Sure! Here's a table summarizing the learning path and topics that you should cover before diving into Wavelet-based Graph Neural Networks (GNNs):
| Learning Stage | Topics and Algorithms | Key Concepts |
|---|---|---|
| 1. Basic Machine Learning | K-Nearest Neighbors (KNN) | Distance metrics, Classification, Regression |
| Linear Regression | Least squares, Residuals, Overfitting | |
| Logistic Regression | Sigmoid function, Binary classification, Cost function | |
| Decision Trees | Splitting criteria (Gini impurity, entropy), Overfitting, Pruning | |
| Support Vector Machines (SVM) | Hyperplane, Margin, Kernels, Non-linear classification | |
| Naive Bayes | Conditional probability, Bayes' theorem, Independence assumption | |
| Random Forests | Ensemble learning, Bagging, Feature randomness | |
| 2. Unsupervised Learning | K-Means Clustering | Centroids, Inertia, Clustering |
| Principal Component Analysis (PCA) | Eigenvectors, Eigenvalues, Dimensionality reduction, Variance | |
| Hierarchical Clustering | Dendrograms, Agglomerative, Divisive clustering | |
| t-SNE (t-Distributed Stochastic Neighbor Embedding) | Perplexity, Pairwise similarity, Gradient descent | |
| 3. Graph Theory | Graphs and Representations | Vertices, Edges, Adjacency matrix/list, Degree, Directed/Undirected graphs |
| Graph Traversal Algorithms | BFS (Breadth-First Search), DFS (Depth-First Search), Pathfinding, Graph traversal | |
| Shortest Path Algorithms | Dijkstra's Algorithm, Bellman-Ford, Floyd-Warshall | |
| Graph Centrality | Betweenness, Degree, Closeness centrality | |
| Graph Matching and Isomorphism | Graph isomorphism, Graph matching | |
| Graph Kernels | Kernel methods for graph comparison | |
| 4. Deep Learning Fundamentals | Neural Networks | Activation functions (ReLU, Sigmoid), Loss functions, Backpropagation, Gradient descent |
| Convolutional Neural Networks (CNNs) | Convolution, Pooling, Feature extraction | |
| Recurrent Neural Networks (RNNs) | Sequence data, LSTM, GRU, Time series | |
| Autoencoders | Encoder-decoder, Latent space, Reconstruction error | |
| 5. Graph Neural Networks (GNNs) | Graph Convolutional Networks (GCN) | Node classification, Graph convolutions, Message passing, Aggregation |
| Graph Attention Networks (GAT) | Attention mechanisms in graph data, Weighted message passing, Node representation learning | |
| GraphSAGE (Graph Sample and Aggregation) | Sampling, Inductive learning, Aggregation functions | |
| Graph Isomorphism Networks (GIN) | Stronger expressiveness, Graph isomorphism | |
| Graph Autoencoders (GAE) | Encoding/decoding graph data, Unsupervised learning | |
| 6. Wavelet Transforms & GNNs | Wavelet Transform | Multiresolution analysis, Haar/Daubechies wavelets, Frequency analysis |
| Wavelet Transform on Graphs | Graph signal processing, Spectral graph theory, Graph wavelets | |
| Graph Signal Processing | Graph Fourier transform, Graph filters, Wavelet transforms | |
| Wavelet-based Graph Neural Networks | Graph convolution with wavelet-based filters, Signal representation on graphs, Spectral graph convolutional networks |
How to Use This Table:
- Start with the basics: Begin with machine learning algorithms (KNN, Regression, Decision Trees, etc.), as they provide fundamental concepts that you will use later in more complex models.
- Understand unsupervised learning: Learn dimensionality reduction (PCA, K-Means) and clustering, as they will help you handle high-dimensional data, which is common in graph data.
- Master graph theory: Since Wavelet-based GNNs work on graph-structured data, understanding graphs, traversal algorithms, centrality measures, and shortest-path algorithms will form the foundation of your graph-related learning.
- Learn deep learning: Familiarize yourself with neural networks, CNNs, RNNs, and autoencoders to understand how data is processed and transformed in deep learning models.
- Dive into Graph Neural Networks (GNNs): Once you are comfortable with the basics, start learning about GCNs, GAT, GraphSAGE, GIN, and Graph Autoencoders to understand how neural networks can operate over graph structures.
- Understand Wavelet Transform: Finally, learn wavelet transforms and their application in graph signal processing, which is crucial for understanding Wavelet-based Graph Neural Networks.
This learning path will prepare you to tackle Wavelet-based Graph Neural Networks, by providing a strong theoretical and practical foundation in relevant areas.