论文标题
自动关系感知图形网络增殖
Automatic Relation-aware Graph Network Proliferation
论文作者
论文摘要
图形神经体系结构搜索引发了很多关注，因为图形神经网络（GNN）在许多关系任务中表现出强大的推理能力。但是，当前使用的图形搜索空间过于强调学习节点特征，而忽略了采矿等级关系信息。此外，由于消息传递中的各种机制，图形搜索空间比CNN大得多。这阻碍了经典搜索策略的直接应用，以探索复杂的图形搜索空间。我们提出了自动关系感知的图形网络增殖（ARGNP），以有效地搜索具有关系引导的消息传递机制的GNN。具体而言，我们首先设计了一个新颖的双重关系图形搜索空间，该搜索空间既包括节点和关系学习操作。这些操作可以提取分层节点/关系信息，并为传递图形的消息提供各向异性指导。其次，类似于细胞增殖，我们设计了一个网络增殖搜索范式，以逐步确定GNN体系结构，通过迭代执行网络分裂和分化。四个图形学习任务的六个数据集的实验表明，我们方法生产的GNN优于当前最新的手工制作和基于搜索的GNN。代码可在https://github.com/phython96/argnp上找到。
Graph neural architecture search has sparked much attention as Graph Neural Networks (GNNs) have shown powerful reasoning capability in many relational tasks. However, the currently used graph search space overemphasizes learning node features and neglects mining hierarchical relational information. Moreover, due to diverse mechanisms in the message passing, the graph search space is much larger than that of CNNs. This hinders the straightforward application of classical search strategies for exploring complicated graph search space. We propose Automatic Relation-aware Graph Network Proliferation (ARGNP) for efficiently searching GNNs with a relation-guided message passing mechanism. Specifically, we first devise a novel dual relation-aware graph search space that comprises both node and relation learning operations. These operations can extract hierarchical node/relational information and provide anisotropic guidance for message passing on a graph. Second, analogous to cell proliferation, we design a network proliferation search paradigm to progressively determine the GNN architectures by iteratively performing network division and differentiation. The experiments on six datasets for four graph learning tasks demonstrate that GNNs produced by our method are superior to the current state-of-the-art hand-crafted and search-based GNNs. Codes are available at https://github.com/phython96/ARGNP.
