Feature Hashing for Network Representation Learning
Authors: Qixiang Wang, Shanfeng Wang, Maoguo Gong, Yue Wu
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Compared with the existing state-of-the-art network representation learning approaches, node2hash shows a competitive performance on multi-class node classification and link prediction tasks on three real-world networks from various domains. |
| Researcher Affiliation | Academia | Qixiang Wang1, Shanfeng Wang2, Maoguo Gong1 , Yue Wu3 1 Key Laboratory of Intelligent Perception and Image Understanding, Xidian University, Xi an 710071, China 2 School of Cyber Engineering, Xidian University, Xi an 710071, China 3 School of Computer Science and Technology, Xidian University, Xi an 710071, China omegawangqx@gmail.com, sfwang@xidian.edu.cn, gong@ieee.org, ywu@xidian.edu.cn |
| Pseudocode | Yes | The detailed pseudocode for building proximity matrix is given in Algorithm 1. ... Algorithm 2 The pseudocode of Extract Proximity ... The whole framework of the proposed node2hash algorithm is given in Algorithm 3. |
| Open Source Code | No | No explicit statement or link regarding the release of source code for the proposed method is found in the paper. |
| Open Datasets | Yes | There are three networks considered in our experiments. Citeseer [Mc Callum et al., 2000] is a citation network in which there are 3, 312 scientific publications classified into 6 classes and 4, 732 links among these. Cora [Mc Callum et al., 2000] is also a citation network which is composed of 2, 708 scientific publications from 7 classes and 5, 429 links. Wiki [Sen et al., 2008] contains 2, 405 web pages from 19 categories and 17, 981 links between them. |
| Dataset Splits | No | The paper describes training data and testing data (hidden edges) for link prediction, and varying training ratios for node classification, but does not explicitly mention a 'validation set' or a general validation strategy for their own model training. It mentions 10-fold cross-validation for hyperparameter tuning of the baseline 'node2vec', not for the proposed 'node2hash's general validation. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.x, PyTorch x.x, scikit-learn x.x). |
| Experiment Setup | Yes | The number of walks denoted as n is set 10, the number of walk length denoted as l is set 80, and the window size denoted as w is set 10. ... We utilized the same setting as their paper (n = 10, l = 80, w = 10) and employed a grid search over return and in-out hyperparameters p, q {0.25, 0.5, 1, 1.5, 2} by 10-fold cross-validation. ... Our proposed node2hash owns a same parameter setting with naive feature (n = 10, l = 200, w = 50, T = 2). ... Note that the size of dimensionality d is set 256 for above four mentioned approaches excepted naive feature (d = |V |). ... We pay most attention on the impact of the size of hash set T. Therefore, we fix the number of walks, walk length and window size (n = 10, l = 200,w = 50) and test the effects of node2hash with different T. |