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Zhipeng Cai

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2026

Approximation Algorithm for Minimum Weight (2,m)-Connected Dominating Set

Using a connected dominating set (CDS) as a virtual backbone of a wireless sensor network can effectively save energy, reduce interference, and extend network lifespan, which also has wide applications in geometric routing algorithms and network topology control. A fault-tolerant virtual backbone can be modeled as a <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-connected <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula>-dominating set (abbreviated as a <inline-formula> <tex-math notation="LaTeX">$(k,m)$ </tex-math></inline-formula>-CDS) in a graph. In this paper, we present an approximation algorithm for the minimum weight <inline-formula> <tex-math notation="LaTeX">$(2,m)$ </tex-math></inline-formula>-CDS problem in a general graph, which achieves approximation ratio at most <inline-formula> <tex-math notation="LaTeX">$5.164H(n-1)$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$H(\gamma)=\sum _{i=1}^{\gamma }1/i$ </tex-math></inline-formula> is the <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>th Harmonic number and <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> is the number of nodes in the graph. This ratio improves previously best known ratio by a factor of at least 3.87.

Jiao Zhou, Zhipeng Cai, Xiaohui Huang et al. · 0 citations
Open access Jul 2026

Graph Attributed Unlearning via Propagation Suppression and Knowledge Dissipation

A graph unlearning framework specifically designed for feature-level unlearning, consisting of two main stages, which zero out the features of the unlearned nodes at each layer to block their propagation through the GNN, thereby reducing their influence on neighboring node representations.

Zhiyu Chen, Jiaquan Liang, Qi Luo et al. · 0 citations

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