Graphs

Mirage — Mirage: Model-Agnostic Graph Distillation for Graph Classification

Mridul Gupta, Sahil Manchanda, Hariprasad Kodamana et al. · ICLR 2024notableGraphspaper ↗code ↗

Mirage condenses a graph-classification dataset by decomposing every input graph into the multiset of computation trees a message-passing GNN would build on it, then compressing the (typically skewed) frequency distribution of these computation trees into a concise summary, rather than emulating gradient flows on the full dataset. Because it needs no full-dataset training and no target architecture during distillation, Mirage is unsupervised and architecture-agnostic, and the paper reports better generalization accuracy, compression and distillation efficiency than prior graph distillation baselines.

KIDD — Kernel Ridge Regression-Based Graph Dataset Distillation

Zhe Xu, Yuzhong Chen, Menghai Pan · KDD 2023Graphspaper ↗code ↗

KIDD (Kernel ridge regression-based graph Dataset Distillation) replaces the inner GNN training loop with kernel ridge regression, using a purpose-built graph kernel (LiteGNTK, related to the random-walk graph kernel) so the condensation objective has a closed-form solution. The paper reports strong empirical performance across seven real-world graph datasets, in some cases with condensed graphs outperforming the full training set using as few as 1.65% of the original training graphs.

SFGC — Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data

Xin Zheng, Miao Zhang, Chunyang Chen et al. · NeurIPS 2023notableGraphspaper ↗code ↗

SFGC drops the joint node-and-structure optimization of prior graph condensation methods and instead distills only a condensed node set with topology reduced to the identity matrix (graph-free data), implicitly folding structural information into the node attributes; a training-trajectory meta-matching scheme aligns the long-term GNN learning behavior between the original and condensed graph, and a closed-form graph neural feature score dynamically evaluates candidate condensed data during training. The paper reports superior performance across different condensation ratios versus prior graph condensation baselines.

GCond — Graph Condensation for Graph Neural Networks

Wei Jin, Lingxiao Zhao, Shichang Zhang et al. · ICLR 2022landmarkGraphspaper ↗code ↗

Introduces GCond, which condenses a large graph into a small synthetic graph and features by imitating the GNN training trajectory on the original graph through a gradient-matching loss, jointly synthesizing node features and structure so that GNNs trained on the condensed graph transfer across architectures; reports approximating original test accuracy by 95.3% on Reddit, 99.8% on Flickr and 99.0% on Citeseer while shrinking graph size by over 99.9%.

DosCond — Condensing Graphs via One-Step Gradient Matching

Wei Jin, Xianfeng Tang, Haoming Jiang et al. · KDD 2022Graphspaper ↗code ↗

Proposes DosCond, a one-step gradient-matching scheme for graph condensation that models the discrete graph structure probabilistically and matches gradients for a single step instead of unrolling multi-step training; reports 90% graph-size reduction while retaining up to 98% of original performance, about 15x faster than multi-step gradient matching.

CTRL — Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matching

Tianle Zhang, Yuchen Zhang, Kun Wang et al. · arXiv 2024Graphspaper ↗code ↗

CTRL observes that prior graph condensation methods match only the direction of gradients, which lets the condensed graph's training trajectory drift from the real graph's and accumulates error (worsened by condensation/evaluation-phase mismatch); it corrects this with an optimized starting point closer to the real feature distribution and a more refined gradient-matching strategy. The paper reports that CTRL neutralizes the impact of accumulated errors on condensed-graph performance, with experiments across multiple graph datasets and downstream tasks.

Graph Data Condensation via Self-expressive Graph Structure Reconstruction

Zhanyu Liu, Chaolv Zeng, Guanjie Zheng · KDD 2024Graphspaper ↗code ↗

GCSR (GCSR) condenses a graph by reconstructing an interpretable, closed-form self-expressive adjacency for the condensed nodes (each node reconstructed from a sparse combination of the others in its feature subspace) instead of learning a structure generator or dropping structure entirely, while node features are refined by multi-step gradient matching against the full graph's training trajectories. The paper reports the best average cross-architecture GNN accuracy among graph condensation baselines, attributing the gain to keeping explicit structural information.

