Graph-Hist: Graph Classification from Latent Feature Histograms with Application to Bot Detection
Authors: Thomas Magelinski, David Beskow, Kathleen M. Carley5134-5141
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that Graph-Hist improves state of the art performance on true social media benchmark datasets, while still performing well on other benchmarks. Finally, we demonstrate Graph-Hist s performance by conducting bot detection in social media. We apply Graph-Hist to classify these conversational graphs. In the process, we confirm that social media graphs are different than most baselines and that Graph-Hist outperforms existing botdetection models. The mean accuracy and its standard deviation is reported for each dataset in Table 3. Graph-Hist advances state-of-the-art classification in all 3 of the social media benchmarks. It also beats stateof-the-art results for IMDB-B, and obtains second place results for the remaining two datasets. |
| Researcher Affiliation | Academia | Thomas Magelinski, David Beskow, Kathleen M. Carley CASOS, School of Computer Science, Carnegie Mellon University 5000 Forbes Ave. Pittsburgh, PA, 15213 {tmagelin, dbeskow, kathleen.carley}@cs.cmu.edu |
| Pseudocode | No | The paper describes its architecture and procedures through text and mathematical equations, but it does not include a clearly labeled pseudocode block or algorithm. |
| Open Source Code | No | The paper does not provide any statement about making its source code publicly available or provide a link to a code repository. |
| Open Datasets | Yes | The datasets have been obtained from Kersting et al s collection, but were created by Yanardag and Vishwanathan (Kersting et al. 2016; Yanardag and Vishwanathan 2015). |
| Dataset Splits | Yes | We terminated training after 9 consecutive epochs without progress in the testing loss. We then tuned parameters to each dataset in the search space h [2, 4, 6, 8], c [32, 64, 128, 256], ρ [0.2, 0.8]. Parameters were selected based on their performance on the test set. We performed 10-fold cross-validation on each of the datasets using the parameters in Table 2. The new stopping threshold was given by F1 score in the validation set. |
| Hardware Specification | No | The paper states that Graph-Hist was implemented in PyTorch, but it does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions that "Graph-Hist was implemented in Py Torch." However, it does not specify the version of PyTorch or any other software dependencies with version numbers. |
| Experiment Setup | Yes | We used the Reduce LROn Plateau scheduler with an initial learning rate of α = 1e 4, a factor of 0.5, a patience of 2, a cooldown of 0, and a minimum learning rate of 1e 7. We used stochastic gradient descent with a mini-batch size of 32. We terminated training after 9 consecutive epochs without progress in the testing loss. We then tuned parameters to each dataset in the search space h [2, 4, 6, 8], c [32, 64, 128, 256], ρ [0.2, 0.8]. Parameters were selected based on their performance on the test set. The final parameters are shown in Table 2. |