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.
Yubo Sun, Haoxin Sun, Zhongzhi Zhang· Proceedings of the 32nd ACM...· 0 citations
Information centrality is a powerful metric for quantifying node importance and has demonstrated practical value in a variety of real-world applications. Although existing studies have explored information centrality optimization via edge addition, they are restricted to a local setting, where candidate edges must be incident to the target node. Such restrictions fail to fully exploit the global structure of the network and often lead to suboptimal solutions under the same edge budget. In this work, we study the problem of global information centrality optimization, where candidate edges may connect any pair of currently non-adjacent nodes. This formulation significantly enlarges the search space and breaks the supermodularity property that underlies traditional greedy approaches, making the problem substantially more challenging. To address these challenges, we propose two scalable greedy algorithms based on gradient-guided edge selection. Our methods exploit geometric interpretations, dimensionality reduction techniques, and nearly-linear-time Laplacian solvers to efficiently approximate marginal gains and prune the candidate edge set. Extensive experiments on real-world networks demonstrate that our algorithms achieve optimization performance comparable to exact greedy strategies while substantially reducing computational time.
Runze Zhang, Gengyu Wang, Zhongzhi Zhang· Proceedings of the 32nd ACM...· 0 citations
A novel algorithm GSCF is proposed, built on a variant of loop-erased random walks, to generate generalized spanning converging forests in expected O(n) time and two sampling algorithms are developed, FMDE and FMDE+, for estimating the diagonal of the forest matrix.