geraked / complex-networks

Implementation of Complex Networks Algorithms

Complex Networks Analysis

Implementation of Some of the Complex Networks Algorithms From Scratch in Python

Homework 1

Problems | Solutions (Report)

• Erdos-Renyi Random Graph, Small-World Model (Watts-Strogatz), Degree Distribution, Clustering Coefficient, Comparison with Real-world data (Source code)
• Structural (Percolation) Phase Transition, Largest Connected Component, Giant Component (Source code)
• Influence Maximization through Greedy and CELF Algorithms, Independent Cascade Model (Source code)
• Outbreak Detection through Greedy and CELF Algorithms (Source code)

Homework 2

Problems | Solutions (Report)

• Implementing and Comparing the Centrality Metrics (Closeness, Efficiency, Degree, Katz) (Source code)
• Spectral Clustering (Partitioning) Algorithm, Modularity and Min-cut Metrics (Source code)
• Community Detection Using Fast Modularity Optimization Algorithm (Source code)

Final Project

Problems | Solutions (Report)

• DBLP Node Classification, Heterogeneous Graph Neural Network (HGNN), Graph Convolutional Network (GCN), Graph Attention Network (GAT) (Source code)
• Simplifying Graph Convolutional Networks (SGC), PyTorch Geometric (PyG), GNNs (Source code)

Author

Rabist - view on LinkedIn

Details

• Course: Complex Networks Analysis - MS
• Teacher: Dr. Mostafa HaghirChehreghani
• Univ: Amirkabir University of Technology
• Semester: Fall 2022

License

Licensed under MIT.