22 papers · ranked by Valyu relevance
Qisen Chai, Yansong Wang, Junjie Huang, Tao Jia
As graph-structured data grow increasingly large, evaluating their robustness under adversarial attacks becomes computationally expensive and difficult to scale. To address this challenge, we propose to compress graphs into compact representations that preserve both topological structure and robustness profile…
Dorota Celinska-Kopczynska, Eryk Kopczynski
Network theoreticians hypothesize that the structure of real-world networks has a geometric origin. Especially, hyperbolic geometry was proven insightful in representing and modeling of scale-free networks. Embedders are algorithms used to find a geometric representation of a network. In this study, we introduce a fast…
Kenta Yanagiya, Junya Hara, Hiroshi Higashi, Yuichi Tanaka + 1 more
In this paper, we propose a compression framework for weighted graphs in which the graph topology is transmitted losslessly and edge weights are compressed lossily. A challenge in the lossy compression of edge weights is that the underlying relationships between edges are ambiguous. To address this issue, we first…
Kawshik Banerjee, Khaled Mohammed Saifuddin
Graph compression reduces the computational cost of graph learning, but its effect on signal propagation remains largely underexplored. Existing work evaluates compression through downstream task performance or structural preservation, neither of which directly captures how propagation dynamics change after…
Jimmy Dubuisson
Reference-based graph compression encodes each vertex's neighbor list as differences from a nearby encoded list. WebGraph's BVGraph fixes a single encoding pipeline and relies on a separately chosen vertex ordering -- typically URL-lexicographic or Layered Label Propagation (LLP). Their interaction is rarely measured.…
Timothé Rouzé, Rayan Chikhi, Antoine Limasset
Petabases of sequencing data in the Sequence Read Archive (SRA) present a significant challenge for holistic reanalysis due to their sheer volume. Recent efforts have assembled this data into terabytes of unitigs, an efficient k-mer set representation that can reduce data size by an order of magnitude. However, these…
Phanindra Reddy Madduru, Bijo Thomas
This paper proposes a preprocessing framework for optimizing large-scale graph database ingestion through intelligent edge filtering based on value ranking. We combine adapted PageRank algorithms with business-specific metrics and edge type importance to evaluate and rank edges, enabling selective retention of…
Jouni Sirén, Benedict Paten
Existing pangenome file formats are designed for batch processing. Graphs must be loaded into memory, and alignment files must be read sequentially. Indexed file formats that can be used directly from disk would be more appropriate for interactive applications. We propose GBZ-base and GAF-base — SQLite-backed file…
Veronika Hendrychová, Karel Břinda
One important question in bacterial genomics is how to represent and search modern million-genome collections at scale. Phylogenetic compression effectively addresses this by guiding compression and search via evolutionary history, and many related methods similarly rely on tree- and ordering-based heuristics that…
Michail Patsakis, Theodore Chronopoulos, Ioannis Mouratidis, Ilias Georgakopoulos-Soares
Genomic data repositories continue to grow as sequencing technologies improve, with the NCBI SRA alone exceeding 47 PB. General-purpose compressors treat bioinformatics files as unstructured byte streams and fail to exploit the structured nature of omics data. We present NYX, a format-aware compression system for…
Fabio Cumbo, Kabir Dhillon, M. Hassan Najafi, Sercan Aygun + 1 more
The exponential growth of genomic databases necessitates alignment-free methods for comparing genomes. While MinHash-based tools have revolutionized this field by efficiently estimating the Average Nucleotide Identity based on k-mer sets, they inherently discard structural genomic information. We introduce HyperSketch…
Yuhang Wang, Weihua Chen, Linjing Song, Zhiping Xu + 6 more
With the rapid growth of data volume in sensor networks, lossy source coding systems achieve high-efficiency data compression with low distortion under limited transmission bandwidth. However, conventional compression algorithms rely on a two-stage framework with high computational complexity and frequently struggle to…
Sibusiso B. Buthelezi, Jules R. Tapamo, Nikolaos Mitianoudis
We present a hybrid end-to-end learned image compression framework that combines a CNN-based variational autoencoder (VAE) with an efficient hierarchical Swin Transformer to address the limitations of existing entropy models in capturing global dependencies under computational constraints. Traditional VAE-based codecs…
Esteban-Alejandro Durán-Yáñez, Mario-Alberto Rodríguez-Díaz, Ricardo Mendoza-González, Francisco-Javier Luna-Rosas + 2 more
Voxel representations provide a simple way to represent three-dimensional objects as binary occupancy signals, but dense voxel grids and direct sparse encodings remain costly at medium and high resolutions. This paper addresses the gap between conventional dense-grid, octree, and point-cloud-codec representations and…
Authors not listed
Recent advances in generative artificial intelligence have enabled in silico molecular design to become a powerful approach for exploring chemical space toward specific design goals across various domains. However, in actual design workflows, determining the appropriate generation conditions, including generative…
Authors not listed
We present a new method for fingerprint- ing atomic configurations relevant to ML-IAM training and application, utilizing the ChIMES descriptor. These fingerprints enable rigor- ous analysis of statistical distinguishability be- tween configurations. Sample applications in- clude assessing diversity within ML-IAP…
Dong-Ha Kim, Byung-Yoon Choi, Kwan-Jung Oh, Gwangsoon Lee + 2 more
3D Gaussian Splatting (3DGS) has recently emerged as an effective representation for immersive 3D scene rendering, providing high visual fidelity and real-time rendering efficiency. To support interoperable compression of trained 3DGS content, the Moving Picture Experts Group (MPEG) is exploring Gaussian Splat Coding…
Tao Guo, Zhangyao Song, Huihui Wu, Yang Li + 1 more
This paper analyzes the semantic rate-distortion problem motivated by task-oriented data compression with side information. The semantic information related to a task is not directly accessible to the encoder but implicitly impacts the observations through a joint probability distribution. The decoder aims to…
Yanlong Gao, Haiming Xu, Wei Huang, Hao Bai + 3 more
With the extensive applications of satellite image data in environmental monitoring and geographic surveying and mapping, the amount of data has increased rapidly, which brings great challenges for transmitting and storing these images. However, when processing high-resolution and multi-spectral satellite data…
Authors not listed
Transition state (TS) geometries of chemical reactions are key to understanding reaction mechanisms and estimating kinetic properties. Inferring these directly from 2D reaction graphs offers chemists a powerful tool for rapid and accessible reaction analysis. Quantum chemical methods for computing TSs are…
Alice Tor, Yuxin Wu, Stephen E Clarke, Lisa Yamada + 2 more
The complexity of neural data changes as the brain processes information during events. Universal lossless compression algorithms, which are broadly applicable and grounded in information theory, identify and exploit redundancies in data in order to compress it to essentially-optimal sizes regardless of underlying…
Authors not listed
Accurate prediction of redox potentials of iron (Fe) complexes, in tandem with uncertainty quantification, is essential to advance technologies related to electro-deposition and energy storage by enabling reliable modeling, guiding experimental design, and improving the efficiency of material discovery. Since…