NS_CW

Various Network Science Projects (2021-2022)

This repo demonstrates multiple Network Science Techniques including:

introduction to network science, Graph manipulation, extracting general statistics about graphs and graph visualization.
Power law degree functions and degree distributions.
Random Graphs generation including Erdos-Renyi model, hyperparameter optimization for the random graph, and general statistics of random graphs.
More Random Graphs models including Barabasi-Albert model and Watts-Strogatz model with their respective statistics.
A deep dive into Centrality measures and Page-Rank Algorithm on the Moscow metro station dataset.
Study structural similarities on co-watch dataset such as Adjacency matrix, Person correlation, Jaccard and Cosine similarity and Assortative Mixing.
Graph partitioning algorithms including Newman, Modularity, Eigenvalues Algorithms and Spectral clustering.
Community detection Algorithms including Agglomerative, Louvian, Ego-Splitting and Label propagation methods.
Simulation of Compartmental Epidemic including Euler, SI, SIS, SIR and SIRS models.
Applying SIS and SIRS models on Graphs. Implementing Random and Selective immunization.
Influence Propagation effects on Graphs including the study of Linear threshold and independent cascade models and influence maximization problem.
Applying ML on Graphs including Node Classification, Label Propagation by random_walk, SVD node embeddings and the Deepwalk model.
Link Prediction on Email network dataset using similarity score, Dot product predictor and edges embeddings.
Deep dive into graph embeddings using DeepWalk, Node2Vec, Hierarchical softmax and GraRep methods.
A Study on the different types of GNN (Graph neural networks) on the Cora dataset.
Knowledge Graphs models including: Translation models, Entitiy embeddings, Nearest neighbors and Tail predictions.

This repo is part of the NS course, HSE, Moscow, Russia.

Name		Name	Last commit message	Last commit date
Latest commit History 7 Commits
Competitions		Competitions
(1) read a graph, nodes labeling, Direected Graph,radius,diameter,ASPL,AC,layout,visulaization .ipynb		(1) read a graph, nodes labeling, Direected Graph,radius,diameter,ASPL,AC,layout,visulaization .ipynb
(10) SIS and SIRS on networks,Random and Selective immunization in SIS , self-isolation .ipynb		(10) SIS and SIRS on networks,Random and Selective immunization in SIS , self-isolation .ipynb
(11) Cascades and influence maximization ,Linear threshold model ,Independent cascade model, Influence maximization problem, Diffusion on networks.ipynb		(11) Cascades and influence maximization ,Linear threshold model ,Independent cascade model, Influence maximization problem, Diffusion on networks.ipynb
(12) ML on Graphs. Node Classification,Assortativity analysis(solved), Relational neighbor classifier , Label propagation by random_walk , Ridge regression on graphs.ipynb		(12) ML on Graphs. Node Classification,Assortativity analysis(solved), Relational neighbor classifier , Label propagation by random_walk , Ridge regression on graphs.ipynb
(13) link prediction, Negative sampling,similarity score,Dot product ,Classification on edge emdeddings .ipynb		(13) link prediction, Negative sampling,similarity score,Dot product ,Classification on edge emdeddings .ipynb
(14) Graph embeddings , DeepWalk, Node2Vec, Hierarchical softmax ,GraRep.ipynb		(14) Graph embeddings , DeepWalk, Node2Vec, Hierarchical softmax ,GraRep.ipynb
(15) GNN , Graph convolution as matrix product , as message passing , GAE , Graph attention network , GraphSAGE neighbor sampling.ipynb		(15) GNN , Graph convolution as matrix product , as message passing , GAE , Graph attention network , GraphSAGE neighbor sampling.ipynb
(16) KG , Translation models , Entity embeddings, Nearest neighbors of entities,Relation prediction,Tail prediction.ipynb		(16) KG , Translation models , Entity embeddings, Nearest neighbors of entities,Relation prediction,Tail prediction.ipynb
(2) power law CDF PDF PFF , estimate a linear binning ,MLE of a in power law, QQ plot.ipynb		(2) power law CDF PDF PFF , estimate a linear binning ,MLE of a in power law, QQ plot.ipynb
(3) Erdos-Renyi model and Fitting parameters, Degree distribution of random vs real networks ,Giant component in random graphs , APL of random networks, CC of random.ipynb		(3) Erdos-Renyi model and Fitting parameters, Degree distribution of random vs real networks ,Giant component in random graphs , APL of random networks, CC of random.ipynb
(4) Watts-Strogatz , APL in WS , Barabasi-Albert and its degree distribution , and CC , Degree dynamics in BA.ipynb		(4) Watts-Strogatz , APL in WS , Barabasi-Albert and its degree distribution , and CC , Degree dynamics in BA.ipynb
(5) Degree, closeness, betweenness centralities,PageRank algorithm and personalized PR , HITS visualization.ipynb		(5) Degree, closeness, betweenness centralities,PageRank algorithm and personalized PR , HITS visualization.ipynb
(6) sim measures (A , Pearsin, Jaccard ,Cosine), Reverse Cuthill-McKee , Assortative Mixing.ipynb		(6) sim measures (A , Pearsin, Jaccard ,Cosine), Reverse Cuthill-McKee , Assortative Mixing.ipynb
(7) Graph partion , Girvan Newman, edge betweennes ,Modularity,Eigenvalues of graph Laplacian ,Spectral clustering.ipynb		(7) Graph partion , Girvan Newman, edge betweennes ,Modularity,Eigenvalues of graph Laplacian ,Spectral clustering.ipynb
(8) Community Detection , Agglomerative clustering,Louvain method ,Label propogation,Ego-Splitting.ipynb		(8) Community Detection , Agglomerative clustering,Louvain method ,Label propogation,Ego-Splitting.ipynb
(9) Compartmental Epidemic Models,Euler method for ODE ,SI model with Numerical vs analytical solution, SIS,SIR,SIRS,estimate param for SIR .ipynb		(9) Compartmental Epidemic Models,Euler method for ODE ,SI model with Numerical vs analytical solution, SIS,SIR,SIRS,estimate param for SIR .ipynb
README.md		README.md

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