Year: 2024
Type: conference-paper
Abstract: 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.
Authors: Yang Zheng, Yang Yu, Yue Zhang, Shanshan Lin
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4403728283