Pith. sign in

REVIEW 5 cited by

Graph Neural Networks: A Review of Methods and Applications

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1812.08434 v6 pith:U7YTQEC2 submitted 2018-12-20 cs.LG cs.AIstat.ML

Jie Zhou , Ganqu Cui , Shengding Hu , Zhengyan Zhang , Cheng Yang , Zhiyuan Liu , Lifeng Wang , Changcheng Li
show 1 more author
Maosong Sun
This is my paper
classification cs.LGcs.AIstat.ML
keywords graphlearninggraphsmodelsnetworkneuralapplicationsdata
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein interface, and classifying diseases demand a model to learn from graph inputs. In other domains such as learning from non-structural data like texts and images, reasoning on extracted structures (like the dependency trees of sentences and the scene graphs of images) is an important research topic which also needs graph reasoning models. Graph neural networks (GNNs) are neural models that capture the dependence of graphs via message passing between the nodes of graphs. In recent years, variants of GNNs such as graph convolutional network (GCN), graph attention network (GAT), graph recurrent network (GRN) have demonstrated ground-breaking performances on many deep learning tasks. In this survey, we propose a general design pipeline for GNN models and discuss the variants of each component, systematically categorize the applications, and propose four open problems for future research.

Discussion (0). Sign in to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Rethinking Query Optimization for Multi-Agent Systems [Vision]

    cs.DB 2025-12 conditional novelty 6.0 of 10

    Agentic data pipelines are built by hand today; this paper sets a research agenda for automatically optimizing their structure, model choices, and execution engines jointly as a new query-optimization problem.

  2. Hot-Ham: an accurate and efficient E(3)-equivariant machine-learning electronic structures calculation framework

    physics.comp-ph 2025-09 conditional novelty 6.0 of 10

    Hot-Ham combines Gaunt tensor products with a local-coordinate SO(2) convolution to predict DFT Hamiltonians accurately and efficiently across several material classes.

  3. DEF: Diffusion-augmented Ensemble Forecasting

    cs.LG 2025-06 conditional novelty 6.0 of 10

    A conditional diffusion model that generates perturbed initial states can turn any deterministic neural weather forecast model into an ensemble, with measured error reduction on a single ERA5 case study.

  4. Physics-Informed EvolveGCN: Satellite Prediction for Multi Agent Systems

    cs.MA 2025-07 reject novelty 4.0 of 10

    A physics-informed EvolveGCN that adds a Clohessy-Wiltshire-based loss term to satellite swarm trajectory prediction shows only mixed, preliminary improvements over the same model without physics.

  5. MVAN: Multi-View Attention Networks for Fake News Detection on Social Media

    cs.CL 2025-06 reject novelty 3.0 of 10

    MVAN, a model combining BiGRU text attention with graph attention over retweet structures, reports accuracy gains of roughly 2.5 percent over prior state-of-the-art on Twitter15 and Twitter16.

Pith tools