← 研踪 PaperTrailarXiv:2611.16692 · v1
Theory-Driven Network Backbones for Bipartite Projections
首次提交 2026-11-12 20:08
版本更新 2026-11-12 20:08
雷达观测 2026-11-13 14:15
摘要
Collecting data, including network data, is subject to measurement error. Network backboning is the process of statistically validating each edge in a network to estimate the probability that the edge is born of noisy measurements. The current backboning literature provides approaches in two classes: structural and statistical. The classes differ in many respects, but they share one commonality: the test used to determine whether to filter out noisy edges is data-agnostic; it does not take into account what the network is representing. However, the nodes and edges of all non-synthetic networks correspond to specific real-world entities, whose dynamics determine how the edge-generating process operates. In this paper, we propose the first in a new class of network backboning algorithms: a theory-driven backboning approach that allows us to encode known dynamics into the backboning process. The resulting backbone embeds the encoded theory and produces a network compatible with it, rather than one that obeys a data-agnostic network process, as in the structural and statistical backboning classes. We specifically focus on the case of projecting and backboning a bipartite structure. We do so by allowing users to specify an Agent-Based Model for the two types of agents (nodes) in the network and by performing Simulation-Based Inference to fit the observed bipartite structure and, as a result, reconstruct the true relationships between the two classes of agents. We test our framework both on synthetic data -- where we have perfect information about the edge-generating process -- and in a case study using archaeological data on amphorae retrieved from different archaeological sites. Our approach improves latent-edge recovery over conventional backboning methods in synthetic experiments and reveals interpretable archaeological patterns despite imperfect model fit.
作者备注：13 pages, 10 figures, 3 tables
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v1 · 2026-11-12 20:08 · 最新观测版本
Theory-Driven Network Backbones for Bipartite Projections
Collecting data, including network data, is subject to measurement error. Network backboning is the process of statistically validating each edge in a network to estimate the probability that the edge is born of noisy measurements. The current backboning literature provides approaches in two classes: structural and statistical. The classes differ in many respects, but they share one commonality: the test used to determine whether to filter out noisy edges is data-agnostic; it does not take into account what the network is representing. However, the nodes and edges of all non-synthetic networks correspond to specific real-world entities, whose dynamics determine how the edge-generating process operates. In this paper, we propose the first in a new class of network backboning algorithms: a theory-driven backboning approach that allows us to encode known dynamics into the backboning process. The resulting backbone embeds the encoded theory and produces a network compatible with it, rather than one that obeys a data-agnostic network process, as in the structural and statistical backboning classes. We specifically focus on the case of projecting and backboning a bipartite structure. We do so by allowing users to specify an Agent-Based Model for the two types of agents (nodes) in the network and by performing Simulation-Based Inference to fit the observed bipartite structure and, as a result, reconstruct the true relationships between the two classes of agents. We test our framework both on synthetic data -- where we have perfect information about the edge-generating process -- and in a case study using archaeological data on amphorae retrieved from different archaeological sites. Our approach improves latent-edge recovery over conventional backboning methods in synthetic experiments and reveals interpretable archaeological patterns despite imperfect model fit.
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