Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Fast Network Embedding Enhancement via High Order Proximity Approximation
Authors: Cheng Yang, Maosong Sun, Zhiyuan Liu, Cunchao Tu
IJCAI 2017 | Venue PDF | 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. |