Multi-scale compositionality: identifying the compositional structures of social dynamics using deep learning.

ABSTRACT:

Objective

Social media exhibit rich yet distinct temporal dynamics which cover a wide range of different scales. In order to study this complex dynamics, two fundamental questions revolve around (1) the signatures of social dynamics at different time scales, and (2) the way in which these signatures interact and form higher-level meanings.

Method

In this paper, we propose the Recursive Convolutional Bayesian Model (RCBM) to address both of these fundamental questions. The key idea behind our approach consists of constructing a deep-learning framework using specialized convolution operators that are designed to exploit the inherent heterogeneity of social dynamics. RCBM's runtime and convergence properties are guaranteed by formal analyses.

Results

Experimental results

SUBMITTER: Peng HK 

PROVIDER: S-EPMC4382120 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature