This year my resolution is that I will implement at least 52 data mining papers.
- 1. Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and Node2Vec
- 2. Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs
- 3. GL2Vec: Graph Embedding Enriched by Line Graphs with Edge Features
- 4. A Simple Baseline Algorithm for Graph Classification
- 5. Using Laplacian Spectrum as Graph Feature Representation
- 6. Symmetric Nonnegative Matrix Factorization for Graph Clustering
- 7. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
- 8. High Quality, Scalable and Parallel Community Detection for Large Real Graphs
- 9. NetLSD: Hearing the Shape of a Graph
- 10. Asymmetric Transitivity Preserving Graph Embedding
- 11. GEMSEC: Graph Embedding with Self Clustering
- 12. Geometric Scattering for Graph Data Analysis
- 13. Invariant Embedding for Graph Classification
- 14. Attributed Social Network Embedding
- 15. Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models
- 16. Predicting Path Failure In Time-Evolving Graphs
- 17. Structured Sequence Modeling with Graph Convolutional Recurrent Networks
- 18. GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Prediction
- 19. EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graph
- 20. Predictive Temporal Embedding of Dynamic Graphs
- 21. Walking with Perception: Efficient Random Walk Sampling via Common Neighbor Awareness
- 22. Sampling Social Networks Using Shortest Paths
- 23. Leveraging History for Faster Sampling of Online Social Networks
- 24. On Random Walk Based Graph Sampling
- 25. Metric Convergence in Social Network Sampling
- 26. Beyond Random Walk and Metropolis-Hastings Samplers: Why You Should Not Backtrack for Unbiased Graph Sampling
- 27. Sampling Community Structure
- 28. Estimating and Sampling Graphs with Multidimensional Random Walks
- 29. Walking in Facebook: A Case Study of Unbiased Sampling of OSNs
- 30. Metropolis Algorithms for Representative Subgraph Sampling
- 31. Sampling From Large Graphs
- 32. Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations
- 33. Generating Random Spanning Trees More Quickly Than the Cover Time
- 34. Snowball Sampling
- 35. Network Sampling: From Static to Streaming Graphs
- 36. Reducing Large Internet Topologies for Faster Simulations
- 37. SubNets of Scale-Free Networks Are Not Scale-Free: Sampling Properties of Networks
- 38. Search In Power-Law Networks
- 39. GLEE: Geometric Laplacian Eigenmap Embeddings