A Unified View on Neural Message Passing with Opinion Dynamics for Social Networks
Outongyi Lv, Bingxin Zhou, Jing Wang, Xiang Xiao, Weishu Zhao, Lirong, Zheng

TL;DR
This paper introduces ODNet, a novel neural message passing framework inspired by opinion dynamics, which improves graph learning, reduces oversmoothing, and effectively analyzes social and biological networks.
Contribution
It harmonizes sociometric opinion dynamics with neural message passing, proposing ODNet with bounded confidence to enhance social network analysis and graph representation learning.
Findings
ODNet improves prediction accuracy across various graph types.
It alleviates oversmoothing in neural message passing.
Successfully analyzes real-world metabolic gene networks.
Abstract
Social networks represent a common form of interconnected data frequently depicted as graphs within the domain of deep learning-based inference. These communities inherently form dynamic systems, achieving stability through continuous internal communications and opinion exchanges among social actors along their social ties. In contrast, neural message passing in deep learning provides a clear and intuitive mathematical framework for understanding information propagation and aggregation among connected nodes in graphs. Node representations are dynamically updated by considering both the connectivity and status of neighboring nodes. This research harmonizes concepts from sociometry and neural message passing to analyze and infer the behavior of dynamic systems. Drawing inspiration from opinion dynamics in sociology, we propose ODNet, a novel message passing scheme incorporating bounded…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsOpinion Dynamics and Social Influence · Complex Network Analysis Techniques · Mental Health Research Topics
MethodsALIGN
