Title: Learn Low-dimensional Shortest-path Representation of Large-scale and Complex Graphs. Abstract: Estimation of shortest-path (SP) distance lies at the heart of network analysis tasks. Along with the rapid emergence of large-scale and complex graphs, approximate SP-representing algorithms that transform a graph into compact and low-dimensional representations are critical for fast and scalable online analysis. Among different approaches, learning-based representation methods have made a breakthrough both in response time and accuracy. Several competitive works in learning-based methods heuristically leverage truncated random walk and optimization on the arbitrary linkage for SP representation learning. However, they have limitations on both exploration range and distance preservation. We propose in this paper an efficient and interpretable SP representation method called Betweenness Centrality-based Distance Resampling (BCDR). First, we prove that betweenness centrality-based random walk can occupy a wider exploration range of distance due to its awareness of high-order path structures. Second, we leverage distance resampling to simulate random shortest paths from original paths and prove that the optimization on such shortest paths preserves distance relations via implicitly decomposing SP distance-based similarity matrix. BCDR yields an average improvement of 25% accuracy and 25-30% query speed, compared to all existing approximate methods when evaluated on a broad class of real-world and synthetic graphs with diverse sizes and structures.