Abstract: With the rapid rise of short videos on digital platforms,micro-video popularity prediction(MVPP) has become an important research area.Short videos contain rich multimodal content,including video frames,text,and social network interaction data,all of which significantly influence their popularity.However,existing methods have two main shortcomings:they typically rely only on the multimodal content features of the short video itself and fail to effectively model the complex social network structure information formed by user interactions(such as comments,likes,and shares);when handling large-scale social multimodal graphs,existing graph learning methods often lead to the loss of valuable multimodal signals due to neighbor sampling strategies.To address these shortcomings,this paper proposes a novel approach－DHSGL(Dual-Stream Heterogeneous Social Graph Learning Framework).The core innovation of this method lies in:1)proposing an efficient graph learning pre-calculation strategy,which aggregates the complete graph structure information through a single global propagation and constructs unimodal graphs to preserve the original modality features,thus significantly reducing information loss;2)constructing a social multimodal graph that integrates social interactions and multimodal content to fully leverage the neglected social structure information.Experimental results demonstrate a significant improvement in prediction performance,validating the effectiveness of integrating social network structure with multimodal content to enhance micro-video popularity prediction.

Key words: Micro-video popularity prediction, Multimodal fusion, Social network, Heterogeneousgraph neural networks