Pre-trained Prompt-driven Community Search

The "pre-train, prompt" paradigm is widely adopted in various graph-based tasks and has shown promising performance in community detection. Most existing semi-supervised community detection algorithms detect communities based on known ones, and the detected communities typically do not contain the given query node. Therefore, they are not suitable for searching the community of a given node. Motivated by this, we adopt this paradigm into the semi-supervised community search for the first time and propose Pre-trained Prompt-driven Community Search (PPCS), a novel model designed to enhance search accuracy and efficiency. PPCS consists of three main components: node encoding, sample generation, and prompt-driven fine-tuning. Specifically, the node encoding component employs graph neural networks to learn local structural patterns of nodes in a graph, thereby obtaining representations for nodes and communities. Next, the sample generation component identifies an initial community for a given node and selects known communities that are structurally similar to the initial one as training samples. Finally, the prompt-driven fine-tuning component leverages these samples as prompts to guide the final community prediction. Experimental results on five real-world datasets demonstrate that PPCS performs better than baseline algorithms. It also achieves higher community search efficiency than semi-supervised community search baseline methods, with ablation studies verifying the effectiveness of each component of PPCS.

Community Search in Time-dependent Road-social Attributed Networks

Real-world networks often involve both keywords and locations, along with travel time variations between locations due to traffic conditions. However, most existing cohesive subgraph-based community search studies utilize a single attribute, either keywords or locations, to identify communities. They do not simultaneously consider both keywords and locations, which results in low semantic or spatial cohesiveness of the detected communities, and they fail to account for variations in travel time. Additionally, these studies traverse the entire network to build efficient indexes, but the detected community only involves nodes around the query node, leading to the traversal of nodes that are not relevant to the community. Therefore, we propose the problem of discovering semantic-spatial aware k-core, which refers to a k-core with high semantic and time-dependent spatial cohesiveness containing the query node. To address this problem, we propose an exact and a greedy algorithm, both of which gradually expand outward from the query node. They are local methods that only access the local part of the attributed network near the query node rather than the entire network. Moreover, we design a method to calculate the semantic similarity between two keywords using large language models. This method alleviates the disadvantages of keyword-matching methods used in existing community search studies, such as mismatches caused by differently expressed synonyms and the presence of irrelevant words. Experimental results show that the greedy algorithm outperforms baselines in terms of structural, semantic, and time-dependent spatial cohesiveness.