Name
Abstract | ||
|---|---|---|
We study the extent to which the formation of a two-way relationship can be predicted in a dynamic social network. A two-way (called reciprocal) relationship, usually developed from a one-way (parasocial) relationship, represents a more trustful relationship between people. Understanding the formation of two-way relationships can provide us insights into the micro-level dynamics of the social network, such as what is the underlying community structure and how users influence each other. Employing Twitter as a source for our experimental data, we propose a learning framework to formulate the problem of reciprocal relationship prediction into a graphical model. The framework incorporates social theories into a machine learning model. We demonstrate that it is possible to accurately infer 90% of reciprocal relationships in a dynamic network. Our study provides strong evidence of the existence of the structural balance among reciprocal relationships. In addition, we have some interesting findings, e.g., the likelihood of two "elite" users creating a reciprocal relationships is nearly 8 times higher than the likelihood of two ordinary users. More importantly, our findings have potential implications such as how social structures can be inferred from individuals' behaviors. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/2063576.2063740 | CIKM |
Keywords | Field | DocType |
social structure,social network,two-way relationship,dynamic network,social theory,reciprocal relationship,reciprocal relationship prediction,trustful relationship,graphical model,dynamic social network,community structure,social influence,predictive model,prediction model,machine learning | Social theory,Data mining,Reciprocal,Social network,Computer science,Cognitive psychology,Social influence,Social structure,Artificial intelligence,Dynamic network analysis,Structural balance,Graphical model,Machine learning | Conference |
Citations | PageRank | References |
96 | 3.22 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| John Hopcroft | 1 | 4245 | 1836.70 |
| Tiancheng Lou | 2 | 455 | 19.49 |
| Jie Tang | 3 | 5871 | 300.22 |