Name
Title | ||
|---|---|---|
Two local dissimilarity measures for weighted graphs with application to protein interaction networks |
Abstract | ||
|---|---|---|
We extend the Czekanowski-Dice dissimilarity measure, classically used to cluster the vertices of unweighted graphs, to weighted
ones. The first proposed formula corresponds to edges weighted by a probability of existence. The second one is adapted to
edges weighted by intensity or strength. We show on simulated graphs that the class identification process is improved by
computing weighted compared to unweighted edges. Finally, an application to a drosophila protein network illustrates the fact
that using these new formulas improves the ’biological accuracy’ of partitioning. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/s11634-008-0018-3 | Adv. Data Analysis and Classification |
Keywords | Field | DocType |
graph distance · graph partitioning · heuristic optimisation · biological networks,biological network,graph partitioning | Discrete mathematics,Graph,Combinatorics,Protein Interaction Networks,Vertex (geometry),Biological network,Distance,Graph partition,Clustering coefficient,Multiple edges,Mathematics | Journal |
Volume | Issue | ISSN |
2 | 1 | 1862-5355 |
Citations | PageRank | References |
5 | 0.51 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jean-baptiste Angelelli | 1 | 11 | 0.91 |
| Anaïs Baudot | 2 | 107 | 6.83 |
| Christine Brun | 3 | 169 | 11.90 |
| A. Guénoche | 4 | 229 | 41.64 |