伊朗阿巴斯港，位于霍尔木兹海峡北岸。视觉中国/图
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当前，霸权主义、强权政治大行其道，严重冲击现行国际秩序。全球南方应当加强沟通协调，共同维护自身正当权益，共同开拓自主发展空间。
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Summary: We introduce an innovative technique for developing wavelet transformations applicable to functions on nodes of general finite weighted graphs. Our methodology employs scaling operations within the graph's spectral representation, which corresponds to the eigenvalue analysis of the graph Laplacian matrix Ł. Using a wavelet kernel function g and scaling factor t, we establish the scaled wavelet operator as T_g^t = g(tŁ). These spectral graph wavelets emerge when this operator acts upon delta functions. Provided g meets certain criteria, the transformation becomes reversible. We examine the wavelets' concentration characteristics as scales become increasingly refined. We also demonstrate an efficient computational approach using Chebyshev polynomial estimation that eliminates matrix diagonalization. The versatility of this transformation is illustrated through wavelet implementations on diverse graph structures from multiple domains.
2026年4月8日上午6:00