Recently, contrastive learning (CL) has emerged as a successful method for
unsupervised graph representation learning. Most graph CL methods first perform
stochastic augmentation on the input graph to obtain two graph views and
maximize the agreement of representations in the two views. Despite the
prosperous development of graph CL methods, the design of graph augmentation
schemes -- a crucial component in CL -- remains rarely explored. We argue that
the data augmentation schemes should preserve intrinsic structures and
attributes of graphs, which will force the model to learn representations that
are insensitive to perturbation on unimportant nodes and edges. However, most
existing methods adopt uniform data augmentation schemes, like uniformly
dropping edges and uniformly shuffling features, leading to suboptimal
performance. In this paper, we propose a novel graph contrastive representation
learning method with adaptive augmentation that incorporates various priors for
topological and semantic aspects of the graph. Specifically, on the topology
level, we design augmentation schemes based on node centrality measures to
highlight important connective structures. On the node attribute level, we
corrupt node features by adding more noise to unimportant node features, to
enforce the model to recognize underlying semantic information. We perform
extensive experiments of node classification on a variety of real-world
datasets. Experimental results demonstrate that our proposed method
consistently outperforms existing state-of-the-art baselines and even surpasses
some supervised counterparts, which validates the effectiveness of the proposed
contrastive framework with adaptive augmentation.

本文提出了一种新的自适应增强方法，通过设计基于节点中心性和节点属性的增强策略来保留图的内在结构和属性信息，并验证此方法在节点分类任务中优于现有方法和监督学习模型。

自适应增强的图形对比学习

Graph Contrastive Learning with Adaptive Augmentation

Graph embedding techniques have been increasingly deployed in a multitude of
different applications that involve learning on non-Euclidean data. However,
existing graph embedding models either fail to incorporate node attribute
information during training or suffer from node attribute noise, which
compromises the accuracy. Moreover, very few of them scale to large graphs due
to their high computational complexity and memory usage. In this paper we
propose GraphZoom, a multi-level framework for improving both accuracy and
scalability of unsupervised graph embedding algorithms. GraphZoom first
performs graph fusion to generate a new graph that effectively encodes the
topology of the original graph and the node attribute information. This fused
graph is then repeatedly coarsened into much smaller graphs by merging nodes
with high spectral similarities. GraphZoom allows any existing embedding
methods to be applied to the coarsened graph, before it progressively refine
the embeddings obtained at the coarsest level to increasingly finer graphs. We
have evaluated our approach on a number of popular graph datasets for both
transductive and inductive tasks. Our experiments show that GraphZoom can
substantially increase the classification accuracy and significantly accelerate
the entire graph embedding process by up to 40.8x, when compared to the
state-of-the-art unsupervised embedding methods.

本文介绍了一种名为 GraphZoom 的多层框架，旨在提高无监督图嵌入算法的准确性和可扩展性。该方法利用节点属性和图拓扑信息进行图合并，并通过多次缩小并简化合并的图，将现有嵌入方法应用于合并后的图，从而在多种图数据集上达到更高的分类准确性和更快的处理速度。