Finding crucial vertices is a key problem for improving the reliability and ensuring the effective operation of networks,solved by approaches based on multiple attribute decision that suffer from ignoring the correlat...Finding crucial vertices is a key problem for improving the reliability and ensuring the effective operation of networks,solved by approaches based on multiple attribute decision that suffer from ignoring the correlation among each attribute or the heterogeneity between attribute and structure. To overcome these problems, a novel vertex centrality approach, called VCJG, is proposed based on joint nonnegative matrix factorization and graph embedding. The potential attributes with linearly independent and the structure information are captured automatically in light of nonnegative matrix factorization for factorizing the weighted adjacent matrix and the structure matrix, which is generated by graph embedding. And the smoothness strategy is applied to eliminate the heterogeneity between attributes and structure by joint nonnegative matrix factorization. Then VCJG integrates the above steps to formulate an overall objective function, and obtain the ultimately potential attributes fused the structure information of network through optimizing the objective function. Finally, the attributes are combined with neighborhood rules to evaluate vertex's importance. Through comparative analyses with experiments on nine real-world networks, we demonstrate that the proposed approach outperforms nine state-of-the-art algorithms for identification of vital vertices with respect to correlation, monotonicity and accuracy of top-10 vertices ranking.展开更多
动态属性网络的语义社区发现及演化分析具有重要研究价值,其包含动态社区发现、社区语义解释及社区演化分析三个任务,但现有方法均难以同时实现.针对该问题,提出一种基于联合非负矩阵分解的方法DANNMF(NMF for Dynamic Attributed Netwo...动态属性网络的语义社区发现及演化分析具有重要研究价值,其包含动态社区发现、社区语义解释及社区演化分析三个任务,但现有方法均难以同时实现.针对该问题,提出一种基于联合非负矩阵分解的方法DANNMF(NMF for Dynamic Attributed Networks).DAN-NMF可以统一集成网络拓扑结构信息、节点属性信息及社区演化平滑约束信息,并利用最大最小化优化框架推导相关因子矩阵的迭代更新规则,从而可以直接获得动态社区发现、社区语义解释及社区演化分析结果.在人工合成和真实的动态属性网络进行大量相关实验,结果表明DAN-NMF比最优的基准方法在准确性指标上至少提高了7.3%.此外,在真实动态属性网络上的相关数据分析结果也表明DAN-NMF能够有效地发现动态社区的演化模式,并提供丰富的社区语义解释.展开更多
基金Project supported by the National Natural Science Foundation of China (Grant Nos.62162040 and 11861045)。
文摘Finding crucial vertices is a key problem for improving the reliability and ensuring the effective operation of networks,solved by approaches based on multiple attribute decision that suffer from ignoring the correlation among each attribute or the heterogeneity between attribute and structure. To overcome these problems, a novel vertex centrality approach, called VCJG, is proposed based on joint nonnegative matrix factorization and graph embedding. The potential attributes with linearly independent and the structure information are captured automatically in light of nonnegative matrix factorization for factorizing the weighted adjacent matrix and the structure matrix, which is generated by graph embedding. And the smoothness strategy is applied to eliminate the heterogeneity between attributes and structure by joint nonnegative matrix factorization. Then VCJG integrates the above steps to formulate an overall objective function, and obtain the ultimately potential attributes fused the structure information of network through optimizing the objective function. Finally, the attributes are combined with neighborhood rules to evaluate vertex's importance. Through comparative analyses with experiments on nine real-world networks, we demonstrate that the proposed approach outperforms nine state-of-the-art algorithms for identification of vital vertices with respect to correlation, monotonicity and accuracy of top-10 vertices ranking.
文摘动态属性网络的语义社区发现及演化分析具有重要研究价值,其包含动态社区发现、社区语义解释及社区演化分析三个任务,但现有方法均难以同时实现.针对该问题,提出一种基于联合非负矩阵分解的方法DANNMF(NMF for Dynamic Attributed Networks).DAN-NMF可以统一集成网络拓扑结构信息、节点属性信息及社区演化平滑约束信息,并利用最大最小化优化框架推导相关因子矩阵的迭代更新规则,从而可以直接获得动态社区发现、社区语义解释及社区演化分析结果.在人工合成和真实的动态属性网络进行大量相关实验,结果表明DAN-NMF比最优的基准方法在准确性指标上至少提高了7.3%.此外,在真实动态属性网络上的相关数据分析结果也表明DAN-NMF能够有效地发现动态社区的演化模式,并提供丰富的社区语义解释.
