A Novel Multi-view Hypergraph Adaptive Fusion Approach for Representation Learning

The power systems have generated a large amount of multi-view data with the advancement of electrical equipments. Graph-based multi-view methods boost the performance across diverse power systems, via aggregating inter-view and intra-view topological structures of data, which gains greatly attention. However, they focus on pairwise relationships between data to mine potential patterns, which is difficult to capture complex high-order correlations between data. To this end, a novel multi-view hypergraph adaptive fusion approach (MHAF) is proposed for representation learning in various systems, which contains the multi-view hypergraph modelling module and the adaptive correlation aggregation module. The former constructs hypergraphs from different views to capture different high-order relationship among social systems. The later dynamically weights and fuses different views via employing an attention aggregation mechanism, which creates a more expressive fused representation. Two components work seamlessly together to capture the complex patterns of multi-view data, enhancing the system's ability to understand user behavior and provide more personalized guidance. Numerous experiments on LastFM and FilmTrust demonstrate the superiority and advancement of MHAF over baselines.