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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.


Average clustering coefficient for varying N and MTE = 10×N.
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pone.0122777.g009: Average clustering coefficient for varying N and MTE = 10×N.

Mentions: To study the first aspect, we computed the average clustering coefficient on 10 graphs for different values of the parameter N. Results are presented in Fig 8 and show that the average clustering coefficient decreases when the graph size increases. This behavior is due to the edge insertion process since the probability to add an edge which closes a triangle is lower for large number of vertices. To maintain a high clustering coefficient, it is possible to increase parameter MTE along with the number of vertices. Results where MTE = N×10 for varying number of vertices are presented in Fig 9. Using these parameters, the average clustering coefficient remains relatively stable.


Generating attributed networks with communities.

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

Average clustering coefficient for varying N and MTE = 10×N.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0122777.g009: Average clustering coefficient for varying N and MTE = 10×N.
Mentions: To study the first aspect, we computed the average clustering coefficient on 10 graphs for different values of the parameter N. Results are presented in Fig 8 and show that the average clustering coefficient decreases when the graph size increases. This behavior is due to the edge insertion process since the probability to add an edge which closes a triangle is lower for large number of vertices. To maintain a high clustering coefficient, it is possible to increase parameter MTE along with the number of vertices. Results where MTE = N×10 for varying number of vertices are presented in Fig 9. Using these parameters, the average clustering coefficient remains relatively stable.

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.