G-Mixup: Graph Data Augmentation for Graph Classification

Xiaotian Han · Zhimeng Jiang · Ninghao Liu · Xia Hu

**Concept Graph & Resume using Claude 3.5 Sonnet | Chat GPT4o | Llama 3:**

graph LR
classDef augmentation fill:#d4f9d4, font-weight:bold, font-size:14px
classDef graphTheory fill:#f9d4d4, font-weight:bold, font-size:14px
classDef robustness fill:#d4d4f9, font-weight:bold, font-size:14px
classDef classification fill:#f9f9d4, font-weight:bold, font-size:14px
A[G-Mixup: Graph Data

Augmentation for Graph

Classification] --> B[G-Mixup:

graph augmentation

by interpolation. 1] A --> C[Graphon:

continuous function

for large graphs. 2] A --> D[Graph

classification:

labeling entire

graphs. 3] A --> E[Graph neural

networks:

deep learning

for graphs. 4] A --> F[Data

augmentation:

increase training

data diversity. 5] B --> G[Homomorphism density:

frequency of

subgraph patterns. 6] G --> H[Discriminative motif:

subgraph determining

class. 7] H --> I[Cut norm:

structural

similarity measure. 8] H --> J[Step function:

approximates

graphons. 9] G --> K[Graph generation:

creating synthetic

graphs. 10] C --> L[Manifold intrusion:

synthetic examples

conflict. 11] L --> M[Model robustness:

performance under

perturbations. 12] M --> N[Node/edge perturbation:

modify graph

properties. 13] N --> O[Subgraph sampling:

extract subgraphs. 14] L --> P[Graphon estimation:

infer from data. 15] C --> Q[Weak regularity lemma:

approximate

graphons. 16] Q --> R[Stochastic block model:

random graphs with

communities. 17] R --> S[Graph pooling:

aggregate node

features. 18] R --> T[Mixup:

interpolate features

and labels. 19] S --> U[Label corruption:

random label

changes. 20] A --> V[Topology corruption:

modify graph

structure. 21] V --> W[Open Graph Benchmark:

datasets for graph

tasks. 22] W --> X[Molecular property

prediction:

predict molecule

properties. 23] X --> Y[Graph isomorphism:

structural

equivalence. 24] A --> Z[Batch normalization:

stabilize training. 25] Z --> AA[Dropout:

regularization by

deactivation. 26] AA --> AB[Adam optimizer:

optimization

algorithm. 27] AB --> AC[AUROC:

binary classification

metric. 28] AC --> AD[Statistical significance:

p-values for

results. 29] A --> AE[Hyperparameter sensitivity:

performance with

different

hyperparameters. 30] class B,F,G,H,K augmentation class C,L,M,N,O,P,Q,R,S,T graphTheory class L,M,N,O robustness class W,X,Y classification class V,Z,AA,AB,AC,AD,AE others

Augmentation for Graph

Classification] --> B[G-Mixup:

graph augmentation

by interpolation. 1] A --> C[Graphon:

continuous function

for large graphs. 2] A --> D[Graph

classification:

labeling entire

graphs. 3] A --> E[Graph neural

networks:

deep learning

for graphs. 4] A --> F[Data

augmentation:

increase training

data diversity. 5] B --> G[Homomorphism density:

frequency of

subgraph patterns. 6] G --> H[Discriminative motif:

subgraph determining

class. 7] H --> I[Cut norm:

structural

similarity measure. 8] H --> J[Step function:

approximates

graphons. 9] G --> K[Graph generation:

creating synthetic

graphs. 10] C --> L[Manifold intrusion:

synthetic examples

conflict. 11] L --> M[Model robustness:

performance under

perturbations. 12] M --> N[Node/edge perturbation:

modify graph

properties. 13] N --> O[Subgraph sampling:

extract subgraphs. 14] L --> P[Graphon estimation:

infer from data. 15] C --> Q[Weak regularity lemma:

approximate

graphons. 16] Q --> R[Stochastic block model:

