discrete temporal models of social networks

• DOI: 10.48550/arxiv.0908.1258
• Publication Date: 2010-01-01
• Authors: Hanneke, Steve; Fu, Wenjie; Xing, Eric P.
• Keywords: FOS: Computer and information sciences; 05 social sciences; Machine Learning (stat.ML); 01 natural sciences; 0506 political science; Methodology (stat.ME); FOS: Psychology; Statistics - Machine Learning; 170203 Knowledge Representation and Machine Learning; 0101 mathematics; Statistics - Methodology

We propose a family of statistical models for social network evolution over time, which represents an extension of Exponential Random Graph Models (ERGMs). Many of the methods for ERGMs are readily adapted for these models, including maximum likelihood estimation algorithms. We discuss models of this type and their properties, and give examples, as well as a demonstration of their use for hypothesis testing and classification. We believe our temporal ERG models represent a useful new framework for modeling time-evolving social networks, and rewiring networks from other domains such as gene regulation circuitry, and communication networks.