We propose the Line Graph Normalized Least Mean Square (LGNLMS) algorithm for
online time-varying graph edge signals prediction. LGNLMS utilizes the Line
Graph to transform graph edge signals into the node of its edge-to-vertex dual.
This enables edge signals to be processed using established GSP concepts
without redefining them on graph edges.

我们提出了一种线图归一化最小均方（LGNLMS）算法，用于在线时间变化图边缘信号预测。LGNLMS 利用线图将图边缘信号转换为其边缘到节点的对偶。这使得边缘信号能够使用已建立的图形信号处理概念进行处理，无需在图边缘上重新定义它们。

通过线图转换对图边进行在线信号估计

Online Signal Estimation on the Graph Edges via Line Graph  Transformation

This work aims to tackle the challenging heterogeneous graph encoding problem
in the text-to-SQL task. Previous methods are typically node-centric and merely
utilize different weight matrices to parameterize edge types, which 1) ignore
the rich semantics embedded in the topological structure of edges, and 2) fail
to distinguish local and non-local relations for each node. To this end, we
propose a Line Graph Enhanced Text-to-SQL (LGESQL) model to mine the underlying
relational features without constructing meta-paths. By virtue of the line
graph, messages propagate more efficiently through not only connections between
nodes, but also the topology of directed edges. Furthermore, both local and
non-local relations are integrated distinctively during the graph iteration. We
also design an auxiliary task called graph pruning to improve the
discriminative capability of the encoder. Our framework achieves
state-of-the-art results (62.8% with Glove, 72.0% with Electra) on the
cross-domain text-to-SQL benchmark Spider at the time of writing.

本研究提出了一种针对 text-to-SQL 任务中具有挑战性的异构图编码问题的 LGESQL 模型，通过线图增强策略挖掘底层的关系特征，同时设计了一个名为图修剪的辅助任务，提高编码器的判别能力，在跨领域的文本到 SQL 测试 Spider 上取得了最先进的结果。