Higher-order dependencies for multi-step link prediction

Xiang Li

Chaos, Solitons & Fractals, 2025, vol. 200, issue P1

Abstract: Multi-step link prediction methods offer significant potential across diverse domains, including trajectory prediction, recommender systems, and evolutionary game theory. By capturing higher-order dependencies among nodes, these methods enhance the accuracy of multi-step link prediction. In this paper, we introduce a novel algorithm for multi-step link prediction that explicitly considers higher-order dependencies within networks. To achieve precise multi-step link prediction, we propose a higher-order dependency network model based on flow data, selectively converting higher-order dependencies among nodes into higher-order nodes along with corresponding edges, and next devise an efficient algorithm. The effectiveness of our approach is demonstrated through empirical flow datasets, and we further apply it in the context of journal recommender systems.

Keywords: Higher-order dependency; Multi-step link prediction; Higher-order dependency network; Journal recommender system (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077925009439
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:200:y:2025:i:p1:s0960077925009439

DOI: 10.1016/j.chaos.2025.116930

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().