A Versioned Unified Graph Index for Dynamic Timestamp-Aware Nearest Neighbor Search
- URL: http://arxiv.org/abs/2608.27663v1
- Date: Thu, 27 Aug 2026 19:51:28 GMT
- Title: A Versioned Unified Graph Index for Dynamic Timestamp-Aware Nearest Neighbor Search
- Abstract summary: TiGER is a novel approach for performing fast time-aware approximate nearest neighbor searches on dynamic vector datasets.<n>Our proposed algorithm builds and maintains a unified graph for all vectors by leveraging an index structure based on integrated versioned connectivity.
- Score: 4.414455125136369
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present TiGER (Time-Integrated Graph for Efficient Retrieval), a novel approach for performing fast time-aware approximate nearest neighbor searches on dynamic vector datasets with flexibility over any possible time range. Our proposed algorithm builds and maintains a unified graph for all vectors by leveraging an index structure based on integrated versioned connectivity, allowing arbitrary time intervals to be queried directly on the unified graph without having to traverse invalid vectors. This forgoes the need for post-search filtering or merging, or separate graphs for each possible composite range. Empirical evaluations show that our method attains up to a 5x improvement in queries per second (QPS) without compromising accuracy over baselines based on filtering or per-time-segment sub-graphs. We believe that this method will enable efficient temporal analysis across evolving datasets in real-time recommendation systems, log analysis, and any scenario requiring fast similarity search over dynamic, time-segmented data.
Related papers
- Unified Dominance Graph for Interval-Predicate Approximate Nearest Neighbor Search [8.875776605139185]
We study Interval-Predicate ANNS, where validity is determined by a predicate between an object interval and a query interval.<n>Existing range-filtering ANNS methods are designed for single-dimensional scalar filters.<n>We propose the Unified Dominance Graph (UDG), a graph-indexing framework for the closed two-bound conjunctive fragment of IPANNS.
arXiv Detail & Related papers (2026-06-23T06:46:28Z) - CubeGraph: Efficient Retrieval-Augmented Generation for Spatial and Temporal Data [11.878755010092922]
CubeGraph is a framework designed to integrate vector search with arbitrary spatial constraints.<n>During query execution, CubeGraph stitches together adjacent cube-level indices on the fly whenever spatial cells intersect with the query filter.
arXiv Detail & Related papers (2026-04-08T02:49:55Z) - GraphER: An Efficient Graph-Based Enrichment and Reranking Method for Retrieval-Augmented Generation [47.014599006114594]
GraphER is a graph-based enrichment and reranking method that captures multiple forms of proximity beyond semantic similarity.<n>It does not require a knowledge graph, allowing GraphER to integrate seamlessly with standard vector stores.<n> Experiments on multiple retrieval benchmarks demonstrate the effectiveness of the proposed approach.
arXiv Detail & Related papers (2026-03-26T01:30:22Z) - Filtered Approximate Nearest Neighbor Search in Vector Databases: System Design and Performance Analysis [0.5249805590164902]
Filtered Approximate Nearest Neighbor Search (FANNS) is used to combine semantic retrieval with metadata constraints.<n>In this work, we systematize the taxonomy of filtering strategies and evaluate their integration into vectors.<n>Our experiments reveal that algorithmic adaptations within the engine often override raw index performance.
arXiv Detail & Related papers (2026-02-11T23:40:26Z) - Curator: Efficient Vector Search with Low-Selectivity Filters [12.774238654446032]
Graph-based indexes achieve state-of-the-art performance on unfiltered ANNS but encounter connectivity breakdown on low-selectivity filtered queries.<n>Recent research proposes specialized graph indexes that address this issue by expanding graph degree.<n>We present Curator, a partition-based index that complements existing graph-based approaches for low-selectivity filtered ANNS.
arXiv Detail & Related papers (2026-01-03T21:35:01Z) - TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecasting [87.71846357354384]
Time series forecasting methods generally fall into two main categories: Channel Independent (CI) and Channel Dependent (CD)<n>Recent advances in Channel Clustering (CC) aim to refine dependency modeling by grouping channels with similar characteristics.<n>We propose TimeFilter, a GNN-based framework for adaptive and fine-grained dependency modeling.
arXiv Detail & Related papers (2025-01-22T17:40:17Z) - TimeGraphs: Graph-based Temporal Reasoning [64.18083371645956]
TimeGraphs is a novel approach that characterizes dynamic interactions as a hierarchical temporal graph.
Our approach models the interactions using a compact graph-based representation, enabling adaptive reasoning across diverse time scales.
We evaluate TimeGraphs on multiple datasets with complex, dynamic agent interactions, including a football simulator, the Resistance game, and the MOMA human activity dataset.
arXiv Detail & Related papers (2024-01-06T06:26:49Z) - Sparse Training of Discrete Diffusion Models for Graph Generation [45.103518022696996]
We introduce SparseDiff, a novel diffusion model based on the observation that almost all large graphs are sparse.
By selecting a subset of edges, SparseDiff effectively leverages sparse graph representations both during the noising process and within the denoising network.
Our model demonstrates state-of-the-art performance across multiple metrics on both small and large datasets.
arXiv Detail & Related papers (2023-11-03T16:50:26Z) - Temporal Graph Benchmark for Machine Learning on Temporal Graphs [54.52243310226456]
Temporal Graph Benchmark (TGB) is a collection of challenging and diverse benchmark datasets.
We benchmark each dataset and find that the performance of common models can vary drastically across datasets.
TGB provides an automated machine learning pipeline for reproducible and accessible temporal graph research.
arXiv Detail & Related papers (2023-07-03T13:58:20Z) - Reinforcement Learning Based Query Vertex Ordering Model for Subgraph
Matching [58.39970828272366]
Subgraph matching algorithms enumerate all is embeddings of a query graph in a data graph G.
matching order plays a critical role in time efficiency of these backtracking based subgraph matching algorithms.
In this paper, for the first time we apply the Reinforcement Learning (RL) and Graph Neural Networks (GNNs) techniques to generate the high-quality matching order for subgraph matching algorithms.
arXiv Detail & Related papers (2022-01-25T00:10:03Z) - Structural Temporal Graph Neural Networks for Anomaly Detection in
Dynamic Graphs [54.13919050090926]
We propose an end-to-end structural temporal Graph Neural Network model for detecting anomalous edges in dynamic graphs.
In particular, we first extract the $h$-hop enclosing subgraph centered on the target edge and propose the node labeling function to identify the role of each node in the subgraph.
Based on the extracted features, we utilize Gated recurrent units (GRUs) to capture the temporal information for anomaly detection.
arXiv Detail & Related papers (2020-05-15T09:17:08Z) - A Hybrid Approach to Temporal Pattern Matching [2.5001363890681336]
We propose a hybrid approach that achieves effective filtering of potential matches based on structure and time.
We present experiments with real datasets that illustrate the efficiency of our approach.
arXiv Detail & Related papers (2020-01-06T16:52:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.