Graph Classification

73개 벤치마크 · 논문 1,024편 · 이 태스크의 논문 보기 →

Benchmarks

Most implemented

Semi-Supervised Classification with Graph Convolutional Networks

2016-09-09 · 구현 55개

Modeling Relational Data with Graph Convolutional Networks

2017-03-17 · 구현 27개

ImageNet Classification with Deep Convolutional Neural Networks

2012-12-01 · 구현 23개

Inductive Representation Learning on Large Graphs

2017-06-07 · 구현 20개

How Powerful are Graph Neural Networks?

2018-10-01 · 구현 19개

Papers

Global to Local: Topology-Preserving Adaptive Graph Pooling via Granular-Ball

2026-09-04 · Sen Zhao, Gaojie Xu, Shuyin Xia, Yifan Guan 외 arxiv

Graph pooling aims to compress the graph, including both node embeddings and their underlying topological patterns, into a more compact representation. Previous works focus primarily on the overly fine-grained representa…

Graph Classification

Physics-Aware Random Walk Fingerprints for Scalable Power Grid Graph Classification

2026-09-04 · Adnan Anwar arxiv

Recent benchmarks such as PowerGraph provide large collections of power-grid graphs for cascading-failure classification. Graph neural networks (GNNs) achieve strong predictive performance on this task, but typically req…

Graph Classification

Inductive Correlation Clustering with Graph Neural Networks

2026-08-27 · Francesco Paolo Nerini, Francesco Bonchi, Arijit Khan, André Panisson arxiv

Correlation Clustering (CC) is a natural formulation of clustering in combinatorial optimization, which uses a graph representation of the input and does not require a pre-specified number of clusters. Given $n$ objects …

Graph Classification

Boosting Data Augmentation with Stochastic Weight Averaging

2026-08-14 · Longde Huang, Axel Flinth, Jan E. Gerken arxiv

The symmetries of a learning task have become an important factor in designing modern deep learning solutions. Data augmentation is a straightforward and effective way of incorporating symmetries into a generic neural ne…

Graph Classification Data Augmentation

HP-JEPA: Hierarchical Partitioning for Multi-Resolution Graph Joint-Embedding Predictive Learning

2026-08-01 · Ruichen Xu, Jingxiang Qu, Wenhan Gao, Jiaxing Zhang 외 arxiv

Graph self-supervised learning aims to learn transferable representations from large-scale unlabeled graph data. Joint-embedding predictive architectures (JEPAs) avoid explicit negative-pair construction and raw-input re…

Graph Representation Learning Self-Supervised Learning Graph Classification Graph Regression

TopoFormer: Topology Meets Attention for Graph Learning

2026-07-30 · Md Joshem Uddin, Astrit Tola, Cuneyt Gurcan Akcora, Baris Coskunuzer arxiv

We introduce Topoformer, a lightweight and scalable framework for graph representation learning that encodes topological structure into attention-friendly sequences. At the core of our method is Topo-Scan, a novel module…

Molecular Property Prediction Graph Representation Learning Graph Classification Graph Learning

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