A Statistical Model for Social Network Labeling
Bibliographic Data
| ID | 19418921 |
|---|---|
| Authors | Danyang Huang (0000-0002-4053-2680, Renmin University of China), Jun Yin (0000-0002-2009-6068, Peking University), Tao Shi (0000-0003-1907-3552, The Ohio State University), Hansheng Wang (0000-0003-2386-0209, Peking University) |
| Year | 2016 |
| Volume | 34 |
| Issue | 3 |
| Pages | 368-374 |
| Publication date | 2016-07-02 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2015.1039014 |
| OpenAlex | W2407556697 |
| Language | EN |
| References cited | 14 |
We consider a social network from which one observes not only network structure (i.e., nodes and edges) but also a set of labels (or tags, keywords) for each node (or user). These labels are self-created and closely related to the user’s career status, life style, personal interests, and many others. Thus, they are of great interest for online marketing. To model their joint behavior with network structure, a complete data model is developed. The model is based on the classical p1 model but allows the reciprocation parameter to be label-dependent. By focusing on connected pairs only, the complete data model can be generalized into a conditional model. Compared with the complete data model, the conditional model specifies only the conditional likelihood for the connected pairs. As a result, it suffers less risk from model misspecification. Furthermore, because the conditional model involves connected pairs only, the computational cost is much lower. The resulting estimator is consistent and asymptotically normal. Depending on the network sparsity level, the convergence rate could be different. To demonstrate its finite sample performance, numerical studies (based on both simulated and real datasets) are presented
Data set · Estimator · Mathematical optimization · Network model · Rate of convergence · Social media · Statistical model · Statistics · Bayesian Methods and Mixture Models · Complex Network Analysis Techniques · Computer Science · Mathematics · Statistical Methods and Inference · Artificial Intelligence
Networks
Social Network Analysis
Social Network Analysis
Estimation and Prediction for Stochastic Blockstructures
Ergm
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
An Exponential Family of Probability Distributions for Directed Graphs
Inference in Curved Exponential Family Models for Networks
Probabilistic topic models
Network models for social selection processes
Recent developments in exponential random graph (p*) models for social networks
| Highly cited | No |
|---|