Community search has aroused widespread interest in the past decades. Among existing solutions, the learning-based models exhibit outstanding performance in terms of accuracy by leveraging labels to 1) train the model for community score learning, and 2) select the optimal threshold for community identification. However, labeled data are not always available in real-world scenarios. To address this notable limitation of learning-based models, we propose a pre-trained graph Transformer based community search framework that uses Zero label (i.e., unsupervised), termed TransZero. TransZero has two key phases, i.e., the offline pre-training phase and the online search phase. Specifically, in the offline pretraining phase, we design an efficient and effective community search graph transformer (CSGphormer) to learn node representation. To pre-train CSGphormer without the usage of labels, we introduce two self-supervised losses, i.e., personalization loss and link loss, motivated by the inherent uniqueness of node and graph topology, respectively. In the online search phase, with the representation learned by the pre-trained CSGphormer, we compute the community score without using labels by measuring the similarity of representations between the query nodes and the nodes in the graph. To free the framework from the usage of a label-based threshold, we define a new function named expected score gain to guide the community identification process. Furthermore, we propose two efficient and effective algorithms for the community identification process that run without the usage of labels. Extensive experiments over 10 public datasets illustrate the superior performance of TransZero regarding both accuracy and efficiency.

翻译：社区搜索在过去几十年中引起了广泛关注。在现有解决方案中，基于学习的模型通过利用标签在以下两方面表现出卓越的准确性：1) 训练模型进行社区评分学习，2) 选择最优阈值进行社区识别。然而，在现实场景中标注数据并非总是可用。为解决基于学习模型这一显著局限性，我们提出了一种基于预训练图Transformer的社区搜索框架，该框架无需任何标签即可运行（即无监督），称为TransZero。TransZero包含两个关键阶段：离线预训练阶段和在线搜索阶段。具体而言，在离线预训练阶段，我们设计了一种高效且有效的社区搜索图Transformer（CSGphormer）来学习节点表示。为在无标签情况下预训练CSGphormer，我们引入了两种自监督损失：基于节点内在唯一性的个性化损失和基于图拓扑结构的链接损失。在在线搜索阶段，利用预训练CSGphormer学习到的表示，我们通过度量查询节点与图中节点表示的相似性，在无需标签的情况下计算社区评分。为使框架摆脱对基于标签阈值的依赖，我们定义了一个名为期望得分增益的新函数来指导社区识别过程。此外，我们提出了两种高效且有效的无标签社区识别算法。在10个公开数据集上的大量实验表明，TransZero在准确性和效率方面均具有优越性能。