Satellite System Graph: Towards the Efficiency Up-Boundary of Graph-Based Approximate Nearest Neighbor Search
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Approximate Nearest Neighbor Search (ANNS) in high dimensional space is essential in database and information retrieval. Recently, there has been a surge of interests in exploring efficient graph-based indices for the ANNS problem. Among them, the NSG has resurrected the theory of Monotonic Search Networks (MSNET) and achieved the state-of-the-art performance. However, the performance of the NSG deviates from a potentially optimal position due to the high sparsity of the graph. Specifically, though the average degree of the graph is small, their search algorithm travels a longer way to reach the query. Integrating both factors, the total search complexity (i.e., the number of distance calculations) is not minimized as their wish. In addition, NSG suffers from a high indexing time complexity, which limits the efficiency and the scalability of their method. In this paper, we aim to further mine the potential of the MSNETs. Inspired by the message transfer mechanism of the communication satellite system, we find a new family of MSNETs, namely the Satellite System Graphs (SSG). In particular, while inheriting the superior ANNS properties from the MSNET, we try to ensure the angles between the edges to be no smaller than a given value. Consequently, each node in the graph builds effective connections to its neighborhood omnidirectionally, which ensures an efficient search-routing on the graph like the message transfer among the satellites. We also propose an approximation of the SSG, Navigating SSG, to increase the efficiency of indexing. Both theoretical and extensive experimental analysis are provided to demonstrate the strengths of the proposed approach over the existing state-of-the-art algorithms. Our code has been released on GitHub.
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