Inferring Motif-Based Diffusion Models for Social Networks
Authors: Qing Bao, William K. Cheung, Jiming Liu
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For performance evaluation, we apply the proposed model to three real-world social network datasets with significant improvement on modelling accuracy compared with some recent work. 4 Experiments We compare our model with some recently proposed diffusion models using three real-world social and information network datasets. Figure 1 shows the experimental results. |
| Researcher Affiliation | Academia | Qing Bao, William K. Cheung, and Jiming Liu Dept. of Computer Science, Hong Kong Baptist University, Hong Kong |
| Pseudocode | No | The paper describes the two-level EM algorithm verbally and mathematically but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any statement about releasing the source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | Yes | Three real datasets are used for the evaluation, namely Meme Tracker [Leskovec et al., ], Digg [Lerman and Ghosh, 2010] and Flixster [Jamali and Ester, 2010] where both the network structure and the information cascades are available. Meme Tracker: Download Meme Tracker data. [Online]. Available: http://www.memetracker.org/data.html. |
| Dataset Splits | Yes | Also, five-fold cross-validation is adopted to avoid experimental bias. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers. |
| Experiment Setup | Yes | For all the experiments performed, the initial values of {ˆ m,w} are within [0, 0.1] as the diffusion probabilities in real data are known to be very small (e.g., with a mean value of 0.04 and standard deviation of 0.07 [Gruhl et al., 2004]). The initial values of {ˆ m ij } are generated within [0, 1]. And the initial values of m are generated within [0, 1] satisfying P m ˆ m = 1. Also, for COMP-IC, LCM-IC and Motif-IC, we obtain the optimal number of model components using the cross-validation method. |