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Topic-Aware Latent Models for Representation Learning on Networks
Pattern Recognition Letters ( IF 5.1 ) Pub Date : 2021-01-14 , DOI: 10.1016/j.patrec.2021.01.006
Abdulkadir Çelikkanat , Fragkiskos D. Malliaros

Network representation learning (NRL) methods have received significant attention over the last years thanks to their success in several graph analysis problems, including node classification, link prediction and clustering. Such methods aim to map each vertex of the network into a low dimensional space in a way that the structural information of the network is preserved. Of particular interest are methods based on random walks; such methods transform the network into a collection of node sequences, aiming to learn node representations by predicting the context of each node within the sequence. In this paper, we introduce TNE, a generic framework to enhance the embeddings of nodes acquired by means of random walk-based approaches with topic-based information. Similar to the concept of topical word embeddings in Natural Language Processing, the proposed model first assigns each node to a latent community with the favor of various statistical graph models and community detection methods, and then learns the enhanced topic-aware representations. We evaluate our methodology in two downstream tasks: node classification and link prediction. The experimental results demonstrate that by incorporating node and community embeddings, we are able to outperform widely-known baseline NRL models.



中文翻译:

用于网络表示学习的主题感知潜在模型

近年来,网络表示学习(NRL)方法由于在一些图形分析问题(包括节点分类,链接预测和聚类)中的成功而受到了广泛的关注。这样的方法旨在以保留网络的结构信息的方式将网络的每个顶点映射到低维空间。特别有趣的是基于随机游走的方法。这种方法将网络转换为节点序列的集合,旨在通过预测序列中每个节点的上下文来学习节点表示。在本文中,我们介绍了TNE,这是一个通用框架,用于通过基于随机游走的方法与基于主题的信息来增强所获取节点的嵌入。与自然语言处理中的主题词嵌入概念相似,提出的模型首先借助各种统计图模型和社区检测方法将每个节点分配给一个潜在社区,然后学习增强的主题感知表示。我们在两个下游任务中评估我们的方法:节点分类和链接预测。实验结果表明,通过合并节点和社区嵌入,我们能够胜过众所周知的基准NRL模型。

更新日期:2021-01-14
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