From which world is your graph
Authors: Cheng Li, Felix MF Wong, Zhenming Liu, Varun Kanade
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report several experiments on synthetic and real-world data collected on Twitter from Oct 1 and Nov 30, 2016. Our experiments demonstrate that our model and inference algorithms perform well on real-world data and reveal interesting structures in networks. |
| Researcher Affiliation | Collaboration | Cheng Li College of William & Mary Felix M. F. Wong Independent Researcher Zhenming Liu College of William & Mary Varun Kanade University of Oxford Currently at Google. |
| Pseudocode | Yes | Figure 1: Subroutines of our Latent Inference Algorithm. LATENT-INFERENCE(A) 1 // Step 1. Estimate Φ . 2 bΦ = SM-EST(A). 3 // Step 2. Execute isomap algo. 4 D = ISOMAP-ALGO(bΦ) 5 // Step 3. Find latent variables. 6 Run a line embedding algorithm [48, 49]. ISOMAP-ALGO(bΦ, ℓ) 1 Execute S DENOISE(bΦ) (See Section 3.2) 2 // S is a subset of [n]. 3 Build G = {S, E} s.t. {i, j} E iff 4 |( Φd)i ( Φd)j| ℓ/ log n (ℓa constant). 5 Compute D such D(i, j) is the shortest 6 path distance between i and j when i, j S. 7 return D SM-EST(A, t) 1 [ UA, SA, VA] = svd(A). 2 Let also λi be i-th singular value of A. 3 // let t be a suitable parameter. 4 d = DECIDETHRESHOLD(t, ρ(n)). 5 SA: diagonal matrix comprised of {λi}i d 6 UA, VA: the singular vectors 7 corresponding to SA. 8 Let bΦ = p C(n)UAS1/2 A . 9 return bΦ DECIDETHRESHOLD(t, ρ(n)) 1 // This procedure decides d the number 2 of Eigenvectors to keep. 3 // t is a tunable parameter. See Proposition 3.1. 4 d = arg maxd{λd( A ρ(n)) λd+1( A ρ(n)) θ}. 5 where θ = 10(t/ρ(n))24/59 |
| Open Source Code | No | The paper does not provide any concrete access (link, explicit statement of release) to the source code for the methodology described. |
| Open Datasets | No | The paper mentions 'real-world data collected on Twitter from Oct 1 and Nov 30, 2016' and 'Ideology scores of the US Congress (estimated by third parties [57])' as ground-truth. However, it does not provide specific links, DOIs, repositories, or explicit statements for public access to the collected Twitter dataset used in the experiments. While [57] is cited for ground truth, the primary dataset (Twitter data) is described as collected by the authors without public access details. |
| Dataset Splits | No | The paper does not provide specific details on dataset splits (e.g., percentages, sample counts, or references to predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (GPU, CPU models, memory, etc.) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values, optimizer settings, or other training configurations. |