Approximate Nearest Neighbor Search with arbitrary filtering predicates (AFANNS) is essential for modern data applications, yet existing methods often incur substantial storage and computational costs. In this work, we introduce the Maximal Clique Index (\mci), a novel graph-based index designed for robust and efficient AFANNS. The core idea of \mci is to approximate a dense Nearest Neighbor Graph (NNG) through a compact, clique-based representation. We propose two key techniques: (1) Maximal Clique Cover (\mcc), which exploits the geometric transitivity of high-dimensional spaces to encode dense neighborhoods as maximal cliques, achieving an index with high compression and connectivity; and (2) Local Neighborhood Graph Geometric Densification, a strategy that constructs an index approximating a large NNG from a sparse initial NNG, recovers global connectivity by progressively increasing distance thresholds to locally densify the structure. The index is built in a lock-free parallel manner for scalability and queried via a carefully-designed multi-seed strategy to handle fragmented predicate-induced subgraphs. Extensive experiments on 10 datasets show that \mci significantly outperforms state-of-the-art methods by up to one order of magnitude in QPS at high recall while using substantially smaller space, and remains competitive even on range/keyword filtering tasks, demonstrating robust general-purpose performance.

翻译：带任意过滤谓词的近似最近邻搜索（AFANNS）是现代数据应用中的关键技术，但现有方法往往面临存储与计算成本过高的问题。本文提出最大团索引（\mci）——一种基于图结构的新型索引，旨在实现鲁棒且高效的AFANNS。其核心思想是通过紧凑的团表示来近似稠密最近邻图（NNG）。我们提出两项关键技术：(1) 最大团覆盖（\mcc），利用高维空间的几何传递性将稠密邻域编码为最大团，从而构建高压缩比与高连通性的索引；(2) 局部邻域图几何致密化策略，该策略通过从稀疏初始NNG出发，逐步增大距离阈值以局部致密化结构，构建近似大规模NNG的索引并恢复全局连通性。索引采用无锁并行方式构建以保证可扩展性，并通过精心设计的多种子搜索策略处理由谓词分割导致的碎片化子图。在10个数据集上的大量实验表明，\mci 在高召回率下的QPS指标较现有最优方法提升达一个数量级，且占用空间显著更小；即使在范围过滤/关键词过滤任务中也保持竞争力，展现出稳健的通用性能。