Graph Reconstruction

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9 papers2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Feasible Flow Matching for Graph Reconstruction via Within-Sampling Primal-Dual Guidance
Sep 26, 2026Haoming Chen, Nicolas Zilberstein, Santiago Paternain +1Graph Reconstruction Constrained Generative Modeling
Graph Learning with Spectral Connectivity Priors for Scarce Data
Sep 23, 2026Mingxiao Liu, Bahar Oveisgharan, Bingyan Zou +3Graph Signal Processing Graph Structure Learning
Tsinghua University, China · York University, Canada
Structurally Speaking: Motif-Oriented Graph Captioning through Bidirectional Graph-Text Translation
Sep 10, 2026Hsiao-Ying Lu, Dongyu Liu, Kwan-Liu MaStructured Prompting Graph Reconstruction
Department of Computer Science, University of California, Davis
VisAdj: Learning Adjacency Matrices from Node-Link Images
Aug 22, 2026Jiahao Xie, Guangmo TongGraph Reconstruction Graph Structure Learning
University of Delaware Newark, Delaware, USA
Pairton: Iterative Reconstruction of Short-Lived Particles
Aug 14, 2026Andreas Hermansen, Chris Scheulen, Tobias GollingGraph Reconstruction High-Energy Physics
Département de physique nucléaire et corpusculaire, University of Geneva
ArchEGraph: A Large-Scale Graph Dataset for Geometry-Topology-Physics Aligned Building Energy Modeling
Aug 7, 2026Yihui Li, Yihui Chen, Kaidi Zha +7Graph Reconstruction Benchmark Construction
Tsinghua University · UC Berkeley · 1Tsinghua University 2UC Berkeley 3MIT +4
GraViti: Graph-Level Variational Autoencoders with Relaxed Permutation Invariance
May 15, 2026Roman Bresson, Konstantinos Divriotis, Johannes F. Lutzeyer +2Molecular Generation Molecular Representation Learning
Mohamed bin Zayed University of Artificial Intelligence · LIX, CNRS, École Polytechnique, IP Paris
Spectral Embeddings Leak Graph Topology: Theory, Benchmark, and Adaptive Reconstruction
Apr 22, 2026Thinh Nguyen-Cong, Truong-Son Hy, Thang N. DinhGraph Reconstruction Spectral Methods
Department of Computer Science Virginia Commonwealth University Richmond, VA, USA · Department of Computer Science The University of Alabama at Birmingham Birmingham, AL, USA
Toward Graph-Tokenizing Large Language Models with Reconstructive Graph Instruction Tuning
Mar 2, 2026Zhongjian Zhang, Xiao Wang, Mengmei Zhang +2Graph Reconstruction LLMs for Graph Learning
Beijing University of Posts and Telecommunications, Beijing, China · Beihang University, Beijing, China · China Telecom Bestpay, Beijing, China

Sep 26, 2026J/K move · Enter open · S save

Feasible Flow Matching for Graph Reconstruction via Within-Sampling Primal-Dual Guidance

Haoming Chen, Nicolas Zilberstein, Santiago Paternain, Santiago Segarra

Graph Reconstruction Constrained Generative Modeling Discrete Flow Matching Primal-Dual Optimization

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Graph reconstruction from partial observations often comes with structural side information, such as degree bounds, triangle counts, or an edge-density band. Prior-Informed Flow Matching (PIFM) reconstructs graphs by transporting a local prior toward the graph distribution, but it provides no mechanism to incorporate this side information. We put forth Constrained Primal-Dual PIFM (CPD-PIFM), which augments the sampler with Lagrange multipliers that evolve along each trajectory. The multipliers respond to constraint violations at a predicted endpoint and guide subsequent sampling steps without retraining. We prove that the sampler inherits PIFM's permutation equivariance and bound its expected terminal slack by a term that decays as the inverse square root of the number of steps, plus two approximation terms. On three link-prediction benchmarks and nine combinations of datasets and constraints, CPD-PIFM raises feasibility by 11-26 percentage points and remains competitive with fixed guidance without selecting a separate multiplier for each constraint.