Many real-world networks have signed relationships between the nodes. Identification of these relationships is an important aspect of decision making. The existing signed relationships in a network may impact the relationships between the other nodes, hence learning from the existing signed relationships in a network can be used for decision making in various mining tasks. These signed networks are getting attention in recent years due to their relevance to many applications such as categorization, recommendation, and relationship discovery in various domains for decision support such as biological, social network analysis, communication and making knowledge graphs. In this work, we focus on edge classification (sign/label prediction for edges) in unweighted and undirected signed networks where the task is to predict the label of the unlabeled edges. Edge classification is a challenging problem as in real-world signed networks, edges are scarcely labeled. In our work, we are using labeled edges to predict the sign of unlabeled edges (classification) with the help of structural information. In this work, we have proposed a novel framework named NPECF for the classification of unlabeled edges. The proposed framework is novel in its way of utilizing the existing information in the signed network to predict the label of unlabeled edges. The utilization of the unlabeled edges in NPECF using three spanning subgraph projections of the given network minimizes the information loss. The experiments have been performed on four real-world datasets from different domains to demonstrate the effectiveness of the proposed framework.

许多现实世界的网络已经在节点之间建立了签名关系。识别这些关系是决策的重要方面。网络中现有的已签名关系可能会影响其他节点之间的关系，因此从网络中的现有已签名关系中学习可以用于各种挖掘任务中的决策。这些已签名的网络由于与许多应用相关，例如分类，推荐和各种领域中的关系发现，以提供决策支持，如生物学，社交网络分析，交流和制作知识图谱，因此近年来受到关注。在这项工作中 我们专注于未加权和无向签名网络中的边缘分类（边缘的符号/标签预测），其中的任务是预测未标记边缘的标签。边缘分类是一个具有挑战性的问题，因为在现实世界中，签名网络几乎没有标记边缘。在我们的工作中，我们使用标记的边缘借助结构信息来预测未标记的边缘的符号（分类）。在这项工作中，我们提出了一个名为NPECF的新颖框架，用于未标记边缘的分类。所提出的框架在利用已签名网络中的现有信息来预测未标记边缘的标签方面具有新颖性。使用给定网络的三个跨子图投影来利用NPECF中未标记的边缘，可以最大程度地减少信息丢失。