Graph Convolutional Networks (GCNs) are a popular method from graph representation learning that have proved effective for tasks like node classification tasks. Although typical GCN models focus on classifying nodes within a static graph, several recent variants propose node classification in dynamic graphs whose topologies and node attributes change over time, e.g., social networks with dynamic relationships, or literature citation networks with changing co-authorships. These works, however, do not fully address the challenge of flexibly assigning different importance to snapshots of the graph at different times, which depending on the graph dynamics may have more or less predictive power on the labels. We address this challenge by proposing a new method, GCN-SE, that attaches a set of learnable attention weights to graph snapshots at different times, inspired by Squeeze and Excitation Net (SE-Net). We show that GCN-SE outperforms previously proposed node classification methods on a variety of graph datasets. To verify the effectiveness of the attention weight in determining the importance of different graph snapshots, we adapt perturbation-based methods from the field of explainable machine learning to graphical settings and evaluate the correlation between the attention weights learned by GCN-SE and the importance of different snapshots over time. These experiments demonstrate that GCN-SE can in fact identify different snapshots' predictive power for dynamic node classification.
翻译:虽然典型的GCN模型侧重于在静态图形中对节点进行分类,但最近的一些变量提议在动态图形中进行节点分类,其表层和节点属性随时间而变化,例如,具有动态关系的社交网络或具有不断变化的共同作者的文献引用网络。然而,这些工作并不完全解决在不同时间灵活地将不同重要性赋予图形的快照,视图形动态在标签上可能具有或多或少的预测力。我们通过提出一种新的方法,即GCN-SE来应对这一挑战,该方法对不同时间的图形截图进行一套可学习的注意权重,这种图表截图面图因时间的变化而变化,例如,具有动态关系的社交网络,或具有不断变化的共同作者关系的文献引用网络。我们显示,GCN-SE在不同的图表数据集中,没有完全地反映在确定不同图形相貌相貌相貌相近性照片重要性时的注意度,我们没有根据GSESE所学的图形化模型模型模型和G-SESE所学的模型模型模型,这些对不同时间性模型的重要性作出不同的解释。