RSEA-MVGNN：具有可靠结构增强和聚合的多视图图神经网络
RSEA-MVGNN: Multi-View Graph Neural Network with Reliable Structural Enhancement and Aggregation
AI总结：
本文提出RSEA-MVGNN，通过主观逻辑估计视图不确定性，实现可靠结构增强和视图质量评估，提升多视图图神经网络的特征聚合效果。
摘要：
图神经网络（GNNs）在学习多视图图数据方面表现出显著成效。在多视图图神经网络框架中，关键挑战在于有效结合不同视图，每个视图具有不同的图结构特征（GSFs）。现有方法主要集中在两个方面：1）优先考虑最重要的GSFs，2）利用GNN进行特征聚合。然而，优先考虑最重要的GSFs可能导致特征多样性受限，现有基于GNN的聚合策略同等对待每个视图而不考虑视图质量。为解决这些问题，我们提出了一种新的多视图图神经网络，具有可靠的结构增强和聚合（RSEA-MVGNN）。首先，我们利用主观逻辑估计视图特定的不确定性。基于此不确定性，我们设计了可靠的结构增强算法，通过特征去相关算法。这种方法使每个增强过程专注于不同的GSFs，从而在增强结构中实现多样的特征表示。其次，模型学习视图特定的信念和不确定性作为观点，这些观点用于评估视图质量。基于这些观点，模型使高质量的视图主导GNN聚合，从而促进表示学习。在五个真实世界数据集上进行的实验结果表明，RSEA-MVGNN优于几种最先进的基于GNN的方法。
英文摘要：
Graph Neural Networks (GNNs) have exhibited remarkable efficacy in learning from multi-view graph data. In the framework of multi-view graph neural networks, a critical challenge lies in effectively combining diverse views, where each view has distinct graph structure features (GSFs). Existing approaches to this challenge primarily focus on two aspects: 1) prioritizing the most important GSFs, 2) utilizing GNNs for feature aggregation. However, prioritizing the most important GSFs can lead to limited feature diversity, and existing GNN-based aggregation strategies equally treat each view without considering view quality. To address these issues, we propose a novel Multi-View Graph Neural Network with Reliable Structural Enhancement and Aggregation (RSEA-MVGNN). Firstly, we estimate view-specific uncertainty employing subjective logic. Based on this uncertainty, we design reliable structural enhancement by feature de-correlation algorithm. This approach enables each enhancement to focus on different GSFs, thereby achieving diverse feature representation in the enhanced structure. Secondly, the model learns view-specific beliefs and uncertainty as opinions, which are utilized to evaluate view quality. Based on these opinions, the model enables high-quality views to dominate GNN aggregation, thereby facilitating representation learning. Experimental results conducted on five real-world datasets demonstrate that RSEA-MVGNN outperforms several state-of-the-art GNN-based methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
