Name
Abstract | ||
|---|---|---|
In social networks, edges often form closed triangles or triads. Standard approaches to measuring triadic closure, however, fail for multi-edge networks, because they do not consider that triads can be formed by edges of different multiplicity. We propose a novel measure of triadic closure for multi-edge networks based on a shared partner statistic and demonstrate that this measure can detect meaningful closure in synthetic and empirical multi-edge networks, where conventional approaches fail. This work is a cornerstone in driving inferential network analyses from the analysis of binary networks towards the analyses of multi-edge and weighted networks, which offer a more realistic representation of social interactions and relations.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3341161.3342926 | ASONAM '19: International Conference on Advances in Social Networks Analysis and Mining
Vancouver
British Columbia
Canada
August, 2019 |
Keywords | Field | DocType |
multi-edge networks, triadic closure, network inference, social networks, statistical learning | Social network,Statistic,Computer science,Triadic closure,Theoretical computer science,Artificial intelligence,Machine learning,Binary number | Journal |
Volume | ISSN | ISBN |
abs/1905.02990 | 2473-9928 | 978-1-4503-6868-1 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Laurence Brandenberger | 1 | 0 | 0.34 |
| Giona Casiraghi | 2 | 0 | 1.01 |
| Vahan Nanumyan | 3 | 2 | 1.07 |
| Frank Schweitzer | 4 | 1 | 0.69 |