Community Search in Time-dependent Road-social Attributed Networks

TL;DR

This paper introduces a local community search method in time-dependent road-social attributed networks that considers both keywords and locations, using novel algorithms and language models to improve semantic and spatial cohesiveness.

Contribution

It proposes the semantic-spatial aware k-core problem and develops exact and greedy algorithms that expand locally from a query node, incorporating large language models for better keyword similarity.

Findings

Greedy algorithm outperforms baselines in cohesiveness measures

Local algorithms reduce unnecessary network traversal

Language models improve keyword similarity assessment

Abstract

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.