Network Schema Preserving Heterogeneous Information Network Embedding
Authors: Jianan Zhao, Xiao Wang, Chuan Shi, Zekuan Liu, Yanfang Ye
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three real-world datasets demonstrate that our proposed model NSHE significantly outperforms the state-of-the-art methods. |
| Researcher Affiliation | Academia | 1School of CS, Beijing University of Posts and Telecommunications, Beijing, China 2Department of CDS, Case Western Reserve University, OH, USA |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and dataset is publicly available on Github1. |
| Open Datasets | Yes | DBLP [Lu et al., 2019]: We extract a subset of DBLP which contains 9556 papers (P), 2000 authors (A), and 20 conferences (C). [...] IMDB [Wang et al., 2019]: We extract a subset of IMDB which contains 3676 movies (M), 4353 actors (A), and 1678 directors (D). [...] ACM [Wang et al., 2019]: We extract papers published in KDD, SIGMOD, SIGCOMM, Mobi COMM, and VLDB and divide them into three classes: database, wireless communication, and data mining. |
| Dataset Splits | No | The paper states "we train a logistic classifier with 80% of the labeled nodes and use the remaining data for testing." but does not explicitly mention a separate validation split or its proportion. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper mentions using "Adam [Kingma and Ba, 2015] algorithm" but does not specify version numbers for any software libraries, frameworks, or programming languages used for implementation. |
| Experiment Setup | Yes | For our proposed model, the feature dimension in common space and the embedding dimension d is set as 128. The negative schema instance sample rate Ms in Section 3.2 is set as 4. We perform neighborhood aggregation via an one-layer GCN, i.e., L = 1, and use two-layer-MLPs for schema instance classification. |