Dynamic Graph Neural Networks with Temporal Knowledge Distillation for Evolving Social Influence in Decentralized Online Communities

Abstract

The social impact of decentralised online communities, such as blockchain-based social networks, is complex because their decentralisation allows users to exercise greater freedom and independence. A novel Dynamic Graph Neural Network with Temporal Knowledge Distillation (DGNN-TKD) is proposed to model and predict influence patterns. DGNN-TKD differs from standard Graph Neural Networks (GNNs), which function under the assumption of static graphs. It tracks the temporal evolution of a graph, and introduces a knowledge distillation mechanism that enables the transfer of influence embeddings over time. We propose a novel multi-dimensional influence metric that captures agent reputation, engagement and trust, supplemented with robust attention-based temporal aggregation. In experiments on decentralized social network datasets, DGNN-TKD surpasses current dynamic GNNs in influence prediction, community detection, and misinformation detection in decentralized governance/Web3 applications. This framework connects graph-based learning and social dynamics and serves as a powerful tool to study decentralized phenomena.