOI: 10.1209/0295-5075/91/18004
|View full text |Cite
|
Sign up to set email alerts
|

A PageRank-based preferential attachment model for the evolution of the World Wide Web

Abstract: We propose a model of network growth aimed at mimicking the evolution of the World Wide Web. To this purpose, we take as a key quantity, in the network evolution, the centrality or importance of a vertex as measured by its PageRank. Using a preferential attachment rule and a rewiring procedure based on this quantity, we can reproduce most of the topological properties of the system.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
3
0
1

Year Published

2011
2011
2021
2021

Publication Types

Select...
4
1
1

Relationship

1
5

Authors

Journals

citations
Cited by 6 publications
(4 citation statements)
references
References 36 publications
0
3
0
1
Order By: Relevance
“…It is interesting to note that the topology obtained with our model differs significantly from those resulting from preferential attachment implemented in network growth models, which often lead to scale-free degree distributions [22][24]. This type of broad distribution is also present in the empirical network of corporate control [9], [25].…”
Section: Resultsmentioning
confidence: 97%
“…It is interesting to note that the topology obtained with our model differs significantly from those resulting from preferential attachment implemented in network growth models, which often lead to scale-free degree distributions [22][24]. This type of broad distribution is also present in the empirical network of corporate control [9], [25].…”
Section: Resultsmentioning
confidence: 97%
“…Since reciprocity represents only the first viable correlation for such information sharing, it can be asserted that even better fits could be expected if the model took care of conservation of similar measures such as triad significance profile [29] or some other local structural motives. Such attempts were reported in [30] with interesting results. Taking into account the neighborhood of articles as a pool of more probable information sharing could also improve fitting qualities of the model.…”
mentioning
confidence: 84%
“…These nodes are known as hubs, or better the nodes which collect the largest number of connections with other nodes. In scientific literature this behavior is know as the preferential attachment logic [69]. Furthermore a cluster synchronization could be also considered, thinking the system formed of cluster of nodes, which firstly reach the synchronization inside each cluster, and then all the clusters start to synchronize together between them.…”
Section: Six Node Casementioning
confidence: 99%