### Abstract

Original language | English |
---|---|

Journal | Social Network Analysis and Mining |

Publication status | Published - 22 Apr 2011 |

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### Keywords

- cs.SI
- physics.soc-ph

### Cite this

*Social Network Analysis and Mining*.

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*Social Network Analysis and Mining*.

**Internal links and pairs as a new tool for the analysis of bipartite complex networks.** / Allali, Oussama; Tabourier, Lionel; Magnien, Clémence; Latapy, Matthieu.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Internal links and pairs as a new tool for the analysis of bipartite complex networks

AU - Allali, Oussama

AU - Tabourier, Lionel

AU - Magnien, Clémence

AU - Latapy, Matthieu

PY - 2011/4/22

Y1 - 2011/4/22

N2 - Many real-world complex networks are best modeled as bipartite (or 2-mode) graphs, where nodes are divided into two sets with links connecting one side to the other. However, there is currently a lack of methods to analyze properly such graphs as most existing measures and methods are suited to classical graphs. A usual but limited approach consists in deriving 1-mode graphs (called projections) from the underlying bipartite structure, though it causes important loss of information and data storage issues. We introduce here internal links and pairs as a new notion useful for such analysis: it gives insights on the information lost by projecting the bipartite graph. We illustrate the relevance of theses concepts on several real-world instances illustrating how it enables to discriminate behaviors among various cases when we compare them to a benchmark of random networks. Then, we show that we can draw benefit from this concept for both modeling complex networks and storing them in a compact format.

AB - Many real-world complex networks are best modeled as bipartite (or 2-mode) graphs, where nodes are divided into two sets with links connecting one side to the other. However, there is currently a lack of methods to analyze properly such graphs as most existing measures and methods are suited to classical graphs. A usual but limited approach consists in deriving 1-mode graphs (called projections) from the underlying bipartite structure, though it causes important loss of information and data storage issues. We introduce here internal links and pairs as a new notion useful for such analysis: it gives insights on the information lost by projecting the bipartite graph. We illustrate the relevance of theses concepts on several real-world instances illustrating how it enables to discriminate behaviors among various cases when we compare them to a benchmark of random networks. Then, we show that we can draw benefit from this concept for both modeling complex networks and storing them in a compact format.

KW - cs.SI

KW - physics.soc-ph

M3 - Article

JO - Social Network Analysis and Mining

JF - Social Network Analysis and Mining

SN - 1869-5450

ER -