https://github.com/benedekrozemberczki/resolutions-2019
A list of data mining and machine learning papers that I implemented in 2019.
https://github.com/benedekrozemberczki/resolutions-2019
attention-mechanism deep-learning deepwalk gcn graph-classification graph-clustering graph-convolutional-networks graph-embedding graph-kernel machine-learning network-embedding network-science node-classification node-embedding node2vec pytorch resolution scipy sklearn tensorflow
Last synced: 8 months ago
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A list of data mining and machine learning papers that I implemented in 2019.
- Host: GitHub
- URL: https://github.com/benedekrozemberczki/resolutions-2019
- Owner: benedekrozemberczki
- License: mit
- Created: 2019-03-11T13:28:20.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-12-28T21:47:17.000Z (almost 6 years ago)
- Last Synced: 2025-03-25T11:21:34.548Z (8 months ago)
- Topics: attention-mechanism, deep-learning, deepwalk, gcn, graph-classification, graph-clustering, graph-convolutional-networks, graph-embedding, graph-kernel, machine-learning, network-embedding, network-science, node-classification, node-embedding, node2vec, pytorch, resolution, scipy, sklearn, tensorflow
- Homepage:
- Size: 33.2 KB
- Stars: 21
- Watchers: 4
- Forks: 10
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## Implementing papers 👨🏻💻    
This year my resolution is that I will implement 52 machine learning papers.
- [x] [1. Graph Classification using Structural Attention](https://github.com/benedekrozemberczki/GAM)
- [x] [2. Alternating Direction Method of Multipliers for Non-Negative Matrix Factorization with the Beta-Divergence](https://github.com/benedekrozemberczki/NMF_ADMM)
- [x] [3. SINE: Scalable Incomplete Network Embedding](https://github.com/benedekrozemberczki/SINE)
- [x] [4. Watch Your Step: Learning Graph Embeddings Through Attention](https://github.com/benedekrozemberczki/AttentionWalk)
- [x] [5. Graph Wavelet Neural Network](https://github.com/benedekrozemberczki/GraphWaveletNeuralNetwork)
- [x] [6. Biological Network Comparison Using Graphlet Degree Distribution](https://github.com/benedekrozemberczki/OrbitalFeatures)
- [x] [7. Learning Role-based Graph Embeddings](https://github.com/benedekrozemberczki/role2vec)
- [x] [8. SimGNN: A Neural Network Approach to Fast Graph Similarity Computation](https://github.com/benedekrozemberczki/SimGNN)
- [x] [9. Predict then Propagate: Graph Neural Networks meet Personalized PageRank](https://github.com/benedekrozemberczki/APPNP)
- [x] [10. A Higher Order Graph Convolutional Network](https://github.com/benedekrozemberczki/MixHop-and-N-GCM)
- [x] [11. Ego-splitting Framework: from Non-Overlapping to Overlapping Clusters](https://github.com/benedekrozemberczki/EgoSplitting)
- [x] [12. Splitter: Learning Node Representations that Capture Multiple Social Contexts](https://github.com/benedekrozemberczki/Splitter)
- [x] [13. Capsule Graph Neural Network](https://github.com/benedekrozemberczki/CapsGNN)
- [x] [14. GEMSEC: Graph Embedding With Self-Clustering](https://github.com/benedekrozemberczki/GEMSEC)
- [x] [15. Jump Around! Multi-scale Attributed Node Embedding](https://github.com/benedekrozemberczki/MUSAE)
- [x] [16. Semi-Supervised Graph Classification: A Hierarchical Graph Perspective](https://github.com/benedekrozemberczki/SEAL)
- [x] [17. MixHop: Higher-Order Graph Convolutional Architecturesvia Sparsified Neighborhood Mixing](https://github.com/benedekrozemberczki/MixHop-and-N-GCN)
- [x] [18. GraRep: Learning Graph Representations with Global Structural Information](https://github.com/benedekrozemberczki/GraRep)
- [x] [19. Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks](https://github.com/benedekrozemberczki/ClusterGCN)
- [x] [20. EdMot: An Edge Enhancement Approach for Motif-aware Community Detection](https://github.com/benedekrozemberczki/EdMot)
- [x] [21. Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation](https://github.com/benedekrozemberczki/BoostedFactorization)
- [x] [22. A Non-negative Symmetric Encoder-Decoder Approach
for Community Detection](https://github.com/benedekrozemberczki/karateclub)
- [x] [23. Multi-scale Attributed Node Embedding](https://github.com/benedekrozemberczki/MUSAE)
- [x] [24. Overlapping Community Detection at Scale: A Nonnegative Matrix Factorization Approach](https://github.com/benedekrozemberczki/karateclub)