An activity-driven temporal multilayer network framework to support consensus in group decision making

doi:10.1016/j.cie.2025.111736

2025-12-01

PRE

Zijian Ling *

Youlong Yang

An Huang

DOI：10.1016/j.cie.2025.111736

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摘要

En 中文

Social network group decision-making (SNGDM) provides valuable support for describing the opinion exchange in the decision-making process by using the connected social relationships among decision makers (DMs). With the expansion of social media, DMs are interconnected through various types of links. In this cases, interaction of DMs are no longer confined to single-type binary relationships but exhibit complex multiplexing and high-order dynamic characteristics. To this end, this study develops a consensus model based on multilayer network for improving the reliability of decision-making. First, we construct an attributed multilayer network by utilizing multiple social relationships and decision information, in which attributes serve as auxiliary information to establish additional exotic connectivity patterns. Then, the natural interaction of DMs shows a specific high-order correlation, where some activities occurring over the links of a layer depend on the dynamics of certain links on other layers. We propose an interactive joint random walk model to map this co-evolution into an activity-driven network dynamics process. To accurately capture hidden collective structure, state-based non-columnar communities and physical-based overlapping communities are detected. The reinforcement effects generated in these two types of communities can identify influential nodes and communities, guiding decision aggregation to reach higher consensus level. Finally, a numerical example is presented, and simulation experiments and comparative analysis are performed to validate the effectiveness and superiority of proposed model.

Keyword:

Social network group decision-making

Multilayer network

Collaborative analysis

Community detection

High-order network dynamics