Curvature Regularization to Prevent Distortion in Graph Embedding
Authors: Hongbin Pei, Bingzhe Wei, Kevin Chang, Chunxu Zhang, Bo Yang
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We integrate curvature regularization into five popular proximity-preserving embedding methods, and empirical results in two applications show significant improvements on a wide range of open graph datasets. |
| Researcher Affiliation | Academia | 1College of Computer Science and Technology, Jilin University, China 2Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, USA 3Department of Computer Science, University of Illinois at Urbana-Champaign, USA 4Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, China |
| Pseudocode | Yes | Algorithm 1 Curvature regularization optimizing algorithm. 1: input: graph G 2: output: node representations X 3: preprocessing: get paths in G Shortest paths (Ωc and Ωs) or random-walk paths (Ωa) 4: for t iterations do First phase 5: while not converged do 6: minimize embedding loss term L(G) 7: while not converged do 8: minimize curvature regularization term Ω(X) Specific term is Ωc, Ωs, or Ωa 9: while not converged do Second phase 10: minimize the two terms jointly L(G) + λΩ(X) λ is a trade-off hyperparameter |
| Open Source Code | No | The paper does not provide an explicit statement or link for the release of its source code. |
| Open Datasets | Yes | Datasets. We evaluate the proposed method on eight open graph datasets described below (more details are available in the Appendix). (1) Citation networks. Cora, Citeseer and Pubmed are citation network benchmark datasets [22, 23], where nodes are papers and edges are citation links. ... (2) Web KB. ... (3) Polblogs. Political blog network [24]... |
| Dataset Splits | Yes | we split randomly 60% of the nodes in a graph as the training set and the remaining 40% of nodes in a graph as the test set. ... For all node embedding models, we perform a random sampling hyper-parameter search on validation set of each dataset to get competitor models (See detailed hyper-parameter setting in the Appendix). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | Parameter search. For all node embedding models, we perform a random sampling hyper-parameter search on validation set of each dataset to get competitor models (See detailed hyper-parameter setting in the Appendix). The hyper-parameters searched over include the dimension of node representation as well as hyper-parameters specific to each model. We then integrate the curvature regularization term into those competitors, and only adjust the number of iteration t and the weight λ in algorithm. |