  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.
