论文标题
图形解密：一个透明的双发音图神经网络，以了解节点分类的消息通话机制
Graph Decipher: A transparent dual-attention graph neural network to understand the message-passing mechanism for the node classification
论文作者
论文摘要
图形神经网络可以有效地应用于在广泛不同领域的许多实际问题上找到解决方案。图形神经网络的成功链接到图表上的消息通信机制，但是，在大多数算法中，消息 - 聚集行为仍然不清楚。为了提高功能性，我们提出了一个名为Graph Decipher的新的透明网络，以通过在两个主要组件中进行优先级来调查消息通话机制：图形结构和节点属性，图形，特征和节点分类任务下图上图上的全局级别。但是，计算负担现在成为最重要的问题，因为图结构和节点属性的相关性都是在图上计算的。为了解决此问题，仅通过图形特征过滤器提取相关的代表节点属性，允许以类别为导向的方式执行计算。七个数据集的实验表明，图形解密会达到最新的性能，同时在节点分类任务下施加了较低的计算负担。此外，由于我们的算法能够按类别探索代表性节点属性，因此它可用于减轻多级图数据集中的不平衡节点分类问题。
Graph neural networks can be effectively applied to find solutions for many real-world problems across widely diverse fields. The success of graph neural networks is linked to the message-passing mechanism on the graph, however, the message-aggregating behavior is still not entirely clear in most algorithms. To improve functionality, we propose a new transparent network called Graph Decipher to investigate the message-passing mechanism by prioritizing in two main components: the graph structure and node attributes, at the graph, feature, and global levels on a graph under the node classification task. However, the computation burden now becomes the most significant issue because the relevance of both graph structure and node attributes are computed on a graph. In order to solve this issue, only relevant representative node attributes are extracted by graph feature filters, allowing calculations to be performed in a category-oriented manner. Experiments on seven datasets show that Graph Decipher achieves state-of-the-art performance while imposing a substantially lower computation burden under the node classification task. Additionally, since our algorithm has the ability to explore the representative node attributes by category, it is utilized to alleviate the imbalanced node classification problem on multi-class graph datasets.
