Practical Guide to Graph Neural Networks: Titanic Survival Prediction Using GCN and k-NN Graph Construction

This project was published by Shubhranshu331 on GitHub (original link: https://github.com/Shubhranshu331/Graph-Based-Titanic-Survival-Analysis-GCN-k-NN, release date: 2026-05-26T11:15:18Z). The core content is converting Titanic tabular data into a k-NN graph structure and using Graph Convolutional Networks (GCN) to predict passenger survival rates, including a complete workflow of graph construction, model training, and visualization. Keywords: Graph Neural Network, GCN, Titanic, k-NN, Tabular Data, Graph Construction, Survival Prediction, Data Visualization.

The Titanic dataset is a classic introductory dataset for machine learning. Traditional methods (logistic regression, random forests, etc.) treat passengers as independent samples and ignore the relationships between them. In reality, passengers share similarities (same cabin class, same family, similar age and fare, etc.), and their survival probabilities may be correlated. GNNs provide a natural way to model relationships. This project demonstrates the technical path of converting tabular data to graph structure + GCN prediction, representing a paradigm shift from independent sample modeling to relational modeling.

The core innovation of the project is using k-NN to construct the graph: 1. Standardize passenger features (age, gender, cabin class, fare, etc.) to ensure consistent scaling; 2. Calculate the Euclidean distance between each passenger and others in the feature space; 3. Select the k nearest neighbors to establish edges, forming a k-NN graph. This method captures implicit relationships between passengers with similar features (e.g., young first-class women may have similar survival outcomes).

GCN is a deep learning architecture for processing graph structures. Unlike CNNs, it needs to handle irregular topologies. The core operation is the graph convolution layer: the new feature of each node is the aggregation of its own features and neighbor features (weighted average + self-transformation). Features: 1. Information propagates in the graph to obtain multi-hop neighbor information; 2. Stacked layers capture neighborhood patterns of different ranges; 3. Permutation invariance (node order does not affect results). In this task, GCN uses graph relationships (e.g., neighbor survival status, family connections) to improve predictions.

Training follows the supervised learning paradigm: split into training/test sets, optimize parameters using cross-entropy loss, and update weights via backpropagation. Evaluation uses ROC-AUC (suitable for imbalanced data; Titanic survival rate is about 38%), and loss curves are plotted to monitor convergence. Theoretically, if the k-NN graph captures effective relationships, GCN should outperform traditional methods; if features are sufficient for prediction, the gain is limited. This comparison provides insights into the applicability of GNNs to tabular data.

The project provides static/interactive visualizations: 1. Graph topology (node connections, community structures such as family/cabin groups) to verify the rationality of the k-NN graph; 2. Color coding to show the distribution of features (cabin class, gender) or labels (survived/deceased). If nodes with similar labels cluster, the graph contains useful information; 3. Compare real and predicted labels, identify error nodes, and analyze model limitations.

Insights: If there are potential relationships in tabular data, converting to a graph structure may bring modeling advantages. Extended applications: Recommendation systems (user-item interaction graphs), social networks (behavior similarity graphs), bioinformatics (gene expression similarity graphs). The key is to define meaningful similarity metrics. Future improvements: Try different graph construction methods (domain knowledge explicit relationships, representation learning adaptive graphs), advanced GNN architectures (GAT, GraphSAGE), and apply to more challenging real-world datasets.