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..
Deep Reasoning with Knowledge Graph for Social Relationship Understanding
Authors: Zhouxia Wang, Tianshui Chen, Jimmy Ren, Weihao Yu, Hui Cheng, Liang Lin
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on the public benchmarks demonstrate the superiority of our method over the existing leading competitors. |
| Researcher Affiliation | Collaboration | 1 School of Data and Computer Science, Sun Yat-sen University, China 2 Sense Time Research, China |
| Pseudocode | No | The paper describes the model architecture and mechanisms in detail using mathematical formulations and textual descriptions, but it does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide a link to a code repository for the methodology described. |
| Open Datasets | Yes | Extensive experiments on the large-scale People in Social Context (PISC) [Zhang et al., 2015] and the People in Photo Album Relation (PIPA-Relation) [Sun et al., 2017] datasets |
| Dataset Splits | Yes | For fair comparisons, we follow the standard train/val/test split released by [Li et al., 2017] to train and evaluate our GRM. Specifically, for the coarse level relationship, it divides the dataset into a training set of 13,142 images and 49,017 relationship instances, a validation set of 4,000 images and 14,536 instances and a test set of 4,000 images and 15,497 instances. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as particular CPU or GPU models, or memory specifications. |
| Software Dependencies | No | The paper mentions optimizers (SGD, ADAM) and neural network architectures (Res Net-101, VGG-16) but does not provide specific version numbers for any software dependencies or libraries required for reproduction. |
| Experiment Setup | Yes | For the GGNN propagation model, the dimension of the hidden state is set as 4,098 and that of the output feature is set as 512. The iteration time T is set as 3. |