Modeling the Interplay Between Individual Behavior and Network Distributions

TL;DR

This paper introduces M3D, a comprehensive framework that models how individual actions influence group behaviors and network degree distributions across macro, meso, and micro levels, with applications to social media and citation networks.

Contribution

The paper presents a unified, multi-level framework for understanding the formation of network distributions from individual actions, supported by theoretical analysis and empirical validation.

Findings

M3D effectively models heavy-tailed network distributions.

The framework predicts group behavior formation with high accuracy.

M3D outperforms alternative methods by up to 30% in prediction tasks.

Abstract

It is well-known that many networks follow a power-law degree distribution; however, the factors that influence the formation of their distributions are still unclear. How can one model the connection between individual actions and network distributions? How can one explain the formation of group phenomena and their evolutionary patterns? In this paper, we propose a unified framework, M3D, to model human dynamics in social networks from three perspectives: macro, meso, and micro. At the micro-level, we seek to capture the way in which an individual user decides whether to perform an action. At the meso-level, we study how group behavior develops and evolves over time, based on individual actions. At the macro-level, we try to understand how network distributions such as power-law (or heavy-tailed phenomena) can be explained by group behavior. We provide theoretical analysis for the proposed framework, and discuss the connection of our framework with existing work. The framework offers a new, flexible way to explain the interplay between individual user actions and network distributions, and can benefit many applications. To model heavy-tailed distributions from partially observed individual actions and to predict the formation of group behaviors, we apply M3D to three different genres of networks: Tencent Weibo, Citation, and Flickr. We also use information-burst prediction as a particular application to quantitatively evaluate the predictive power of the proposed framework. Our results on the Weibo indicate that M3D's prediction performance exceeds that of several alternative methods by up to 30\%.