Graph Neural Networks (GNNs) have achieved state-of-the-art performance in
representation learning for graphs recently. However, the effectiveness of
GNNs, which capitalize on the key operation of message propagation, highly
depends on the quality of the topology structure. Most of the graphs in
real-world scenarios follow a long-tailed distribution on their node degrees,
that is, a vast majority of the nodes in the graph are tail nodes with only a
few connected edges. GNNs produce inferior node representations for tail nodes
since they lack structural information. In the pursuit of promoting the
expressiveness of GNNs for tail nodes, we explore how the deficiency of
structural information deteriorates the performance of tail nodes and propose a
general Structural Augmentation based taIL nOde Representation learning
framework, dubbed as SAILOR, which can jointly learn to augment the graph
structure and extract more informative representations for tail nodes.
Extensive experiments on public benchmark datasets demonstrate that SAILOR can
significantly improve the tail node representations and outperform the
state-of-the-art baselines.

利用结构增强的基于长尾分布的图神经网络模型（SAILOR）可以改善尾节点表达并超越现有基准模型。

SAILOR: 结构增强的基于尾节点表示学习

SAILOR: Structural Augmentation Based Tail Node Representation Learning

In this paper, we solve the sample shortage problem in the human parsing
task. We begin with the self-learning strategy, which generates pseudo-labels
for unlabeled data to retrain the model. However, directly using noisy
pseudo-labels will cause error amplification and accumulation. Considering the
topology structure of human body, we propose a trainable graph reasoning method
that establishes internal structural connections between graph nodes to correct
two typical errors in the pseudo-labels, i.e., the global structural error and
the local consistency error. For the global error, we first transform
category-wise features into a high-level graph model with coarse-grained
structural information, and then decouple the high-level graph to reconstruct
the category features. The reconstructed features have a stronger ability to
represent the topology structure of the human body. Enlarging the receptive
field of features can effectively reducing the local error. We first project
feature pixels into a local graph model to capture pixel-wise relations in a
hierarchical graph manner, then reverse the relation information back to the
pixels. With the global structural and local consistency modules, these errors
are rectified and confident pseudo-labels are generated for retraining.
Extensive experiments on the LIP and the ATR datasets demonstrate the
effectiveness of our global and local rectification modules. Our method
outperforms other state-of-the-art methods in supervised human parsing tasks.

本文提出了一种可训练的图推理方法，用于解决解析人体时的样本缺乏问题，并且在 LIP 和 ATR 数据集上得到了显著提高。