Name
Title | ||
|---|---|---|
Optimizing an organized modularity measure for topographic graph clustering: A deterministic annealing approach |
Abstract | ||
|---|---|---|
This paper proposes an organized generalization of Newman and Girvan's modularity measure for graph clustering. Optimized via a deterministic annealing scheme, this measure produces topologically ordered graph clusterings that lead to faithful and readable graph representations based on clustering induced graphs. Topographic graph clustering provides an alternative to more classical solutions in which a standard graph clustering method is applied to build a simpler graph that is then represented with a graph layout algorithm. A comparative study on four real world graphs ranging from 34 to 1133 vertices shows the interest of the proposed approach with respect to classical solutions and to self-organizing maps for graphs. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.neucom.2009.11.023 | Neurocomputing |
Keywords | Field | DocType |
classical solution,self-organizing maps,readable graph representation,graph clustering,organized modularity measure,deterministic annealing,induced graph,deterministic annealing approach,social network,simpler graph,graph,modularity,graph layout algorithm,topographic graph clustering,real world graph,graph clusterings,standard graph,clustering,comparative study,self organizing maps,graph representation | Line graph,Graph property,Null graph,Artificial intelligence,Butterfly graph,Lattice graph,Machine learning,Graph (abstract data type),Voltage graph,Mathematics,Graph Layout | Journal |
Volume | Issue | ISSN |
73 | 7-9 | Neurocomputing |
Citations | PageRank | References |
14 | 0.78 | 24 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| fabrice rossi | 1 | 603 | 53.90 |
| Nathalie Villa-Vialaneix | 2 | 72 | 10.94 |