GRATIS：具有特定于任务的拓扑和多维边缘特征的深度学习图表示
GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge Features
摘要：
Graph对于表示各种类型的真实世界数据非常强大。图的拓扑结构（边的存在）和边的特征决定了图中顶点之间的消息传递机制。虽然大多数现有方法仅手动定义单值边来描述一对顶点之间的连接性或关联强度，但这种手动定义的拓扑和单值边特征可能会忽略任务特定的和关键的关系线索。在本文中，我们提出了第一个通用的图表示学习框架（称为GRATIS），它可以生成一个强大的图表示与任务特定的拓扑结构和任务特定的多维边缘功能，从任何任意输入。为了学习每个边的存在和多维特征，我们的框架考虑了相应的顶点对及其全局上下文信息，使生成的图表示具有针对不同下游任务的全局最优消息传递机制。对11个图和非图数据集的各种图分析任务的原则性调查结果表明，我们的GRATIS不仅可以大大增强预定义的图，而且还可以学习非图数据的强大图表示，所有任务的性能都有明显的提高。特别是，学习的拓扑结构和多维边缘特征为图形分析任务提供了与任务相关的补充线索。我们的框架是有效的，健壮的和灵活的，是一个即插即用的模块，可以与不同的骨干和图神经网络（GNN）相结合，从各种图形和非图形数据生成特定于任务的图形表示。我们的代码可在https://github.com/SSYSteve/Learning-Graph-Representation-with-Task-specific-Topology-and-Multi-dimensional-Edge-Features上公开获取。
英文摘要：
Graph is powerful for representing various types of real-world data. The topology (edges' presence) and edges' features of a graph decides the message passing mechanism among vertices within the graph. While most existing approaches only manually define a single-value edge to describe the connectivity or strength of association between a pair of vertices, task-specific and crucial relationship cues may be disregarded by such manually defined topology and single-value edge features. In this paper, we propose the first general graph representation learning framework (called GRATIS) which can generate a strong graph representation with a task-specific topology and task-specific multi-dimensional edge features from any arbitrary input. To learn each edge's presence and multi-dimensional feature, our framework takes both of the corresponding vertices pair and their global contextual information into consideration, enabling the generated graph representation to have a globally optimal message passing mechanism for different down-stream tasks. The principled investigation results achieved for various graph analysis tasks on 11 graph and non-graph datasets show that our GRATIS can not only largely enhance pre-defined graphs but also learns a strong graph representation for non-graph data, with clear performance improvements on all tasks. In particular, the learned topology and multi-dimensional edge features provide complementary task-related cues for graph analysis tasks. Our framework is effective, robust and flexible, and is a plug-and-play module that can be combined with different backbones and Graph Neural Networks (GNNs) to generate a task-specific graph representation from various graph and non-graph data. Our code is made publicly available at https://github.com/SSYSteve/Learning-Graph-Representation-with-Task-specific-Topology-and-Multi-dimensional-Edge-Features.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
