New Motif-Based Naïve Bayes Framework Improves Sign Prediction in Signed Networks

Global: New Motif-Based Naïve Bayes Framework Improves Sign Prediction in Signed Networks

A team of researchers has introduced a generalized sign‑prediction framework that explicitly models heterogeneous influence among neighboring nodes, according to a paper posted on arXiv in December 2025. The work aims to enhance the accuracy of inferring the sign of target links in signed networks, which represent both positive and negative interactions in social and financial systems.

Background

Signed networks are widely used to capture complex relational data where connections can convey trust, antagonism, or other dual‑nature relationships. Predicting the sign of an unobserved link—known as sign prediction—supports applications such as reputation management, fraud detection, and recommendation systems.

Limitations of Existing Approaches

Traditional motif‑based Naïve Bayes models treat all neighboring nodes as contributing equally to the sign of a target link. This assumption overlooks the varied influence that different neighbors may exert, potentially constraining predictive performance.

Proposed Heterogeneity Modeling

The authors propose two role functions that quantify the differentiated influence of neighboring nodes within each motif. By assigning distinct weights to neighbors based on their structural roles, the framework captures the nuanced contributions that drive link sign outcomes.

Extending to Multiple Motifs

To broaden the analytical scope, the study introduces two strategies for incorporating multiple motifs. The generalized multiple‑motif Naïve Bayes model linearly combines information from diverse motifs, while the Feature‑driven Generalized Motif‑based Naïve Bayes (FGMNB) model integrates high‑dimensional motif features through machine‑learning techniques.

Experimental Evaluation

Extensive experiments were conducted on four real‑world signed networks. Results indicate that FGMNB consistently outperforms five state‑of‑the‑art embedding‑based baselines on three of these networks, demonstrating the advantage of modeling neighbor heterogeneity and leveraging multiple motif structures.

Insights on Predictive Motifs

The analysis reveals that the most predictive motif structures vary across datasets, underscoring the importance of local structural patterns and offering guidance for future motif‑based feature engineering.

Practical Implications

By providing a theoretically grounded solution to sign prediction, the framework holds potential for improving trust assessment and security mechanisms on online platforms where positive and negative interactions coexist.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.