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This paper analyzes distance-preserving embeddings in inhomogeneous random graphs, providing tighter distortion bounds than classical worst-case results and introducing a GNN-augmented variant that learns universal features from small graphs.
Five researchers from Tsinghua, Stanford, and Max Planck have developed a new shortest path algorithm that beats Dijkstra's for sparse directed graphs, achieving O(m log^(2/3) n) time complexity, the first improvement since 1987.
Researchers from Tsinghua University have developed a new shortest-path algorithm with O(m log^{2/3} n) complexity, surpassing Dijkstra's algorithm, which had been considered theoretically optimal for 41 years.