论文标题
Cross-GCN：增强图形卷积网络，具有$ K $ - 订单功能交互
Cross-GCN: Enhancing Graph Convolutional Network with $k$-Order Feature Interactions
论文作者
论文摘要
图形卷积网络（GCN）是一种新兴技术，可在图形数据上进行学习和推理。它通过汇总邻居节点的特征以获取每个目标节点的嵌入来操作特征学习。由于具有强大的代表力，最近的研究表明，GCN在多个任务（例如建议和链接的文档分类）上实现了最先进的绩效。 尽管具有有效性，但我们认为GCN的现有设计放弃建模交叉功能，从而使GCN对交叉功能很重要的任务或数据效果降低。尽管神经网络可以近似任何连续功能，包括用于建模特征交叉的乘法运算符，但如果没有明确的设计，这样做可能会效率低下（即浪费许多参数，冒着过度拟合的风险）。 为此，我们设计了一个名为Cross-Feature Graph卷积的新运算符，该操作员明确地对具有复杂性线性的任意订单交叉特征建模，以特征维度和顺序大小。我们将我们提出的体系结构称为跨GCN，并在三个图上进行实验以验证其有效性。广泛的分析验证了GCN中显式建模横梁特征的实用性，尤其是在较低层的特征学习中。
Graph Convolutional Network (GCN) is an emerging technique that performs learning and reasoning on graph data. It operates feature learning on the graph structure, through aggregating the features of the neighbor nodes to obtain the embedding of each target node. Owing to the strong representation power, recent research shows that GCN achieves state-of-the-art performance on several tasks such as recommendation and linked document classification. Despite its effectiveness, we argue that existing designs of GCN forgo modeling cross features, making GCN less effective for tasks or data where cross features are important. Although neural network can approximate any continuous function, including the multiplication operator for modeling feature crosses, it can be rather inefficient to do so (i.e., wasting many parameters at the risk of overfitting) if there is no explicit design. To this end, we design a new operator named Cross-feature Graph Convolution, which explicitly models the arbitrary-order cross features with complexity linear to feature dimension and order size. We term our proposed architecture as Cross-GCN, and conduct experiments on three graphs to validate its effectiveness. Extensive analysis validates the utility of explicitly modeling cross features in GCN, especially for feature learning at lower layers.
