Accelerating Skyline Path Enumeration with a Core Attribute Index on Multi-attribute Graphs

As a building block of many graph-based areas, the s-t path enumeration problem aims to find all paths between s and t by satisfying a given constraint, e.g., hop numbers. In many real-world scenarios, graphs are multi-attribute, where vertices and edges are associated with numerical attributes, such as expense or distance in road networks. However, existing methods have not fully leveraged all attributes in s-t path analysis. Hence, in this paper, we study the problem of skyline path enumeration, which aims to identify paths that balance multiple attributes, ensuring that no skyline result is dominated by another, thus meeting diverse user needs. To efficiently tackle this problem, we design a task-oriented core attribute index, called CAI, to rule out all redundant vertices and edges not located in any skyline path. Additionally, we introduce a hop-dependency label propagation strategy to construct the CAI index in parallel, improving the indexing process. Based on this index, we further design a CAI-based querying strategy that reduces fruitless explorations between candidate vertices not in the same skyline path, significantly optimizing query processing time. Experimental evaluations on fifteen real-world graphs show that CAI outperforms existing methods by up to four orders of magnitude in speed while demonstrating enhanced scalability and well-bound memory costs.