Name
Abstract | ||
|---|---|---|
Interpersonal relations facilitate information flow and give rise to positional advantage of individuals in a social network. We ask the question: How would an individual build relations with members of a dynamic social network in order to arrive at a central position in the network? We formalize this question using the dynamic network building problem. Two strategies stand out to solve this problem: The first directs the individual to exploit their social proximity by linking to nodes that are close-by, while the second tries its best to explore distant regions of the network. We evaluate and contrast these two strategies with respect to edge- and distance-based cost metrics, as well as other structural properties such as embeddedness and clustering coefficient. Experiments are performed on models of dynamic random graphs and real-world data sets. We then discuss and test ways that combine these two strategies. |
Year | Venue | Field |
|---|---|---|
2017 | WISE | Dynamic network analysis,Information flow (information theory),Data mining,Social network,Random graph,Computer science,Centrality,Theoretical computer science,Exploit,Clustering coefficient,Interpersonal ties |
DocType | Citations | PageRank |
Conference | 2 | 0.40 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| bo yan | 1 | 35 | 15.11 |
| Yang Chen | 2 | 2 | 2.09 |
| Jiamou Liu | 3 | 49 | 23.19 |