<![CDATA[Community detection is an important aspect of complex network analysis, especially in attribute networks where topological structure and attribute information both play a role in community formation. Traditional structure-based methods tend to result in topologically dense but semantically inconsistent communities, while attribute-based approaches can improve semantic coherence but face scalability constraints and high computational costs. On the other hand, graph sparsification techniques have been used to reduce the size of the network, but most focus on structural aspects alone and rarely consider attributes, so the quality of the resulting community is often degraded. This study proposes CPSK (Community Preserving Sparsification based on K-core), a sparsification framework that combines k-core decomposition with attribute-based side weighting. This approach is designed specifically for attribute networks, with the aim of maintaining a balance between structural reduction and community semantic consistency, while improving the efficiency of the detection process. Evaluation of the six datasets showed that CPSK consistently generates more stable and meaningful communities than existing attribute-based community detection methods, while maintaining an edge in computing efficiency on large-scale and heterogeneous networks.]]>
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