Graph isomorphism network
Architectures AIMech:GraphIsomorphismNetwork DRAFT
Aliases: GIN
A graph neural architecture using multilayer perceptrons on sums of neighboring representations and a separately weighted self-representation.
Identity Notes & Concept Scope
GIN is the named sum-and-MLP architecture, not the graph-isomorphism decision problem, all graph neural networks, or all architectures as expressive as a Weisfeiler–Lehman test.
Claims & Grounded Evidence
1 Claim
Mechanism
#mechanism
Each update applies an MLP to the sum of neighbor states and (1 + epsilon) times the current node state, with epsilon fixed or learned.
⚠️ Curated Limitations & Negative Results
- The equation is taken from revised v3; the earlier v1 expression lacks the separately parameterized self weighting.
- No claim that GIN solves arbitrary graph isomorphism or that every trained finite network attains the theoretical expressivity bound is curated.
Artificial Intelligence Ontology (AIO) Grounding
Mapped AIO Classes
| AIO Target Class | Mapping Relation | Mapping Rationale |
|---|---|---|
AIO:ArtificialNeuralNetwork
Artificial Neural Network
|
broad_match | The architecture uses learned multilayer perceptrons to transform graph-node states; the AIO neural-network category is broader. |
AIO:GraphConvolutionalNetwork
Graph Convolutional Network
|
related_match | The AIO category describes graph-structured neural computation broadly; the GIN construction has a specific aggregation and self-weighting rule. |
Evidence Sources & Primary Literature
1 Source| Source ID | Publication / Document | Type / Access | Reference & Checksum |
|---|---|---|---|
original-paper
|
Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka
|
primary paper
full_text
|
ID: arXiv:1810.00826v3
sha256:a6ebf80c2790…
|
Curation Provenance
Curator: Codex
Method: ai_assisted
Model: GPT-6
Created: 2026-10-11T03:12:53Z
Updated: 2026-10-11T03:12:53Z
Source File:
data/curated/GraphIsomorphismNetwork.yaml