Multitask Representation Learning With Multiview Graph Convolutional Networks
Multitask Representation Learning With Multiview Graph Convolutional Networks
复制标题
使用多视图图卷积网络进行多任务表示学习
DOI：
10.1109/tnnls.2020.3036825
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发表时间：
2022-03-01
影响因子：
10.4
通讯作者：
Jin, Hai
中科院分区：
文献类型：
--
作者：
Huang, Hong;Song, Yu;Jin, Hai
链接预测和节点分类是网络表示学习的两个重要下游任务。现有的方法已经取得了可接受的结果，但他们分别执行这两个任务，这需要大量的重复工作，忽略了任务之间的相关性。此外，传统的模型遭受的多个视图的信息相同的处理，因此，他们无法学习鲁棒的表示下游的任务。为此，我们在本文中通过多任务多视图学习同时解决链接预测和节点分类问题。我们首先解释多任务多视图学习的可行性和优势，这两个任务。然后，我们提出了一种新的模型MT-MVGCN同时执行链路预测和节点分类任务。更具体地说，我们设计了一个多视图图卷积网络来提取网络中多个视图的丰富信息，这些信息由不同的任务共享。我们进一步应用两种注意机制：视图注意机制和任务注意机制，使视图和任务调整视图融合过程。此外，视图重建可以作为一个辅助任务，以提高所提出的模型的性能。在真实网络数据集上的实验表明，我们的模型是有效的，并且在这两个任务中优于高级基线。
Link prediction and node classification are two important downstream tasks of network representation learning. Existing methods have achieved acceptable results but they perform these two tasks separately, which requires a lot of duplication of work and ignores the correlations between tasks. Besides, conventional models suffer from the identical treatment of information of multiple views, thus they fail to learn robust representation for downstream tasks. To this end, we tackle link prediction and node classification problems simultaneously via multitask multiview learning in this article. We first explain the feasibility and advantages of multitask multiview learning for these two tasks. Then we propose a novel model named MT-MVGCN to perform link prediction and node classification tasks simultaneously. More specifically, we design a multiview graph convolutional network to extract abundant information of multiple views in a network, which is shared by different tasks. We further apply two attention mechanisms: view the attention mechanism and task attention mechanism to make views and tasks adjust the view fusion process. Moreover, view reconstruction can be introduced as an auxiliary task to boost the performance of the proposed model. Experiments on real-world network data sets demonstrate that our model is efficient yet effective, and outperforms advanced baselines in these two tasks.