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Generating attributed networks with communities.

Largeron C, Mougel PN, Rabbany R, Zaïane OR - PLoS ONE (2015)

Bottom Line: Evaluating algorithms or comparing algorithms for automatic discovery of communities requires networks with known structures.Synthetic generators of networks have been proposed for this task but most solely focus on connectivity and their properties and overlook attribute values and the network properties vis-à-vis these attributes.In this paper, we propose a new generator for attributed networks with community structure that dependably follows the properties of real world networks.

View Article: PubMed Central - PubMed

Affiliation: Hubert Curien Laboratory, University of Lyon, Saint-Étienne, France.

ABSTRACT
In many modern applications data is represented in the form of nodes and their relationships, forming an information network. When nodes are described with a set of attributes we have an attributed network. Nodes and their relationships tend to naturally form into communities or clusters, and discovering these communities is paramount to many applications. Evaluating algorithms or comparing algorithms for automatic discovery of communities requires networks with known structures. Synthetic generators of networks have been proposed for this task but most solely focus on connectivity and their properties and overlook attribute values and the network properties vis-à-vis these attributes. In this paper, we propose a new generator for attributed networks with community structure that dependably follows the properties of real world networks.

No MeSH data available.


User interface of the generator.
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pone.0122777.g011: User interface of the generator.

Mentions: Algorithm 1 is available at the url http://perso.univ-st-etienne.fr/largeron/ANC_Generator/. It is a free software distributed under the terms of the GNU General Public Licence (version 3). It is implemented in Java and it can be executed on any platform with a Java virtual machine. A screen copy of the user interface of the generator is presented in Fig 11. It is formed by three views. On the left side, the user selects the generator parameters presented in Table 1. The central part displays the generated graph using either a layout based on the graph structure (e.g., Kamada-Kawaï) or based on the attribute values for ∣𝓐∣ = 2. The right side of the interface presents the measures corresponding to the generated graph.


Generating attributed networks with communities.

Largeron C, Mougel PN, Rabbany R, Zaïane OR - PLoS ONE (2015)

User interface of the generator.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4404059&req=5

pone.0122777.g011: User interface of the generator.
Mentions: Algorithm 1 is available at the url http://perso.univ-st-etienne.fr/largeron/ANC_Generator/. It is a free software distributed under the terms of the GNU General Public Licence (version 3). It is implemented in Java and it can be executed on any platform with a Java virtual machine. A screen copy of the user interface of the generator is presented in Fig 11. It is formed by three views. On the left side, the user selects the generator parameters presented in Table 1. The central part displays the generated graph using either a layout based on the graph structure (e.g., Kamada-Kawaï) or based on the attribute values for ∣𝓐∣ = 2. The right side of the interface presents the measures corresponding to the generated graph.

Bottom Line: Evaluating algorithms or comparing algorithms for automatic discovery of communities requires networks with known structures.Synthetic generators of networks have been proposed for this task but most solely focus on connectivity and their properties and overlook attribute values and the network properties vis-à-vis these attributes.In this paper, we propose a new generator for attributed networks with community structure that dependably follows the properties of real world networks.

View Article: PubMed Central - PubMed

Affiliation: Hubert Curien Laboratory, University of Lyon, Saint-Étienne, France.

ABSTRACT
In many modern applications data is represented in the form of nodes and their relationships, forming an information network. When nodes are described with a set of attributes we have an attributed network. Nodes and their relationships tend to naturally form into communities or clusters, and discovering these communities is paramount to many applications. Evaluating algorithms or comparing algorithms for automatic discovery of communities requires networks with known structures. Synthetic generators of networks have been proposed for this task but most solely focus on connectivity and their properties and overlook attribute values and the network properties vis-à-vis these attributes. In this paper, we propose a new generator for attributed networks with community structure that dependably follows the properties of real world networks.

No MeSH data available.