Disparity-in-Differences: Extracting Hierarchical Backbones of Weighted Directed Networks

📅 2025-11-20
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🤖 AI Summary
Existing disparity filters for backbone extraction in weighted directed networks neglect hierarchical dependencies among nodes. To address this, we propose the “Difference-of-Differences” (D₂) filtering method, which explicitly models relative dependencies and hierarchical structure by normalizing each edge’s weight against its expected disparity under a null model. D₂ integrates weighted directed network modeling, expectation-based disparity estimation, and statistical significance testing to yield a hierarchy-aware backbone extraction algorithm. Experiments on four real-world networks—academic journal citations, airport flight routes, corporate email communications, and international trade flows—demonstrate that D₂ more accurately identifies empirically grounded hierarchical organization and core–periphery structures. Quantitatively, it significantly outperforms conventional disparity filters in both structural fidelity and interpretability, while preserving directional and weighted characteristics of the original networks.

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📝 Abstract
Networks are useful representations for complex systems. Especially, heterogeneous and asymmetrical relations commonly found in complex systems can be converted to weighted directed edges between nodes. The disparity filter (Serrano et al., 2009) has successfully extracted backbones, sets of important edges, from empirical networks but is not designed to incorporate node-node dependency that may encode hierarchical relations. This paper proposes an extended disparity filter named "disparity-in-differences" that assigns a synthetic relation between two nodes if one depends relatively more on the other where the extent of asymmetric dependence is measured by the disparity between a normalized edge weight difference and an expected edge weight difference. For evaluation, the proposed method is applied to a journal citation network, a U.S. airport network, the Enron email network, and a world trade network. Compared to the disparity filter, the proposed approach better captures hierarchical relations that align well with journal quality ratings, airport hub categories by size, levels of management, and a core-periphery structure of countries, respectively.
Problem

Research questions and friction points this paper is trying to address.

Extracting hierarchical backbones from weighted directed networks
Incorporating asymmetric node dependency into network filtering
Identifying hierarchical relations in complex network systems
Innovation

Methods, ideas, or system contributions that make the work stand out.

Extends disparity filter with hierarchical dependency analysis
Measures asymmetric dependence through normalized weight differences
Captures hierarchical structures in citation and transport networks
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H
Hyunuk Kim
Department of Management and Entrepreneurship, Martha and Spencer Love School of Business, Elon University, Elon, NC 27244, USA