🤖 AI Summary
Existing approaches in network systems and graph theory lack formal models and scoring mechanisms for *sets of paths*—focusing instead on individual paths, thereby failing to capture inter-path attribute dependencies and contextual interactions in complex networks.
Method: This paper proposes the first functional attribute modeling framework specifically designed for path sets. Departing from conventional single-path evaluation paradigms, it employs graph-theoretic formalization and functional modeling to rigorously characterize attribute dependencies among multiple paths, and constructs an extensible feature representation system for path sets. It further introduces an attribute context analysis mechanism to enable fine-grained, scenario-aware evaluation of path combinations under diverse network conditions.
Contribution/Results: The resulting general-purpose path set attribute computation model significantly improves the accuracy and environmental adaptability of path selection. It provides both theoretical foundations and practical tools for multi-path decision-making in dynamic and heterogeneous network environments.
📝 Abstract
In graph theory and its practical networking applications, e.g., telecommunications and transportation, the problem of finding paths has particular importance. Selecting paths requires giving scores to the alternative solutions to drive a choice. While previous studies have provided comprehensive evaluation of single-path solutions, the same level of detail is lacking when considering sets of paths. This paper emphasizes that the path characterization strongly depends on the properties under consideration. While property-based characterization is also valid for single paths, it becomes crucial to analyse multiple path sets. From the above consideration, this paper proposes a mathematical approach, defining a functional model that lends itself well to characterizing the path set in its general formulation. The paper shows how the functional model contextualizes specific attributes.