Graph is an important data representation which occurs naturally in the real
world applications \cite{goyal2018graph}. Therefore, analyzing graphs provides
users with better insights in different areas such as anomaly detection
\cite{ma2021comprehensive}, decision making \cite{fan2023graph}, clustering
\cite{tsitsulin2023graph}, classification \cite{wang2021mixup} and etc.
However, most of these methods require high levels of computational time and
space. We can use other ways like embedding to reduce these costs. Knowledge
graph (KG) embedding is a technique that aims to achieve the vector
representation of a KG. It represents entities and relations of a KG in a
low-dimensional space while maintaining the semantic meanings of them. There
are different methods for embedding graphs including random walk-based methods
such as node2vec, metapath2vec and regpattern2vec. However, most of these
methods bias the walks based on a rigid pattern usually hard-coded in the
algorithm. In this work, we introduce \textit{subgraph2vec} for embedding KGs
where walks are run inside a user-defined subgraph. We use this embedding for
link prediction and prove our method has better performance in most cases in
comparison with the previous ones.

在本研究中，我们介绍了一种名为 subgraph2vec 的知识图谱嵌入方法，通过在用户定义的子图内运行 walks，用于链接预测，并证明我们的方法在大多数情况下具有更好的性能。

基于随机游走的知识图谱嵌入算法：Subgraph2vec

Subgraph2vec: A random walk-based algorithm for embedding knowledge  graphs

In this paper, we present subgraph2vec, a novel approach for learning latent
representations of rooted subgraphs from large graphs inspired by recent
advancements in Deep Learning and Graph Kernels. These latent representations
encode semantic substructure dependencies in a continuous vector space, which
is easily exploited by statistical models for tasks such as graph
classification, clustering, link prediction and community detection.
subgraph2vec leverages on local information obtained from neighbourhoods of
nodes to learn their latent representations in an unsupervised fashion. We
demonstrate that subgraph vectors learnt by our approach could be used in
conjunction with classifiers such as CNNs, SVMs and relational data clustering
algorithms to achieve significantly superior accuracies. Also, we show that the
subgraph vectors could be used for building a deep learning variant of
Weisfeiler-Lehman graph kernel. Our experiments on several benchmark and
large-scale real-world datasets reveal that subgraph2vec achieves significant
improvements in accuracies over existing graph kernels on both supervised and
unsupervised learning tasks. Specifically, on two realworld program analysis
tasks, namely, code clone and malware detection, subgraph2vec outperforms
state-of-the-art kernels by more than 17% and 4%, respectively.

本文介绍了 subgraph2vec，一种用于从大型图形中学习根节点子图的潜在表示的新方法，该方法受到深度学习和图形核方法的最新进展的启发。我们证明了我们的方法能够与 CNNs、SVMs 和关系数据聚类算法等分类器相结合，从而实现显著优越的准确度。此外，我们还展示了子图向量可以用于构建深度学习 Weisfeiler-Lehman 图形核的变体。我们对多个基准和大规模真实世界数据集进行的实验表明，subgraph2vec 在监督和无监督学习任务中都显着提高了图形核方法的准确性。