对抗图对比学习：细粒度IP定位的ARIEL方法

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本文主要探讨了细粒度IP定位领域的最新研究进展，特别是对抗图对比学习（ARIEL）这一方法。对抗图对比学习是一种在无监督情况下提升图数据表示学习能力的有效策略，它将先前在图像领域广泛应用的数据增强技术扩展到了图数据上。然而，与图像数据不同，图数据的增强操作相对不直观，且生成高质量的对比样本对模型性能至关重要，这使得现有的图对比学习框架仍有很大的优化空间。 ARIEL方法的核心在于引入了两个关键组件：一是对抗性图视图，它模拟真实世界中的数据扰动，通过构造具有误导性的图变体来增强模型的鲁棒性和泛化能力；二是信息正则化，这是一种约束机制，确保在增强过程中不会丢失过多原始信息，从而保持了图结构的忠实度。通过这两者结合，ARIEL方法旨在提供一种简单但高效的方式来生成对比样本，提升图嵌入的质量，从而在细粒度IP定位等任务中取得更好的表现。作者们来自美国伊利诺伊大学厄巴纳-香槟分校和IBM研究实验室，他们提出的方法不仅关注于解决图数据特有的挑战，而且借鉴了对抗学习的理论，使之适用于图数据的特征学习。ARIEL的研究旨在填补当前图对比学习技术的空白，为后续的网络协议分析、安全防御以及地理位置预测等应用提供了新的思考方向和可能的技术改进。该工作对于理解如何在复杂网络数据上进行有效的自我监督学习具有重要意义，也为未来的对抗学习在图数据分析中的进一步发展奠定了基础。

Adversarial Graph Contrastive Learning

with Information Regularization

Shengyu Feng

shengyu8@illinois.edu

University of Illinois at Urbana-Champaign

USA

Baoyu Jing

baoyuj2@illinois.edu

University of Illinois at Urbana-Champaign

USA

Yada Zhu

yzhu@us.ibm.com

IBM Research

USA

Hanghang Tong

htong@illinois.edu

University of Illinois at Urbana-Champaign

USA

ABSTRACT

Contrastive learning is an eective unsupervised method in graph

representation learning. Recently, the data augmentation based con-

trastive learning method has been extended from images to graphs.

However, most prior works are directly adapted from the models

designed for images. Unlike the data augmentation on images, the

data augmentation on graphs is far less intuitive and much harder

to provide high-quality contrastive samples, which are the key to

the performance of contrastive learning models. This leaves much

space for improvement over the existing graph contrastive learning

frameworks. In this work, by introducing an adversarial graph view

and an information regularizer, we propose a simple but eective

method, Adversarial Graph Contrastive Learning (ArieL), to extract

informative contrastive samples within a reasonable constraint. It

consistently outperforms the current graph contrastive learning

methods in the node classication task over various real-world

datasets and further improves the robustness of graph contrastive

learning.

CCS CONCEPTS

• Mathematics of computing → Information theory

;

Graph

algorithms

;

• Computing methodologies → Neural networks

;

Learning latent representations.

KEYWORDS

graph representation learning, contrastive learning, adversarial

training, mutual information

ACM Reference Format:

Shengyu Feng, Baoyu Jing, Yada Zhu, and Hanghang Tong. 2022. Adversarial

Graph Contrastive Learning with Information Regularization. In Proceedings

of the ACM Web Conference 2022 (WWW ’22), April 25–29, 2022, Virtual Event,

Lyon, France. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/

3485447.3512183

Permission to make digital or hard copies of all or part of this work for personal or

classroom use is granted without fee provided that copies are not made or distributed

for prot or commercial advantage and that copies bear this notice and the full citation

on the rst page. Copyrights for components of this work owned by others than ACM

must be honored. Abstracting with credit is permitted. To copy otherwise, or republish,

to post on servers or to redistribute to lists, requires prior specic permission and/or a

fee. Request permissions from permissions@acm.org.

WWW ’22, April 25–29, 2022, Virtual Event, Lyon, France.

ACM ISBN 978-1-4503-9096-5/22/04.. . $15.00

https://doi.org/10.1145/3485447.3512183

1 INTRODUCTION

Contrastive learning is a widely used technique in various graph

representation learning tasks. In contrastive learning, the model

tries to minimize the distances among positive pairs and maximize

the distances among negative pairs in the embedding space. The

denition of positive and negative pairs is the key component

in contrastive learning. Earlier methods like DeepWalk [

] and

node2vec [

] dene positive and negative pairs based on the co-

occurrence of node pairs in the random walks. For knowledge graph

embedding, it is a common practice to dene positive and negative

pairs based on translations [2, 11, 18, 33, 34, 36].

Recently, the breakthroughs of contrastive learning in computer

vision have inspired some works to apply the similar ideas from

visual representation learning to graph representation learning.

To name a few, Deep Graph Infomax (DGI) [

] extends Deep In-

foMax [

] and achieves signicant improvements over previous

random-walk based methods. Graphical Mutual Information (GMI)

[

] uses the same framework as DGI but generalizes the concept

of mutual information from vector space to graph domain. Con-

trastive multi-view graph representation learning (referred to as

MVGRL in this paper) [

] further improves DGI by introducing

graph diusion into the contrastive learning framework. The more

recent works often follow the data augmentation based contrastive

learning methods [

], which treat the data augmented samples

from the same instance as positive pairs and dierent instances as

negative pairs. Graph Contrastive Coding (GCC) [

] uses random

walks with restart [

] to generate two subgraphs for each node

as two data augmented samples. Graph Contrastive learning with

Adaptive augmentation (GCA) [

] introduces an adaptive data

augmentation method which perturbs both the node features and

edges according to their importance, and it is trained in a similar

way as the famous visual contrastive learning framework SimCLR

[

]. Its preliminary work, which uses the uniform random sampling

rather than the adaptive sampling, is referred to as GRACE [

]

in this paper. Robinson et al. [

] proposes a way to select hard

negative samples based on the embedding space distances, and use

it to obtain high-quality graph embedding. There are also many

works [

] systemically studying the data augmentation on the

graphs.

However, unlike the transformations on images, the transforma-

tions on graphs are far less intuitive to human beings. The data

augmentation on the graph, could be either too similar to or totally

arXiv:2202.06491v1 [cs.LG] 14 Feb 2022

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