Approximate Nearest Neighbor (ANN) search is a core operation in large-scale vector databases that support recommendation, semantic search, and retrieval-augmented generation. Graph-based indexes are widely used for their superior search performance at million scale, but scaling them to billion-scale datasets exposes a fundamental trade-off between construction efficiency and search performance. CPU-based methods produce high-quality graphs enabling fast search but require multiple days to construct, while GPU-based methods accelerate construction at the cost of degraded search performance. This trade-off stems from the traditional requirement of enforcing global connectivity during graph construction. We observe that graph-based search primarily depends on local connectivity between entry points and a query's nearest neighbors, making global connectivity unnecessary for high search performance. In this paper, we introduce HEXA, a disjoint-subgraph-based indexing framework that partitions the dataset into disjoint clusters and constructs refined proximity subgraphs independently on GPUs. At query time, HEXA employs a two-level routing structure to select relevant subgraphs and query-close entry points, then applies a budget-adaptive search algorithm that allocates exploration effort based on each subgraph's likelihood of containing nearest neighbors. Experiments on billion-scale datasets demonstrate that HEXA achieves up to 14.7 times higher query throughput at the same recall levels compared with various indexing methods, and reduces construction time to approximately one hour.
Dense retrieval services underpin semantic search, recommendation, and retrieval-augmented generation, yet clients typically see only a top-k list with no auditable execution evidence. We present V3DB, a verifiable, versioned vector-search service that checks on demand whether an untrusted provider's approximate nearest-neighbor (ANN) result was produced by executing published I VF-PQ semantics on a committed snapshot. V3DB commits to each corpus snapshot and standardizes IVF-PQ into a fixed-shape, five-step query semantics. Given a public commitment and query embedding, the service returns top-k payloads and, when challenged, produces a succinct zero-knowledge proof that the output follows these semantics on the committed snapshot, without revealing corpus embeddings or private index contents to the verifier during audit. To make proving practical, V3DB avoids costly in-circuit sorting and random access with multiset equality/inclusion checks plus lightweight boundary conditions. Our Plonky2 prototype proves up to 22 & times; faster and uses up to 40% less peak memory than the circuit-only baseline, with millisecond verification.
Graph Neural Networks (GNNs) have emerged as the state-of-theart methodology for learning on graph-structured data, yet their performance is severely constrained by a fundamental mismatch between irregular graph sparsity and the rigid parallelism of modern hardware. While modern GPUs rely on Tensor Cores (TCs) to deliver massive computational throughput, these units demand strictly tiled, dense inputs-a requirement that conflicts with the extreme sparsity of real-world graphs. Existing frameworks fail to resolve this design conflict: they either fallback to legacy SIMT cores, leaving TCs underutilized, or they incur prohibitive memory bloat by forcing sparse data into dense tiles via excessive padding. To bridge this gap, we propose ThunderGNN, a hardware-aware acceleration system designed to reconcile graph irregularity with Tensor Core rigidity. ThunderGNN employs a unified co-design strategy comprising three key optimizations: (1) a sparsity-aware reordering algorithm that logically groups graph rows to maximize local density; (2) a Condensed Binarized Abstraction (CBA) storage layout that physically organizes the adjacency matrix into TC-aligned blocks without explicit zero-padding; and (3) a hardware-aware execution engine that efficiently streams compressed blocks directly into TCs. Extensive experiments on NVIDIA A100 GPUs demonstrate that ThunderGNN significantly outperforms state-of-the-art systems, achieving geometric mean speedups of 1.89 -> over DGL and 2.59 -> over PyG.
Spatio-temporal trajectory density data is widely used in urban analytics, mobility studies, and epidemiology. While differential privacy enables the release and analysis of such data, providing useful privacy guarantees for high-resolution density data remains challenging. Existing approaches typically perturb density data in the spatial domain, making it difficult to preserve spatio-temporal correlations and causing severe utility loss at fine granularity. We propose PrivSTD, a differentially private method for high-resolution spatio-temporal density release. PrivSTD applies the Discrete Cosine Transform to transform density data into the frequency domain, where low-frequency components capture spatial correlations and temporal smoothness. To suppress noise-dominated frequencies, we introduce a Benjamini-Hochberg FDR-based adaptive truncation mechanism that preserves statistically significant structure at no additional privacy cost. PrivSTD further employs a control variate-enhanced Recorrupted-to-Recorrupted denoising model to reconstruct high-quality density estimates. Experiments show that PrivSTD consistently outperforms existing methods, achieving 1.12 & times;-53.89 & times; lower error (6.12 & times; on average) across all datasets.
Cohesive subgraph mining in hypergraphs has recently attracted increasing research attention due to its broad applicability in domains such as social networks, co-authorship networks, and recommendation systems. An important model, the hyper k-truss, is defined as a maximal cohesive subgraph in which each hyper-edge is contained in at least (k-2) hyper-triangles (i.e., structures formed by three pairwise connected hyperedges). In this paper, we study the problem of hyper-truss decomposition, which aims to identify all hyper k-trusses for k >= 0. Due to the complex structure of hyper-triangles, the existing hyperedge-aware framework for hyper-truss decomposition incurs extra computational cost by traversing open hyper-triangles (i.e., hyper-triangles in which two hyperedges are not connected). Moreover, existing strategies enumerate all supporting hyper-triangles for each peeled hyperedge, which substantially limits overall efficiency. To address these issues, we propose a vertex-aware framework that leverages vertex-level connectivity among hyperedges. Under this framework, we design a vertex-oriented counting strategy to completely eliminate the traversal of open hyper-triangles during the counting phase and a vertex-based state propagation method to minimize the number of hyper-triangles enumerated in the peeling phase. Extensive experiments on eleven real-world datasets demonstrate the effectiveness and efficiency of our approach.
