This work proposes two scalable greedy algorithms based on gradient-guided edge selection that efficiently approximate marginal gains and prune the candidate edge set and achieves optimization performance comparable to exact greedy strategies while substantially reducing computational time.
Runze Zhang, Gengyu Wang, Zhong-Zhi Zhang· Proceedings of the 32nd ACM...· 0 citations
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...
Yubo Sun, Haoxin Sun, Zhongzhi Zhang· Proceedings of the 32nd ACM...· 0 citations
This work proposes two scalable greedy algorithms based on gradient-guided edge selection that efficiently approximate marginal gains and prune the candidate edge set and achieves 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.
