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network-science

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Practical assignments for the network science course

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최종 버전 다운로드 (.zip)

- assignment.ipynb
- assignment.ipynb
- assignment.ipynb
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- assignment.ipynb
- assignment.ipynb
- competition.ipynb
- GK.png
- stats.txt
- - node_feat.txt
  - train_edges.txt
  - unlabeled_edges.txt
- 569720_ego_pokec.gml
- assignment_1_task_1_part_1_edges.txt
- assignment_1_task_1_part_2_adjlist.txt
- collaboration_network_of_arxiv_general_relativity_category.txt
- cora_cites.txt
- cora_content.txt
- countries_edges.tsv
- countries_entities.tsv
- countries_relations.tsv
- covid-dynamics.csv.zip
- email-Eu-core-temporal.txt
- email_degree_sequence.txt
- email_eu_core.txt
- facebook_combined.txt
- footballG.gml
- freebase-237-merged-and-remapped.csv.gz
- game_of_thrones_relationships.csv
- les_miserables_neato.txt
- marketing_edges.txt
- movies.gml
- musae_facebook_ego_802.gml
- painters_graph.gml
- polblogs.gml
- real_net_clustering.txt
- real_net_degree.txt
- routes_data.txt
- stations_data.txt
- the_lord_of_the_rings_adjlist.txt
- the_lord_of_the_rings_edgelist.txt
- the_matrix_graph.gexf
- wiki_vote.txt
- biased_random_walk.png
- ego_splitting.png
- hs_btree.png
- hs_btree_proba.png
- messpass.png
- multiscale_relationships.png
- neighbor_sampling.png
- node_embedding.png
- node_word_powerlaw.png
- pos_neg_edges.png
- watts_strogatz_how_to_rewire.png
.gitignore
project_description.md
README.md

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# Practical Assignments for Network Science

This repository contains practical assignments on network analysis. All assignments are presented as Jupyter notebooks, that can be done by writing code instead of the line 
```
# YOUR CODE HERE
```
All notebooks contain test cells with `assert` statements that help you understand whether your code is correct.

Topics:
1. [Introduction to Network Science](assignment_intro/assignment.ipynb)
2. [Power law and scale-free networks](assignment_power_law/assignment.ipynb)
3. [Random graphs](assignment_random_graphs/assignment.ipynb)
4. [Generative network models](assignment_gen_models/assignment.ipynb)
5. [Node centrality measures](assignment_centrality/assignment.ipynb)
6. [Structural properties](assignment_structural_properties/assignment.ipynb)
7. [Graph partitioning](assignment_graph_partitioning/assignment.ipynb)
8. [Network communities](assignment_network_communities/assignment.ipynb)
9. [Epidemic models](assignment_epidemic_models/assignment.ipynb)
10. [Cascades and influence maximization](assignment_cascades_influence/assignment.ipynb)
11. [Node classification](assignment_node_classification/assignment.ipynb)
12. [Link prediction](assignment_link_prediction/assignment.ipynb)
13. [Graph embeddings](assignment_graph_embeddings/assignment.ipynb)
14. [Graph neural networks](assignment_gnn/assignment.ipynb)
15. [Knowledge graphs](assignment_knowledge_graphs/assignment.ipynb)

Here are also descriptions of competitions held among students to solve practical tasks on graphs:
1. [Network generation](competition_network_generation/competition.ipynb)
2. Marketing campaign
3. Link prediction

Lecture materials: http://leonidzhukov.net/hse/2023/networkscience/

Youtube channel with records of lectures: https://youtube.com/playlist?list=PLriUvS7IljvkGesFRuYjqRz4lKgodJgh2