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 | 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 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. |