Knowledge Graphs as Structured Memory for Embedding Spaces: From Training Clusters to Explainable Inference

• URL: http://arxiv.org/abs/2511.14961v1
• Date: Tue, 18 Nov 2025 23:02:59 GMT
• Title: Knowledge Graphs as Structured Memory for Embedding Spaces: From Training Clusters to Explainable Inference
• Authors: Artur A. Oliveira, Mateus Espadoto, Roberto M. Cesar, Roberto Hirata,
• Abstract summary: Graph Memory (GM) is a structured non-parametric framework that augments embedding-based inference with a compact, relational memory over region-level prototypes.<n>By explicitly modeling reliability and relational structure, GM provides a principled bridge between local evidence and global consistency in non-parametric learning.
• Score: 3.2945446636945963
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Graph Memory (GM), a structured non-parametric framework that augments embedding-based inference with a compact, relational memory over region-level prototypes. Rather than treating each training instance in isolation, GM summarizes the embedding space into prototype nodes annotated with reliability indicators and connected by edges that encode geometric and contextual relations. This design unifies instance retrieval, prototype-based reasoning, and graph-based label propagation within a single inductive model that supports both efficient inference and faithful explanation. Experiments on synthetic and real datasets including breast histopathology (IDC) show that GM achieves accuracy competitive with $k$NN and Label Spreading while offering substantially better calibration and smoother decision boundaries, all with an order of magnitude fewer samples. By explicitly modeling reliability and relational structure, GM provides a principled bridge between local evidence and global consistency in non-parametric learning.

Related papers

• Bridging Academia and Industry: A Comprehensive Benchmark for Attributed Graph Clustering [19.247242477915382]
  Attributed Graph Clustering (AGC) is a fundamental unsupervised task that integrates structural topology and node attributes to uncover latent patterns in graph-structured data.<n>Despite its significance in industrial applications such as fraud detection and user segmentation, a significant chasm persists between academic research and real-world deployment.<n>We present PyAGC, a production-ready benchmark and library designed to stress-test AGC methods across diverse scales and structural properties.
  arXiv  Detail & Related papers  (2026-02-09T11:07:24Z)
• Contrastive Geometric Learning Unlocks Unified Structure- and Ligand-Based Drug Design [8.578932742190862]
  We introduce Contrastive Geometric Learning for Unified Drug Design (ConGLUDe)<n>ConGLUDe unifies predicted structure- and ligand-based training.<n>It supports virtual screening and target fishing, while being trained jointly on protein-ligand complexes and large-scale bioactivity data.
  arXiv  Detail & Related papers  (2026-01-14T18:45:08Z)
• MetagenBERT: a Transformer-based Architecture using Foundational genomic Large Language Models for novel Metagenome Representation [4.470992949474734]
  We present MetagenBERT, a framework that produces end to end metagenome embeddings directly from raw DNA sequences without taxonomic or functional annotations.<n>We evaluate this approach on five benchmark gut microbiome datasets (Cirrhosis, T2D, Obesity, IBD, CRC)<n>We additionally introduce MetagenBERT Glob Mcardis, a cross cohort variant trained on the large, phenotypically diverse MetaCardis cohort and transferred to other datasets, retaining predictive signal including for unseen phenotypes.
  arXiv  Detail & Related papers  (2026-01-05T19:36:36Z)
• Predict, Cluster, Refine: A Joint Embedding Predictive Self-Supervised Framework for Graph Representation Learning [0.0]
  Graph representation learning has emerged as a cornerstone for tasks like node classification and link prediction.<n>Current self-supervised learning (SSL) methods face challenges such as computational inefficiency, reliance on contrastive objectives, and representation collapse.<n>We propose a novel joint embedding predictive framework for graph SSL that eliminates contrastive objectives and negative sampling while preserving semantic and structural information.
  arXiv  Detail & Related papers  (2025-02-02T07:42:45Z)
• DeCaf: A Causal Decoupling Framework for OOD Generalization on Node Classification [14.96980804513399]
  Graph Neural Networks (GNNs) are susceptible to distribution shifts, creating vulnerability and security issues in critical domains. Existing methods that target learning an invariant (feature, structure)-label mapping often depend on oversimplified assumptions about the data generation process. We introduce a more realistic graph data generation model using Structural Causal Models (SCMs) We propose a casual decoupling framework, DeCaf, that independently learns unbiased feature-label and structure-label mappings.
  arXiv  Detail & Related papers  (2024-10-27T00:22:18Z)
• A Pure Transformer Pretraining Framework on Text-attributed Graphs [50.833130854272774]
  We introduce a feature-centric pretraining perspective by treating graph structure as a prior. Our framework, Graph Sequence Pretraining with Transformer (GSPT), samples node contexts through random walks. GSPT can be easily adapted to both node classification and link prediction, demonstrating promising empirical success on various datasets.
  arXiv  Detail & Related papers  (2024-06-19T22:30:08Z)
• Disentanglement via Latent Quantization [60.37109712033694]
  In this work, we construct an inductive bias towards encoding to and decoding from an organized latent space. We demonstrate the broad applicability of this approach by adding it to both basic data-re (vanilla autoencoder) and latent-reconstructing (InfoGAN) generative models.
  arXiv  Detail & Related papers  (2023-05-28T06:30:29Z)
• DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion [66.21290235237808]
  We introduce an energy constrained diffusion model which encodes a batch of instances from a dataset into evolutionary states. We provide rigorous theory that implies closed-form optimal estimates for the pairwise diffusion strength among arbitrary instance pairs. Experiments highlight the wide applicability of our model as a general-purpose encoder backbone with superior performance in various tasks.
  arXiv  Detail & Related papers  (2023-01-23T15:18:54Z)
• GSMFlow: Generation Shifts Mitigating Flow for Generalized Zero-Shot Learning [55.79997930181418]
  Generalized Zero-Shot Learning aims to recognize images from both the seen and unseen classes by transferring semantic knowledge from seen to unseen classes. It is a promising solution to take the advantage of generative models to hallucinate realistic unseen samples based on the knowledge learned from the seen classes. We propose a novel flow-based generative framework that consists of multiple conditional affine coupling layers for learning unseen data generation.
  arXiv  Detail & Related papers  (2022-07-05T04:04:37Z)
• GenURL: A General Framework for Unsupervised Representation Learning [58.59752389815001]
  Unsupervised representation learning (URL) learns compact embeddings of high-dimensional data without supervision. We propose a unified similarity-based URL framework, GenURL, which can smoothly adapt to various URL tasks. Experiments demonstrate that GenURL achieves consistent state-of-the-art performance in self-supervised visual learning, unsupervised knowledge distillation (KD), graph embeddings (GE), and dimension reduction.
  arXiv  Detail & Related papers  (2021-10-27T16:24:39Z)
• Deep Relational Metric Learning [84.95793654872399]
  This paper presents a deep relational metric learning framework for image clustering and retrieval. We learn an ensemble of features that characterizes an image from different aspects to model both interclass and intraclass distributions. Experiments on the widely-used CUB-200-2011, Cars196, and Stanford Online Products datasets demonstrate that our framework improves existing deep metric learning methods and achieves very competitive results.
  arXiv  Detail & Related papers  (2021-08-23T09:31:18Z)

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.