Hu Ganglin, Pang Jun, Mo Xian
College of Computer & Information Science, Centre for Research and Innovation in Software Engineering, Southwest University, Chongqing, Chongqing, China.
Faculty of Science, Technology and Medicine & Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg, Luxembourg.
PeerJ Comput Sci. 2022 Jul 8;8:e1030. doi: 10.7717/peerj-cs.1030. eCollection 2022.
Network embedding has shown its effectiveness in many tasks, such as link prediction, node classification, and community detection. Most attributed network embedding methods consider topological features and attribute features to obtain a node embedding but ignore its implicit information behavior features, including information inquiry, interaction, and sharing. These can potentially lead to ineffective performance for downstream applications. In this article, we propose a novel network embedding framework, named information behavior extraction (IBE), that incorporates nodes' topological features, attribute features, and information behavior features within a joint embedding framework. To design IBE, we use an existing embedding method (., SDNE, CANE, or CENE) to extract a node's topological features and attribute features into a basic vector. Then, we propose a topic-sensitive network embedding (TNE) model to extract a node's information behavior features and eventually generate information behavior feature vectors. In our TNE model, we design an importance score rating algorithm (ISR), which considers both effects of the topic-based community of a node and its interaction with adjacent nodes to capture the node's information behavior features. Eventually, we concatenate a node's information behavior feature vector with its basic vector to get its ultimate joint embedding vector. Extensive experiments demonstrate that our method achieves significant and consistent improvements compared to several state-of-the-art embedding methods on link prediction.
网络嵌入在许多任务中都显示出了有效性，如链接预测、节点分类和社区检测。大多数属性网络嵌入方法考虑拓扑特征和属性特征来获得节点嵌入，但忽略了其隐含的信息行为特征，包括信息查询、交互和共享。这些可能会导致下游应用的性能不佳。在本文中，我们提出了一种新颖的网络嵌入框架，名为信息行为提取（IBE），它在一个联合嵌入框架中融合了节点的拓扑特征、属性特征和信息行为特征。为了设计IBE，我们使用现有的嵌入方法（如SDNE、CANE或CENE）将节点的拓扑特征和属性特征提取到一个基本向量中。然后，我们提出了一个主题敏感网络嵌入（TNE）模型来提取节点的信息行为特征，并最终生成信息行为特征向量。在我们的TNE模型中，我们设计了一个重要性评分算法（ISR），该算法同时考虑节点基于主题的社区的影响及其与相邻节点的交互，以捕捉节点的信息行为特征。最终，我们将节点的信息行为特征向量与其基本向量连接起来，得到其最终的联合嵌入向量。大量实验表明，与几种用于链接预测的最先进嵌入方法相比，我们的方法取得了显著且一致的改进。
