Abstract
We investigate the fundamental statistical features of tagged (or annotated) networks having a rich variety of attributes associated with their nodes. Tags (attributes, annotations, properties, features, etc) provide essential information about the entity represented by a given node, thus, taking them into account represents a significant step towards a more complete description of the structure of large complex systems. Our main goal here is to uncover the relations between the statistical properties of the node tags and those of the graph topology. In order to better characterize the networks with tagged nodes, we introduce a number of new notions, including tag-assortativity (relating link probability to node similarity), and new quantities, such as node uniqueness (measuring how rarely the tags of a node occur in the network) and tag-assortativity exponent. We apply our approach to three large networks representing very different domains of complex systems. A number of the tag related quantities display analogous behaviour (e.g. the networks we studied are tag-assortative, indicating possible universal aspects of tags versus topology), while some other features, such as the distribution of the node uniqueness, show variability from network to network allowing for pin-pointing large scale specific features of real-world complex networks. We also find that for each network the topology and the tag distribution are scale invariant, and this self-similar property of the networks can be well characterized by the tag-assortativity exponent, which is specific to each system.
| Original language | English |
|---|---|
| Article number | 123026 |
| Journal | New Journal of Physics |
| Volume | 10 |
| DOIs | |
| Publication status | Published - dec. 18 2008 |
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ASJC Scopus subject areas
- Physics and Astronomy(all)
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Fundamental statistical features and self-similar properties of tagged networks. / Palla, Gergely; Farkas, I.; Pollner, Péter; Derényi, I.; Vicsek, T.
In: New Journal of Physics, Vol. 10, 123026, 18.12.2008.Research output: Article
}
TY - JOUR
T1 - Fundamental statistical features and self-similar properties of tagged networks
AU - Palla, Gergely
AU - Farkas, I.
AU - Pollner, Péter
AU - Derényi, I.
AU - Vicsek, T.
PY - 2008/12/18
Y1 - 2008/12/18
N2 - We investigate the fundamental statistical features of tagged (or annotated) networks having a rich variety of attributes associated with their nodes. Tags (attributes, annotations, properties, features, etc) provide essential information about the entity represented by a given node, thus, taking them into account represents a significant step towards a more complete description of the structure of large complex systems. Our main goal here is to uncover the relations between the statistical properties of the node tags and those of the graph topology. In order to better characterize the networks with tagged nodes, we introduce a number of new notions, including tag-assortativity (relating link probability to node similarity), and new quantities, such as node uniqueness (measuring how rarely the tags of a node occur in the network) and tag-assortativity exponent. We apply our approach to three large networks representing very different domains of complex systems. A number of the tag related quantities display analogous behaviour (e.g. the networks we studied are tag-assortative, indicating possible universal aspects of tags versus topology), while some other features, such as the distribution of the node uniqueness, show variability from network to network allowing for pin-pointing large scale specific features of real-world complex networks. We also find that for each network the topology and the tag distribution are scale invariant, and this self-similar property of the networks can be well characterized by the tag-assortativity exponent, which is specific to each system.
AB - We investigate the fundamental statistical features of tagged (or annotated) networks having a rich variety of attributes associated with their nodes. Tags (attributes, annotations, properties, features, etc) provide essential information about the entity represented by a given node, thus, taking them into account represents a significant step towards a more complete description of the structure of large complex systems. Our main goal here is to uncover the relations between the statistical properties of the node tags and those of the graph topology. In order to better characterize the networks with tagged nodes, we introduce a number of new notions, including tag-assortativity (relating link probability to node similarity), and new quantities, such as node uniqueness (measuring how rarely the tags of a node occur in the network) and tag-assortativity exponent. We apply our approach to three large networks representing very different domains of complex systems. A number of the tag related quantities display analogous behaviour (e.g. the networks we studied are tag-assortative, indicating possible universal aspects of tags versus topology), while some other features, such as the distribution of the node uniqueness, show variability from network to network allowing for pin-pointing large scale specific features of real-world complex networks. We also find that for each network the topology and the tag distribution are scale invariant, and this self-similar property of the networks can be well characterized by the tag-assortativity exponent, which is specific to each system.
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U2 - 10.1088/1367-2630/10/12/123026
DO - 10.1088/1367-2630/10/12/123026
M3 - Article
AN - SCOPUS:59349102883
VL - 10
JO - New Journal of Physics
JF - New Journal of Physics
SN - 1367-2630
M1 - 123026
ER -