Adaptive Shrinkage Estimation for Streaming Graphs
Authors: Nesreen Ahmed, Nick Duffield
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments on large networks show that our approach is superior to baseline methods. |
| Researcher Affiliation | Collaboration | Nesreen K. Ahmed Intel Labs Santa Clara, CA 95054 nesreen.k.ahmed@intel.com Nick Duffield Texas A&M University College Station, TX 77843 duffieldng@tamu.edu |
| Pseudocode | Yes | Algorithm 1 Adaptive Priority Sampling (APS) |
| Open Source Code | No | The paper does not provide a statement or link for the open-sourcing of its code. |
| Open Datasets | Yes | Experimental Setup. We test on graphs from different domains and with different characteristics; see [40] for data downloads. Table 1 provides a summary of dataset characteristics, where |V | is the number of vertcies, |K| is the number of edges, T is the number of triangles, and Tmax is the maximum triangle count per edge. |
| Dataset Splits | No | The paper discusses "sample fractions" for the sampling methods but does not define training, validation, and test splits for a machine learning model, nor does it specify cross-validation. |
| Hardware Specification | Yes | All experiments were performed using a server with two Intel Xeon E5-2687W 3.1GHz CPUs, 256GB of memory. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | Experimental Setup. We test on graphs from different domains and with different characteristics; see [40] for data downloads. ... We repeat the experiment ten different times with sample fractions f = {0.10, 0.20, 0.40, 0.50}. ... Our experimental setup is summarized as follows: For each sample fraction, we use Algorithm 1 to collect a sample b K, from edge stream K. The experiments in this section use triangles as an example of the motif pattern M. ... We compare the results of Algorithm 1 with uniform sampling (i.e., reservoir sampling [53]) using the Horvitz-Thompson estimator, and we also compare with Triest sampling [48]. |