Graph Neural Networks For Complex Heterogeneous Graph Representation Learning

The Internet has penetrated into every aspect of social life and profoundly influenced the real-life experiences of contemporary people.In this process,massive amounts of data are constantly generated or produced,which are usually presented in complex heterogeneous graphs and are extremely valuable resources in the information age.Complex heterogeneous graph data can be seen everywhere in cyberspace and have extensive and important application value in various fields.To fully excavate and utilize the rich semantic information contained in such data to complete the cognition and reasoning of network content is a major practical demand facing society today.Heterogeneous graph representation learning is an effective solution for processing heterogeneous graph data currently.This technology learns the semantic information of the topological structure and node features of heterogeneous graphs,mapping different types of nodes and edges in the graph to low-dimensional vector spaces.The learned vector representations can support analysis and reasoning tasks in specific application scenarios.Due to its ability to naturally integrate node features and network structure information on graph networks,graph neural network technology has received extensive attention from researchers in heterogeneous graph representation learning.However,existing research still has some shortcomings when facing specific heterogeneous graph data.This paper focuses on exploring three typical complex heterogeneous graph data:categorical node feature graph,multi-layer heterogeneous graph,and multi-relational heterogeneous graph,and conducts in-depth research work on them.(Ⅰ)Categorical node feature graph representation learning with feature interaction modeling:Most existing research on the categorical node feature graph has neglected the interaction signals between discrete categorical features,reducing the quality of learned node representations.This paper proposes a method based on categorical feature interaction modeling to optimize initial node representations and enhance graph model performance.Specifically,this paper integrates two explicit interaction modeling strategies into the learning process of initial node representations,namely,local interaction modeling on each pair of categorical features and global interaction modeling on the artificial feature graph.Then,the enhanced initial node representations are improved using graph convolution based on neighborhood aggregation.In particular,this paper analyzes the theoretical advantages of the proposed global interaction modeling scheme in terms of computational efficiency and signal enhancement from the perspectives of spatial domain and spectral domain.Extensive experiments and analyses on multiple datasets show that the models proposed in this paper can effectively improve the quality of initial node representations and enhance performance in downstream tasks.(Ⅱ)Multi-layer heterogeneous graph representation learning with meta-path attention perception:The current solutions on the multi-layer heterogeneous graph are usually unable to naturally integrate and utilize the rich and diverse unsupervised information in such heterogeneous data.This paper proposes an algorithm based on metapath attention perception for representation learning of the multi-layer heterogeneous graph to fully integrate the complex semantic information on this type of heterogeneous graph data,while combining with micro-heterogeneous graph sampling strategies to adapt to representation learning and application in real large graph scenarios.The algorithm can effectively utilize the topological structure and node features of multi-layer heterogeneous graph data and learn and optimize node representations from limited data labels.This paper conducts experimental tests on a large-scale real e-commerce platform dataset combined with semi-supervised user profiling tasks,and verifies the positive effects of the proposed method.(Ⅲ)Multi-relational heterogeneous graph representation learning with high-order graph reasoning networks:Most existing research on representation learning for the multi-relational heterogeneous graph lacks consideration of sparsity in real-world scenarios and cannot provide interpretable prediction results,which may seriously harm their practical applications.This paper proposes using high-order reasoning components to learn endogenous correlations among relations to mine potential rule information and alleviate sparsity issues.Additionally,the relation-aware weight-free graph attentional aggregation mechanism is applied to achieve interpretable modeling on knowledge graphs.This approach can combine different types of scoring functions and effectively improve their performance in sparse scenarios while providing interpretability for prediction results to enhance model trustworthiness.Extensive experiments on multiple sparse knowledge graph datasets also demonstrate the effectiveness and rationality of the multi-relational heterogeneous graph representation learning scheme proposed in this paper.