Graph-based data present unique challenges and opportunities for machine
learning. Graph Neural Networks (GNNs), and especially those algorithms that
capture graph topology through message passing for neighborhood aggregation,
have been a leading solution. However, these networks often require substantial
computational resources and may not optimally leverage the information
contained in the graph's topology, particularly for large-scale or complex
graphs. We propose Topology Coordinate Neural Network (TCNN) and Directional
Virtual Coordinate Neural Network (DVCNN) as novel and efficient alternatives
to message passing GNNs, that directly leverage the graph's topology,
sidestepping the computational challenges presented by competing algorithms.
Our proposed methods can be viewed as a reprise of classic techniques for graph
embedding for neural network feature engineering, but they are novel in that
our embedding techniques leverage ideas in Graph Coordinates (GC) that are
lacking in current practice. Experimental results, benchmarked against the Open
Graph Benchmark Leaderboard, demonstrate that TCNN and DVCNN achieve
competitive or superior performance to message passing GNNs. For similar levels
of accuracy and ROC-AUC, TCNN and DVCNN need far fewer trainable parameters
than contenders of the OGBN Leaderboard. The proposed TCNN architecture
requires fewer parameters than any neural network method currently listed in
the OGBN Leaderboard for both OGBN-Proteins and OGBN-Products datasets.
Conversely, our methods achieve higher performance for a similar number of
trainable parameters. By providing an efficient and effective alternative to
message passing GNNs, our work expands the toolbox of techniques for
graph-based machine learning.

提出了一种基于图拓扑的神经网络方法作为信息传递 GNN 的高效替代方法，实验结果表明该方法在准确性和 ROC-AUC 的相似水平下，相较于信息传递 GNN，需要较少的可训练参数，同时在 OGBN-Proteins 和 OGBN-Products 数据集上实现了更高的性能。

图坐标与传统神经网络：图神经网络的替代方案

Graph Coordinates and Conventional Neural Networks -- An Alternative for  Graph Neural Networks

Generating text from graph-based data, such as Abstract Meaning
Representation (AMR), is a challenging task due to the inherent difficulty in
how to properly encode the structure of a graph with labeled edges. To address
this difficulty, we propose a novel graph-to-sequence model that encodes
different but complementary perspectives of the structural information
contained in the AMR graph. The model learns parallel top-down and bottom-up
representations of nodes capturing contrasting views of the graph. We also
investigate the use of different node message passing strategies, employing
different state-of-the-art graph encoders to compute node representations based
on incoming and outgoing perspectives. In our experiments, we demonstrate that
the dual graph representation leads to improvements in AMR-to-text generation,
achieving state-of-the-art results on two AMR datasets.

提出了一种新的基于图的序列模型，能够有效地对 AMR 图进行编码，借助不同的顶部和底部节点表示以及节点传递策略，实现了更好的文本生成表现，并在两个 AMR 数据集上达到了最先进的结果。

用双图表示增强 AMR 到文本生成

Enhancing AMR-to-Text Generation with Dual Graph Representations

Exploiting fine-grained semantic features on point cloud is still challenging
due to its irregular and sparse structure in a non-Euclidean space. Among
existing studies, PointNet provides an efficient and promising approach to
learn shape features directly on unordered 3D point cloud and has achieved
competitive performance. However, local feature that is helpful towards better
contextual learning is not considered. Meanwhile, attention mechanism shows
efficiency in capturing node representation on graph-based data by attending
over neighboring nodes. In this paper, we propose a novel neural network for
point cloud, dubbed GAPNet, to learn local geometric representations by
embedding graph attention mechanism within stacked Multi-Layer-Perceptron (MLP)
layers. Firstly, we introduce a GAPLayer to learn attention features for each
point by highlighting different attention weights on neighborhood. Secondly, in
order to exploit sufficient features, a multi-head mechanism is employed to
allow GAPLayer to aggregate different features from independent heads. Thirdly,
we propose an attention pooling layer over neighbors to capture local signature
aimed at enhancing network robustness. Finally, GAPNet applies stacked MLP
layers to attention features and local signature to fully extract local
geometric structures. The proposed GAPNet architecture is tested on the
ModelNet40 and ShapeNet part datasets, and achieves state-of-the-art
performance in both shape classification and part segmentation tasks.

本研究基于图形注意力机制，提出了一个新的神经网络 GAPNet，用于学习点云中的局部几何表示，并且在形状分类和部分分割任务中，取得了最先进的性能。