random graphs with

communities. 17] R --> S[Graph pooling:

aggregate node

features. 18] R --> T[Mixup:

interpolate features

and labels. 19] S --> U[Label corruption:

random label

changes. 20] A --> V[Topology corruption:

modify graph

structure. 21] V --> W[Open Graph Benchmark:

datasets for graph

tasks. 22] W --> X[Molecular property

prediction:

predict molecule

properties. 23] X --> Y[Graph isomorphism:

structural

equivalence. 24] A --> Z[Batch normalization:

stabilize training. 25] Z --> AA[Dropout:

regularization by

deactivation. 26] AA --> AB[Adam optimizer:

optimization

algorithm. 27] AB --> AC[AUROC:

binary classification

metric. 28] AC --> AD[Statistical significance:

p-values for

results. 29] A --> AE[Hyperparameter sensitivity:

performance with

different

hyperparameters. 30] class B,F,G,H,K augmentation class C,L,M,N,O,P,Q,R,S,T graphTheory class L,M,N,O robustness class W,X,Y classification class V,Z,AA,AB,AC,AD,AE others

**Resume: **

**1.-** G-Mixup: A graph data augmentation method that interpolates graphons (graph generators) of different graph classes to create synthetic graphs for training.

**2.-** Graphon: A continuous function representing the limiting behavior of large graphs, used as a graph generator.

**3.-** Graph classification: The task of assigning class labels to entire graphs rather than individual nodes.

**4.-** Graph neural networks (GNNs): Deep learning models designed to process graph-structured data.

**5.-** Data augmentation: Techniques to artificially increase training data size and diversity to improve model performance and generalization.

**6.-** Homomorphism density: A measure of the frequency of subgraph patterns in a graph or graphon.

**7.-** Discriminative motif: The minimal subgraph structure that can determine a graph's class label.

**8.-** Cut norm: A measure used to quantify the structural similarity between graphons.

**9.-** Step function: A piecewise constant function used to approximate graphons in practice.

**10.-** Graph generation: The process of creating synthetic graphs from a graphon or other generative model.

**11.-** Manifold intrusion: An issue in mixup methods where synthetic examples conflict with original training data labels.

**12.-** Model robustness: The ability of a model to maintain performance under various perturbations or corruptions of input data.

**13.-** Node/edge perturbation: Graph augmentation techniques that modify node or edge properties of existing graphs.

**14.-** Subgraph sampling: A graph augmentation method that extracts subgraphs from larger graph structures.

**15.-** Graphon estimation: Techniques to infer the underlying graphon from observed graph data.

**16.-** Weak regularity lemma: A theorem guaranteeing that graphons can be well-approximated by step functions.

**17.-** Stochastic block model: A probabilistic model for generating random graphs with community structure.

**18.-** Graph pooling: Methods to aggregate node-level features into graph-level representations for classification tasks.

**19.-** Mixup: A data augmentation technique that linearly interpolates features and labels between training examples.

**20.-** Label corruption: A robustness test where a portion of training labels are randomly changed.

**21.-** Topology corruption: A robustness test where graph structure (edges) is randomly modified.

**22.-** Open Graph Benchmark (OGB): A collection of benchmark datasets for various graph machine learning tasks.

**23.-** Molecular property prediction: A graph classification task to predict properties of molecules represented as graphs.

**24.-** Graph isomorphism: The concept of structural equivalence between graphs, relevant for designing GNN architectures.

**25.-** Batch normalization: A technique to stabilize neural network training by normalizing layer inputs.

**26.-** Dropout: A regularization technique that randomly deactivates neural network units during training.

**27.-** Adam optimizer: A popular optimization algorithm for training neural networks.

**28.-** Area Under Receiver Operating Characteristic (AUROC): A performance metric for binary classification tasks.

**29.-** Statistical significance: The use of p-values to determine if observed results are likely due to chance.

**30.-** Hyperparameter sensitivity: Analysis of how model performance changes with different hyperparameter values.

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