arXiv Machine Learning
GraphVQ: Structure-Aware Autoregressive Decoding over Context-Quantized Graph Tokens
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arXiv Machine Learning
Jun 11
GraphInfer-Bench: Benchmarking LLM's Inference Capability on Graphs
arXiv:2606. 11562v1 Announce Type: new Abstract: Graph analysis underlies many applications whose answers cannot be looked up in a single record or retrieved along a path: laundering rings, drug repurposing, user preference, and scientific theme are all inferred from a node together with its neighbourhood.
arXiv AI
Jun 2
Graph is a Natural Regularization: Revisiting Vector Quantization for Graph Representation Learning
arXiv:2508. 06588v3 Announce Type: replace-cross Abstract: Vector Quantization (VQ) has recently emerged as a promising approach for learning compressed and discrete representations for graph-structured data.
arXiv AI
Jul 14
Graph Optimization Foundation Model: Tokenizing Graph via A Language-Model Paradigm
arXiv:2509. 24256v2 Announce Type: replace-cross Abstract: The pretrain-transfer paradigm, which underpins the success of large language models (LLMs), has demonstrated the immense power of creating foundation models that learn generalizable representations from vast datasets.
arXiv Machine Learning
Jul 14
Community-Aware Vertex Ordering for Reference-Based Graph Compression: A Cross-Encoder Empirical Study
arXiv:2605. 21510v2 Announce Type: replace-cross Abstract: Reference-based graph compression encodes each vertex's neighbor list as differences from a nearby encoded list.
arXiv Machine Learning
Sep 3
Omega-N: Interpretable Structural Node Descriptors and Their Applicability Domain
The paper introduces Omega‑N, a set of ten interpretable node‑level structural descriptors derived from localizing four factors of a composite structural index. By correcting the ill‑conditioned localization with a configuration‑null excess and a multi‑scale personalized‑PageRank neighbourhood, Omega‑N achieves competitive or superior performance in six in‑domain node‑classification tasks compared to a recursive feature engine that uses up to 252 features. In drug‑target prioritisation on protein interaction networks, Omega‑N improves AUPRC by 0.073 to 0.144 over a centrality baseline and remains robust across independent datasets and bias controls, though it offers no benefit when combined with Node2Vec. whyItMatters":"The study demonstrates that a compact, interpretable set of structural features can match or exceed more complex feature sets in practical graph‑based prediction tasks, particularly in biomedical network analysis."
arXiv AI
Sep 18
SCGFM-ART: Amortized Relational Transport for Structure-Centric Graph Foundation Models
SCGFM-ART is a structure‑centric graph foundation model that aligns arbitrary graphs onto a shared relational atlas using Amortized Relational Transport (ART). The atlas, defined by a finite set of relational landmarks, provides a universal coordinate system, while ART predicts end‑to‑end graph‑to‑base transport plans, eliminating costly runtime Gromov‑Wasserstein optimizations. The framework decomposes graphs into global relational response coordinates and local node‑to‑role structural correspondences, enabling unified representations that resolve structural and semantic heterogeneity across diverse graph domains.