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..
Graph Structure Learning on User Mobility Data for Social Relationship Inference
Authors: Guangming Qin, Lexue Song, Yanwei Yu, Chao Huang, Wenzhe Jia, Yuan Cao, Junyu Dong
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three real-world datasets demonstrate the superiority of SRINet against state-of-the-art techniques in inferring social relationships from user mobility data. |
| Researcher Affiliation | Academia | 1College of Computer Science and Technology, Ocean University of China 2Department of Data Science, Duke Kunshan University 3Department of Computer Science, University of Hong Kong |
| Pseudocode | No | The paper includes a block diagram (Figure 1) illustrating the framework, but it does not provide any sections or figures explicitly labeled as 'Pseudocode' or 'Algorithm', nor does it present structured steps in a code-like format. |
| Open Source Code | Yes | The source code of our method is available at https://github.com/qinguangming1999/SRINet. |
| Open Datasets | Yes | We use three publicly available real-world mobility datasets, i.e., Gowalla, Brightkite (Cho, Myers, and Leskovec 2011), and Foursquare (Yang et al. 2019), to evaluate the performance of models. |
| Dataset Splits | Yes | In our experiments, we use 25% friendships in social networks on each dataset as training set. Then we randomly sample another 5% friendship for validating, and use the remaining 70% for testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU or CPU models, memory, or cloud computing specifications. |
| Software Dependencies | No | The paper mentions tuning parameters such as learning rate, dropout, and weight-decay, but it does not specify any software dependencies (e.g., libraries, frameworks, or programming languages) along with their version numbers. |
| Experiment Setup | Yes | For our SRINet, we set user embedding dimension d to 512 unless stated otherwise, the number of convolution layers L to 2, tune learning rate from 0.0001 to 0.01, dropout to 0.01, and weight-decay to 0.0001, use early stopping mechanism, and set patience to 10 to avoid overfitting. The coefficient ω is set to 0.003 for three datasets. To capture more potential meeting events, we set τ to 2 hours. |