Under circumstances of heterophily, where nodes with different labels tend to
be connected based on semantic meanings, Graph Neural Networks (GNNs) often
exhibit suboptimal performance. Current studies on graph heterophily mainly
focus on aggregation calibration or neighbor extension and address the
heterophily issue by utilizing node features or structural information to
improve GNN representations. In this paper, we propose and demonstrate that the
valuable semantic information inherent in heterophily can be utilized
effectively in graph learning by investigating the distribution of neighbors
for each individual node within the graph. The theoretical analysis is carried
out to demonstrate the efficacy of the idea in enhancing graph learning. Based
on this analysis, we propose HiGNN, an innovative approach that constructs an
additional new graph structure, that integrates heterophilous information by
leveraging node distribution to enhance connectivity between nodes that share
similar semantic characteristics. We conduct empirical assessments on node
classification tasks using both homophilous and heterophilous benchmark
datasets and compare HiGNN to popular GNN baselines and SoTA methods,
confirming the effectiveness in improving graph representations. In addition,
by incorporating heterophilous information, we demonstrate a notable
enhancement in existing GNN-based approaches, and the homophily degree across
real-world datasets, thus affirming the efficacy of our approach.

在异质性的情况下，图神经网络（GNNs）往往表现出次优的性能。本文提出并证明了异质性中宝贵的语义信息可以通过研究每个节点在图中的邻居分布来有效地利用于图学习，进而构建了一种新的图结构 HiGNN，该结构通过利用节点分布来增强具有相似语义特征的节点之间的连接性。通过在节点分类任务上的实证评估和与其他方法比较，我们验证了 HiGNN 在改进图表示方面的有效性，并证明通过整合异质性信息可以显著提升现有基于 GNN 的方法，以及在真实世界数据集中的同质性程度，从而证明了我们方法的功效。

异质友善信息增强图神经网络

Learn from Heterophily: Heterophilous Information-enhanced Graph Neural  Network

This paper addresses the task of Unmanned Aerial Vehicles (UAV) visual
geo-localization, which aims to match images of the same geographic target
taken by different platforms, i.e., UAVs and satellites. In general, the key to
achieving accurate UAV-satellite image matching lies in extracting visual
features that are robust against viewpoint changes, scale variations, and
rotations. Current works have shown that part matching is crucial for UAV
visual geo-localization since part-level representations can capture image
details and help to understand the semantic information of scenes. However, the
importance of preserving semantic characteristics in part-level representations
is not well discussed. In this paper, we introduce a transformer-based adaptive
semantic aggregation method that regards parts as the most representative
semantics in an image. Correlations of image patches to different parts are
learned in terms of the transformer's feature map. Then our method decomposes
part-level features into an adaptive sum of all patch features. By doing this,
the learned parts are encouraged to focus on patches with typical semantics.
Extensive experiments on the University-1652 dataset have shown the superiority
of our method over the current works.

这篇论文介绍了一种基于 Transformer 的自适应语义聚集方法，将部分作为图像中最具代表性的语义，并将部分级别的特征分解为所有补丁特征的自适应总和，以实现准确的无人机 - 卫星图像匹配。