Implementation of Some of the Complex Networks Algorithms From Scratch in Python
- 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)
- 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)
- 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)
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- Course: Complex Networks Analysis - MS
- Teacher: Dr. Mostafa HaghirChehreghani
- Univ: Amirkabir University of Technology
- Semester: Fall 2022
Licensed under MIT.