Fast Network Embedding Enhancement via High Order Proximity Approximation
Authors: Cheng Yang, Maosong Sun, Zhiyuan Liu, Cunchao Tu
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on multi-label classification and link prediction tasks. Experimental results show that NEU can make a consistent and significant improvement over a number of NRL methods with almost negligible running time on all three publicly available datasets. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Technology, Tsinghua University, Beijing, China 2Jiangsu Collaborative Innovation Center for Language Ability, Jiangsu Normal University, Xuzhou, China |
| Pseudocode | No | The paper describes the NEU algorithm using mathematical equations (Eq. 3 and 5) but does not present a formal pseudocode block or algorithm listing. |
| Open Source Code | Yes | The source code of this paper can be obtained from https://github.com/thunlp/NEU. |
| Open Datasets | Yes | We conduct experiments on three publicly available datasets: Cora1 [Sen et al., 2008], Blog Catalog and Flickr2 [Tang and Liu, 2011]. 1http://linqs.cs.umd.edu/projects/ /projects/lbc/index.html. 2http://socialcomputing.asu.edu/pages/ datasets. |
| Dataset Splits | Yes | For multi-label classification task, we randomly select a portion of vertices as training set and leave the rest as test set. We set the hyperparameters of NEU as follows: λ1 = 0.5, λ2 = 0.25 for all three datasets, T = 3 for Cora and Blog Catalog and T = 1 for Flickr. Here λ1, λ2 are set empirically following the intuition that lower proximity matrix should have a higher weight and T is set as the maximum iteration before the performance on 10% random validation set begins to drop. |
| Hardware Specification | Yes | The experiments are executed on a single CPU for the ease of running time comparison and the CPU type is Intel Xeon E5-2620 @ 2.0GHz. |
| Software Dependencies | No | The paper mentions using "Lib Linear [Fan et al., 2008]" but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We set the hyperparameters of NEU as follows: λ1 = 0.5, λ2 = 0.25 for all three datasets, T = 3 for Cora and Blog Catalog and T = 1 for Flickr. |