Fast Change Point Detection on Dynamic Social Networks
Authors: Yu Wang, Aniket Chakrabarti, David Sivakoff, Srinivasan Parthasarathy
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our algorithm achieves up to 9X speedup over the state-of-the-art while improving quality on both synthetic and real world networks. ... We demonstrate the utility of our algorithm on both synthetic and real world networks ... We did thorough evaluation of our edge probability estimation based change point detection algorithm (called Edge Monitoring for simplicity) on synthetic and real world datasets. |
| Researcher Affiliation | Academia | Yu Wang Aniket Chakrabarti David Sivakoff# Srinivasan Parthasarathy Department of Computer Science and Engineering # Department of Statistics The Ohio State University, Columbus, Ohio, USA wang.5205@osu.edu, srini@cse.ohio-state.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about the release of open-source code for the described methodology, nor does it provide a direct link to a code repository. |
| Open Datasets | Yes | We construct a co-sponsorship network from bills (co-)sponsored in US Senate during the 93rd-108th Congress. An edge is formed between two congresspersons if they cosponsored the same bill. Each bill corresponds to a snapshot... ([Fowler, 2006]) ... Klimt and Yang, 2004; Asur et al., 2007 (for communication networks, which includes Enron) ... Enron 150 weekly... |
| Dataset Splits | No | The paper uses a sliding window approach for data processing ("We define Wt to be a subsequence of s consecutive observed networks ending at network Gt, so Wt (Gt s+1, Gt s+2, . . . , Gt)"), but it does not specify explicit train/validation/test dataset splits. |
| Hardware Specification | No | Table 3, footnote 1, states: "All run on a commercial desktop with 60hrs as time limit." This does not provide specific hardware details such as CPU, GPU models, or memory. |
| Software Dependencies | No | Table 3, footnote 1, states: "EM and DC are implemented in MATLAB while LC in Python." It specifies the software environments but does not provide specific version numbers for MATLAB, Python, or any other libraries used. |
| Experiment Setup | Yes | We ran experiments with network sizes ranging from 1k to 50k, window size to be from 10 to 100 and continuity rate 1 α to be 0.51 and 0.9. We generated a total of 5000 snapshots and sampled 250 edges uniformly at random to track. ... For KL and KS, edges are grouped into 25 equal-sized groups. We use upper 5% quantile as the threshold. |