Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· 0 citations· 30 references
Abstract
Learning on signed graphs poses unique challenges due to the presence of both positive and negative links, which represent complex social dynamics such as trust and conflict. While existing signed graph neural networks (SGNNs) have achieved promising results, most rely on deep, multi-hop propagation architectures that suffer from scalability issues. In this work, we revisit message passing from the perspective of opinion dynamics, and propose SigFJProp, a novel and lightweight SGNN framework inspired by the Friedkin–Johnsen model. SigFJProp models signed message propagation as a discrete-time opinion update process, whose equilibrium state admits a closed-form solution involving the signed forest matrix. To make this computation tractable on large-scale graphs, we introduce Expansion Forest Sampling, a linear-time approximation method tailored for signed networks. Our approach decouples neural representation learning from propagation dynamics, enabling efficient and interpretable learning. Experimental results on seven benchmark datasets, including one with over 750K nodes and 10M edges, demonstrate that SigFJProp achieves competitive or superior performance compared to state-of-the-art methods, while significantly reducing computational overhead. The code is available at https://github.com/SunYubo23/SigFJProp.
While signed social recommendation has shown great potential by modeling both trust and distrust relations, its effectiveness is often hindered by structural noise and data sparsity. In this work, we first identify a fundamental inconsistency across the structural, propagation, and semantic layers of existing models, which leads to biased representations learned from sparse or noisy datasets. Furthermore, we observe that most existing methods treat the observed graph as fixed, failing to bridge the gap between noisy topologies and reliable social semantics. To address these issues, we propose a unified framework named SSC-Loop that treats signed social recommendation as the maximization of structural consistency. SSC-Loop includes three dedicated modules: ESA-DA for structural consistency, a P/N/O propagation mechanism for propagation consistency, and a contrastive learning objective for semantic consistency. Experiments on Epinions demonstrate that SSC-Loop achieves strong performance on explicit signed social rating prediction, while auxiliary results on Slashdot under a derived link-existence setting further suggest its ability to exploit signed social structures. Source code is available at https://github.com/Refrainwww/SSC-Loop.
Existing Signed Graph Neural Networks optimize link sign prediction objectives fundamentally misaligned with friend recommendation, while discarding trust asymmetry, edge strength, and adaptive social theory application. We present ASRA-GNN, addressing these gaps through three contributions: Sign-Aware Structural Role Attention (SSRA) grounded in four social network theories; a Locally Adaptive Theory Mixing (LATM) gate replacing TrustSGCN's binary global threshold with a continuous per-node end-toend learned mixing function; and a Signed Contrastive Recommendation Loss providing the first ranking objective for signed user-user graphs using observed positive-negative pairs as natural contrastive anchors. Experiments on Bitcoin-OTC and Bitcoin-Alpha demonstrate an average of Recall@10 of 0.0599, NDCG@10 of 0.4280, and Precision@10 of 0.0840, outperforming all other baselines.
Pharsana Parveen M, Stanis Arul Mary A· International journal of Com...· 0 citations
Temporal graph learning has become essential for analyzing real-world systems whose interactions continuously evolve over time, including financial transaction networks, communication systems, and online social platforms. However, learning from large-scale temporal graphs remains computationally challenging when networks are dense and rapidly changing. To address this limitation, we propose a network-curvature-inspired edge sparsification framework for dynamic graph learning. Our proposed method, TRicci, extends classical Forman-Ricci curvature to directed weighted temporal graphs by capturing structural support, temporal recency, and local interaction competition. Experiments on 9 transaction networks and 3 temporal graph benchmark datasets demonstrate that the proposed framework preserves predictive performance across multiple graph-level prediction tasks. The results show that TRicci sparsifies temporal graphs by approximately 80% while reducing end-to-end downstream training and inference time by an average of 55.94%, without substantial degradation in predictive performance. Our findings suggest that temporal curvature can serve as a principled basis for scalable temporal graph learning by preserving predictive temporal-structural information under substantial sparsification.
Poupak Azad, C. Akcora, Kiarash Shamsi· 0 citations
Results show that mixed-distribution training can improve structural transfer in GNN-based centrality approximation, while identifying closeness centrality's sensitivity to topology as an open challenge.
Samra Sana, Giorgio Mantica, Saul Imbrici· 0 citations
Graph unlearning removes the influence of sensitive edges and nodes from trained Graph Neural Networks (GNNs) without full retraining, which is essential for privacy protection. However, existing graph unlearning methods do not account for the heterogeneity of positive and negative edges in signed graphs, thereby degrading both model utility and unlearning effectiveness when applied to widespread signed graph applications. To fill this research gap, we propose Certified Signed Graph Unlearning (CSGU), which leverages the sociological principles underlying signed graphs, providing provable privacy guarantees while maintaining model utility. Specifically, CSGU efficiently identifies minimal influenced neighborhoods via triangular structures, and then applies sociological theories to quantify edge influence. Subsequently, it performs influence-weighted parameter updates with calibrated noise injection to achieve certified privacy guarantees with minimal utility degradation. Extensive experiments across five datasets show that CSGU outperforms four competing graph unlearning methods on four GNN architectures in most settings, achieving state-of-the-art results in both utility preservation and unlearning effectiveness.
Junpeng Zhao, Lin Li, Yu Yang et al.· Proceedings of the 32nd ACM...· 0 citations
While signed-directed graphs have been studied using linear Laplacians in the design of graph neural networks, relatively little research has focused on developing non-linear Laplacian operators for such networks. We introduce a non-linear Laplacian operator specific to signed and directed networks (NLSD). This non-linear operator extends the concepts of the signed Laplacian for signed graphs and the Laplacian for directed graphs. The NLSD calculates node-specific potentials based on features More precisely, if the potential discrepancy is not aligned with the edge direction, we ignore it (and vice versa) leveraging message-passing techniques only across edges where potential discrepancies align with the edge's direction. Utilizing this novel operator, we propose an efficient spectral GNN framework (NLSD-GNN). We conducted comprehensive evaluations focusing on node classification and link prediction, examining scenarios involving signed, directional, or both types of information. Our findings reveal that this spectral GNN framework not only integrates signed and directional data effectively but also achieves superior performance across diverse datasets.