Name
Abstract | ||
|---|---|---|
In directed networks, reciprocal links have dramatic effects on dynamical processes, network growth, and higher-order structures such as motifs and communities. While the reciprocity of binary networks has been extensively studied, that of weighted networks is still poorly understood, implying an ever-increasing gap between the availability of weighted network data and our understanding of their dyadic properties. Here we introduce a general approach to the reciprocity of weighted networks, and define quantities and null models that consistently capture empirical reciprocity patterns at different structural levels. We show that, counter-intuitively, previous reciprocity measures based on the similarity of mutual weights are uninformative. By contrast, our measures allow to consistently classify different weighted networks according to their reciprocity, track the evolution of a network's reciprocity over time, identify patterns at the level of dyads and vertices, and distinguish the effects of flux (im) balances or other (a) symmetries from a true tendency towards (anti-) reciprocation. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1038/srep02729 | SCIENTIFIC REPORTS |
Keywords | Field | DocType |
bioinformatics,biomedical research | Reciprocal,Vertex (geometry),Computer science,Theoretical computer science,Weighted network,Artificial intelligence,Reciprocity (social psychology),Homogeneous space,Binary number | Journal |
Volume | ISSN | Citations |
3 | 2045-2322 | 18 |
PageRank | References | Authors |
1.04 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tiziano Squartini | 1 | 67 | 11.86 |
| Francesco Picciolo | 2 | 31 | 3.55 |
| Franco Ruzzenenti | 3 | 38 | 5.36 |
| Diego Garlaschelli | 4 | 90 | 18.49 |