Modern data analysis pipelines are becoming increasingly complex due to the presence of multi-view information sources. While graphs are effective in modeling complex relationships, in many scenarios a single graph is rarely sufficient to succinctly represent all interactions, and hence multi-layered graphs have become popular. Though this leads to richer representations, extending solutions from the single-graph case is not straightforward. Consequently, there is a strong need for novel solutions to solve classical problems, such as node classification, in the multi-layered case. In this paper, we consider the problem of semi-supervised learning with multi-layered graphs. Though deep network embeddings, e.g. DeepWalk, are widely adopted for community discovery, we argue that feature learning with random node attributes, using graph neural networks, can be more effective. To this end, we propose to use attention models for effective feature learning, and develop two novel architectures, GrAMME-SG and GrAMME-Fusion, that exploit the inter-layer dependencies for building multi-layered graph embeddings. Using empirical studies on several benchmark datasets, we evaluate the proposed approaches and demonstrate significant performance improvements in comparison to state-of-the-art network embedding strategies. The results also show that using simple random features is an effective choice, even in cases where explicit node attributes are not available.
翻译:由于存在多视角信息来源,现代数据分析管道变得日益复杂。虽然图表在建模复杂关系中是有效的,但在许多设想中,单图很少足以简洁地代表所有互动,因此多层图变得很受欢迎。虽然这会导致更丰富的表达,但从单一图表案例中扩大解决方案并非直截了当。因此,在多层次案例中,非常需要新颖的解决方案来解决传统问题,如节点分类。在本文中,我们考虑了以多层次图表进行半监督的学习的问题。尽管深网络嵌入(如DeepWalk)被广泛用于社区发现,但我们认为,使用随机节点特性学习,使用图形神经网络,可能更加有效。为此,我们提议使用关注模型有效特征学习,并开发两种新颖架构(GramME-SG)和 GrAMME-Fusion),利用跨层次的相互依存来建立多层图嵌入。我们甚至使用一些基准数据集的经验研究(例如Deeple Walk),我们用随机的特性来评估这些模式的学习效果。我们还要用简单的嵌入式模型来展示一个显著的成绩。