Multi-View Heterogeneous HyperGNN for Heterophilic Knowledge Combination Prediction

Huijie Liu, Shulan Ruan, Han Wu, Zhenya Huang, Defu Lian, Qi Liu, Enhong Chen

Abstract: Knowledge combination prediction involves analyzing current knowledge elements and their relationships, then forecasting how these elements, drawn from various fields, can be creatively combined to form new, innovative solutions. This process is critical for countries and businesses to understand future technology trends and promote innovation in an era of rapid scientific and technological advancement. Existing methods often overlook the integration of knowledge combinations from multiple views, along with their inherent heterophily and the dual “many-to-one” property, where a single knowledge combination can include multiple elements, and a single element may belong to various combinations. To this end, we propose a novel framework named Multi-view Heterogeneous HyperGNN for Heterophilic Knowledge Combination Prediction (H3KCP). Specifically, H3KCP first constructs a hypergraph reflecting the dual “many-to-one” property of knowledge combinations, where each hyperedge may contain several nodes and each node can also belong to multiple hyperedges. Next, the framework employs a multi-view fusion approach to model knowledge combinations, considering heterophily and integrating insights from co-occurrence, co-citation, and hierarchical structure-based views. Furthermore, our analysis of H3KCP from a spectral graph perspective offers insights into its rationality. Finally, extensive experiments on real-world patent datasets and the Open Academic Graph dataset validate the effectiveness and efficiency of our approach, yielding significant insights into knowledge combinations.