Sampling for Approximate Bipartite Network Projection
Authors: Nesreen Ahmed, Nick Duffield, Liangzhen Xia
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real world graphs show that a 10% sample at each stage yields estimates of high similarity edges with weighted relative errors of about 10 2. |
| Researcher Affiliation | Collaboration | Nesreen K. Ahmed1, Nick Duffield2, Liangzhen Xia2 1 Intel Labs, CA 2 Texas A&M University |
| Pseudocode | Yes | Algorithm 1: Adaptive Sampling for Bipartite Projection |
| Open Source Code | No | The paper does not include an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Our evaluations use three datasets comprising bipartite real-world graphs publicly available at Network Repository [Rossi and Ahmed, 2015]. |
| Dataset Splits | No | The paper discusses sampling rates (e.g., 'fm', 'fn') for its algorithm, but it does not specify explicit training, validation, or test dataset splits in terms of percentages or sample counts for reproducibility. |
| Hardware Specification | Yes | The experiments used a 64-bit desktop equipped with an Intel R Core TM i7 Processor with 4 cores running at 3.6 GHz. |
| Software Dependencies | No | The paper mentions implementing a min-heap [Cormen et al., 2001] and refers to its own methods (SIMADAPT, SIMFIXED, SIMUNIF), but does not provide specific names and version numbers for other ancillary software dependencies required for reproducibility. |
| Experiment Setup | Yes | It accepts two reservoir size parameters: m for streaming bipartite edges, and n for similarity matrix estimates. and MOVIE and GITHUB used fm {5%, 10%, 15%, 20%, 25%, 30%}. The RATING achieved the same accuracy with smaller sampling rates {1%, 5%, 10%}. Second stage sampling fractions were fn {5%, 10%, 15%, 100%}, where 100% is exact aggregation. |