Dependent Relational Gamma Process Models for Longitudinal Networks
Authors: Sikun Yang, Heinz Koeppl
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on a simulation study and three realworld temporal network data sets demonstrate the model s capability, competitive performance and scalability compared to state-of-the-art methods. |
| Researcher Affiliation | Academia | Sikun Yang 1 Heinz Koeppl 1 1Department of Electrical Engineering and Information Technology, Technische Universit at Darmstadt, Germany. |
| Pseudocode | No | The paper states: 'The complete Gibbs sampling algorithm and additional experimental results are presented in the supplementary material.' There is no pseudocode or algorithm block directly in the main body of the paper. |
| Open Source Code | No | The paper provides links to the code for baseline methods (DRIFT, DSBM, HGPEPM) but does not provide a link or explicit statement about the availability of the source code for the proposed DRGPM model. |
| Open Datasets | Yes | We consider the following data sets: (1) Face-to-face dynamic contacts network (FFDC): This dataset (Mastrandrea et al., 2015) records timestamped face-to-face contacts among 180 students for 7 school days. (2) DBLP: The DBLP co-authorship network data (Asur et al., 2009) contains the co-authorship information among 958 authors over ten years (1997-2006)... (3) Enron: The Enron data4 contains 517,431 emails among 151 users over 38 months (from May 1999 to June 2002). 4https://www.cs.cmu.edu/ enron/. |
| Dataset Splits | No | We randomly hold out 20% of the network entries (either links or non-links) for each snapshot as test data, and use the remaining 80% to predict the held-out entries. No explicit mention of a separate validation split percentage or count was found. |
| Hardware Specification | Yes | All the experiments were run on a standard desktop with 2.7 GHz CPU and 24 GB RAM. |
| Software Dependencies | No | The paper states: '(all models are implemented in Matlab)'. However, no specific version numbers for Matlab or any other software dependencies are provided. |
| Experiment Setup | Yes | In the experiments, we set the hyperparameters for our model as γ0 = 1, β = 1, c = 1, τ = 1. Unless otherwise stated, we use K = N/2 for initilization, where N is the number of nodes. For all probabilistic methods, we use 2000 burn-in iterations, and collect 1000 samples from the model posterior distribution. |