Telling Peer Direct Effects from Indirect Effects in Observational Network Data
Authors: Xiaojing Du, Jiuyong Li, Debo Cheng, Lin Liu, Wentao Gao, Xiongren Chen, Ziqi Xu
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two semi-synthetic datasets derived from real-world networks and on a dataset from a recommendation system confirm the effectiveness of our approach. |
| Researcher Affiliation | Academia | 1University of South Australia, Adelaide, Australia 2School of Computing Technologies, RMIT University, Melbourne, Australia. Correspondence to: Jiuyong Li <EMAIL>, Debo Cheng <EMAIL>. |
| Pseudocode | Yes | D. The g DIS Algorithm. The pseudo-code of g DIS for estimating PDE, PIE and STE is presented in Algorithm 1. |
| Open Source Code | No | The paper does not provide explicit access to source code for the methodology described. It mentions using "Tensor Flow (Abadi et al., 2016)" and "Network X (Hagberg et al., 2008)" but these are third-party libraries, not the authors' own code release. |
| Open Datasets | Yes | We applied g DIS to two semi-synthetic real-world datasets, Blog Catalog (BC) and Flickr (Li et al., 2015), to evaluate its effectiveness on estimating PDE, PIE and STE. ... Blog Catalog2 (BC): ... 2https://www.blogcatalog.com/ ... Flickr3: ... 3https://www.flickr.com/ ... we conducted experiments using the Coat Dataset1, which simulates MNAR (missing not at random) data from online coat purchases. As described in (Schnabel et al., 2016), the dataset includes user features and 5-point ratings ... 1https://www.cs.cornell.edu/ schnabts/ mnar/ |
| Dataset Splits | Yes | The resulting partitioned data distribution is shown in Table 5. Table 5. Data distribution after graph partitioning. Each tuple (m, k) indicates the number of nodes (m) and the feature dimension (k). Dataset Train Validation Test Blog Catalog (1722, 10) (1733, 10) (1731, 10) Flickr (1557, 10) (2526, 10) (1829, 10) |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. It mentions software libraries but no CPU, GPU, or memory specifications. |
| Software Dependencies | No | The paper mentions that the model is implemented using "Python libraries Tensor Flow (Abadi et al., 2016) and Network X (Hagberg et al., 2008)", but it does not specify the version numbers for these software components. |
| Experiment Setup | Yes | In this section, we provide additional details about the experimental setup and configurations. Our proposed model is implemented using the Python libraries Tensor Flow (Abadi et al., 2016) and Network X (Hagberg et al., 2008). We performed a grid search on the validation set to select the key parameters (learning rate, hidden dimensions, and regularization strength) that yielded the best performance, as shown in Table 6. Table 7 summarizes the parameter settings used for peer effect analysis on Blog Catalog and Flickr. |