Name
Abstract | ||
|---|---|---|
Two concepts of centrality have been defined in complex networks. The first considers the centrality of a node and many different metrics for it have been defined (e.g. eigenvector centrality, PageRank, non-backtracking centrality, etc). The second is related to large scale organization of the network, the core-periphery structure, composed by a dense core plus an outlying and loosely-connected periphery. In this paper we investigate the relation between these two concepts. We consider networks generated via the stochastic block model, or its degree corrected version, with a core-periphery structure and we investigate the centrality properties of the core nodes and the ability of several centrality metrics to identify them. We find that the three measures with the best performance are marginals obtained with belief propagation, PageRank, and degree centrality, while non-backtracking and eigenvector centrality (or MINRES [10], showed to be equivalent to the latter in the large network limit) perform worse in the investigated networks. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1088/1742-5468/2016/02/023401 | JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT |
Keywords | Field | DocType |
message-passing algorithms,random graphs,networks,clustering techniques | Network science,Discrete mathematics,Random walk closeness centrality,Quantum mechanics,Network controllability,Centrality,Theoretical computer science,Betweenness centrality,Complex network,Network theory,Katz centrality,Mathematics | Journal |
Volume | Issue | ISSN |
abs/1510.01116 | 2 | 1742-5468 |
Citations | PageRank | References |
2 | 0.40 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paolo Barucca | 1 | 11 | 3.76 |
| Daniele Tantari | 2 | 15 | 2.78 |
| Fabrizio Lillo | 3 | 41 | 10.66 |