Community search on attributed graphs (CSAG) is a fundamental topic in graph data mining. Given an attributed graph G and a query node q , CSAG seeks a structural- and attribute-cohesive subgraph from G that contains q . Exact methods based on graph traversal are time-consuming, especially for large graphs. Approximate methods improve efficiency by pruning the search space with heuristics but still take hundreds of milliseconds to tens of seconds to respond, hindering their use in time-sensitive applications. Moreover, pruning strategies are typically tailored to specific algorithms and their cohesiveness metrics, making them difficult to generalize. To address this, we study a general approach to accelerate various CSAG methods. We first present a proximity graph-based, cohesiveness-aware hierarchical index that accommodates different cohesiveness metrics. Then, we present two optimizations to enhance the index's navigability and reliability. Finally, we design a compressed storage structure for space-efficient indexing. Experiments on real-world datasets show that integrating our index with existing mainstream CSAG methods results in an average 30.7× speedup while maintaining a comparable or even better attribute cohesiveness.

High-dimensional vector similarity search (HVSS) is gaining prominence as a powerful tool for various data science and AI applications. As vector data scales up, in-memory indexes pose a significant challenge due to the substantial increase in main memory requirements. A potential solution involves leveraging disk-based implementation, which stores and searches vector data on high-performance devices like NVMe SSDs. However, implementing HVSS for data segments proves to be intricate in vector databases where a single machine comprises multiple segments for system scalability. In this context, each segment operates with limited memory and disk space, necessitating a delicate balance between accuracy, efficiency, and space cost. Existing disk-based methods fall short as they do not holistically address all these requirements simultaneously. In this paper, we present Starling, an I/O-efficient disk-resident graph index framework that optimizes data layout and search strategy within the segment. It has two primary components: (1) a data layout incorporating an in-memory navigation graph and a reordered disk-based graph with enhanced locality, reducing the search path length and minimizing disk bandwidth wastage; and (2) a block search strategy designed to minimize costly disk I/O operations during vector query execution. Through extensive experiments, we validate the effectiveness, efficiency, and scalability of Starling. On a data segment with 2GB memory and 10GB disk capacity, Starling can accommodate up to 33 million vectors in 128 dimensions, offering HVSS with over 0.9 average precision and top-10 recall rate, and latency under 1 millisecond. The results showcase Starling's superior performance, exhibiting 43.9x higher throughput with 98% lower query latency compared to state-of-the-art methods while maintaining the same level of accuracy.