Link prediction, which consists of predicting edges based on graph features,
is a fundamental task in many graph applications. As for several related
problems, Graph Neural Networks (GNNs), which are based on an attribute-centric
message-passing paradigm, have become the predominant framework for link
prediction. GNNs have consistently outperformed traditional topology-based
heuristics, but what contributes to their performance? Are there simpler
approaches that achieve comparable or better results? To answer these
questions, we first identify important limitations in how GNN-based link
prediction methods handle the intrinsic class imbalance of the problem -- due
to the graph sparsity -- in their training and evaluation. Moreover, we propose
Gelato, a novel topology-centric framework that applies a topological heuristic
to a graph enhanced by attribute information via graph learning. Our model is
trained end-to-end with an N-pair loss on an unbiased training set to address
class imbalance. Experiments show that Gelato is 145% more accurate, trains 11
times faster, infers 6,000 times faster, and has less than half of the
trainable parameters compared to state-of-the-art GNNs for link prediction.

本文提出了一个新的以拓扑为中心的框架 Gelato，该框架使用了一个基于图学习的拓扑启发式方法，对由属性信息增强的图进行预测。通过与现有的基于图神经网络的方法进行比较，实验表明这个框架训练更快、推断更快、更精确，可用于解决连接预测中的类别不平衡问题。

不使用图神经网络的链路预测

Link Prediction without Graph Neural Networks

Most existing studies on learning local features focus on the patch-based
descriptions of individual keypoints, whereas neglecting the spatial relations
established from their keypoint locations. In this paper, we go beyond the
local detail representation by introducing context awareness to augment
off-the-shelf local feature descriptors. Specifically, we propose a unified
learning framework that leverages and aggregates the cross-modality contextual
information, including (i) visual context from high-level image representation,
and (ii) geometric context from 2D keypoint distribution. Moreover, we propose
an effective N-pair loss that eschews the empirical hyper-parameter search and
improves the convergence. The proposed augmentation scheme is lightweight
compared with the raw local feature description, meanwhile improves remarkably
on several large-scale benchmarks with diversified scenes, which demonstrates
both strong practicality and generalization ability in geometric matching
applications.

本文提出了一种加强现有局部特征描述器的上下文感知统一学习框架，该框架整合了来自高级图像表征的视觉上下文和 2D 关键点分布的几何上下文，并采用有效的 N 对损失来提高收敛效果，在几个大型基准场景上表现良好，表明它在几何匹配应用方面具有很强的实用性和泛化能力。