Link Prediction via Subgraph Embedding-Based Convex Matrix Completion
Authors: Zhu Cao, Linlin Wang, Gerard de Melo
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on several datasets show the effectiveness of our method compared to previous work. We extensively evaluate our algorithm across a range of heterogeneous real-world datasets, and also demonstrate its scalability on large networks of up to a million nodes. The experiments show that our methods yield state-of-the-art link prediction results on all evaluated datasets. |
| Researcher Affiliation | Academia | IIIS, Tsinghua University, Beijing, China Rutgers University, New Brunswick, NJ, USA |
| Pseudocode | Yes | Algorithm 1 Overall Algorithm, Algorithm 2 V, C=Vocabulary(G,D), Algorithm 3 PPMI Matrix M= Rep(V, C), Algorithm 4 W=SOFT-IMPUTE(M), Algorithm 6 Rep2Score(M, u, v) |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | We consider the following datasets: Facebook (Mc Auley and Leskovec 2012; Leskovec and Krevl 2014): We use a Facebook social network dataset... Wikipedia (Leskovec and Krevl 2014): This real-world dataset is collected from Wikipedia... Coauthorship (Leskovec and Krevl 2014): This realworld dataset is formed from the coauthor network of general relativity section on ar Xiv. PPI (Breitkreutz et al. 2008): This protein-protein interaction network... Leskovec, J., and Krevl, A. 2014. SNAP Datasets: Stanford large network dataset collection. http://snap.stanford.edu/ data. |
| Dataset Splits | Yes | For each dataset, the observed edges E are split into two parts ET and EP , where ET is used for training and EP for testing. The splitting is performed with 5-fold cross-validation. That is, the observed edges are split to five equal parts. Then we repeat 5 times, each time take one part as the test set and the rest four parts as the training set. |
| Hardware Specification | Yes | These experiments are run on a laptop with 2.8 GHz CPU and 8G memory. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | For these experiments, the learning rate η and the ratio of regularization λ are optimized to be 0.3 and 0.001, respectively, according to cross validation within the training set. The depth D is taken to be 3 for the datasets FOrig, Wiki, Coauth, and 2 for the dataset PPI. For these tests, we fix the number of inner iterations to an appropriate value (i = 100), which ensures convergence. |