SR-GM — Decoupling and Damping: Structurally-Regularized Gradient Matching for Multimodal Graph Condensation

Lian Shen, Zhendan Chen, Meijia Song et al. · arXiv 2025Graphspaper ↗

Condenses multimodal graphs, where node features integrate sources such as vision and text, by decoupling per-modality gradients before matching so that semantic misalignment between modalities does not create gradient conflicts, and adds a structural damping regularizer that suppresses gradient noise the message-passing mechanism would otherwise amplify through the topology. Reports state-of-the-art results and cross-architecture generalization on four multimodal graph datasets.

SFGC — Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data

Xin Zheng, Miao Zhang, Chunyang Chen et al. · NeurIPS 2023notableGraphspaper ↗code ↗

SFGC drops the joint node-and-structure optimization of prior graph condensation methods and instead distills only a condensed node set with topology reduced to the identity matrix (graph-free data), implicitly folding structural information into the node attributes; a training-trajectory meta-matching scheme aligns the long-term GNN learning behavior between the original and condensed graph, and a closed-form graph neural feature score dynamically evaluates candidate condensed data during training. The paper reports superior performance across different condensation ratios versus prior graph condensation baselines.

GEOM — Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching

Yuchen Zhang, Tianle Zhang, Kai Wang et al. · ICML 2024notableGraphspaper ↗code ↗

Identifies that the prior state-of-the-art graph trajectory-matching method supplies biased and restricted supervision signals from the original graph, limiting both the scale and efficacy of the condensed graph, and proposes GEOM, which trains expert trajectories with a curriculum-learning strategy for more diverse supervision and transfers that information into the condensed graph with an expanding-window matching scheme; reports state-of-the-art results toward lossless graph condensation across several datasets.

GCDM — Graph Condensation via Receptive Field Distribution Matching

Mengyang Liu, Shanchuan Li, Xinshi Chen et al. · arXiv 2022Graphspaper ↗

Proposes GCDM, which views a graph as a distribution of receptive fields and condenses it by matching that distribution between the real and synthetic graphs with a maximum-mean-discrepancy loss; reports strong cross-architecture generalization to a variety of GNN models and substantially faster condensation than earlier graph condensation methods.

GDEM — Graph Distillation with Eigenbasis Matching

Yang Liu, Deyu Bo, Chuan Shi · ICML 2024notableGraphspaper ↗code ↗

Proposes GDEM for graph distillation, which aligns the eigenbasis and node features of the synthetic and real graphs directly and replicates the real graph's spectrum, instead of relying on GNN-derived gradients, representations or trajectories that bias the synthetic spectrum and tie the result to one GNN architecture; adds a discrimination constraint to balance effectiveness and generalization, reporting state-of-the-art results with strong cross-architecture generalization and a theoretical guarantee that the synthetic graphs are restricted spectral approximations of the real ones.

LQM — Dataset Condensation with Latent Quantile Matching

Wei Wei, Tom De Schepper, Kevin Mets · CVPR 2024 WorkshopGraphspaper ↗

Shows that matching only the mean of latent feature embeddings, as in standard distribution matching, lets very different distributions appear identical, and proposes Latent Quantile Matching, which instead matches the quantiles of the embedding distributions to minimize a goodness-of-fit statistic; matches or beats prior distribution-matching methods on image and graph-structured datasets and improves continual graph learning.

SGDD — Does Graph Distillation See Like Vision Dataset Counterpart?

Beining Yang, Kai Wang, Qingyun Sun et al. · NeurIPS 2023notableGraphspaper ↗code ↗

Analyzes graph condensation methods in the spectral domain and finds that prior approaches induce a substantial Laplacian Energy Distribution (LED) shift between synthetic and real graphs, hurting cross-architecture generalization and tasks like anomaly detection and link prediction; proposes SGDD, which broadcasts the original graph's structural information into synthetic-graph generation to reduce this shift, reporting 98.6% of full-graph accuracy on YelpChi at a 1000x size reduction and 17.6-31.4% smaller LED shifts across nine datasets.