Exploiting k-Degree Locality to Improve Overlapping Community Detection
Authors: Hongyi Zhang, Michael R. Lyu, Irwin King
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare our LNMF model with several baseline methods on various real-world networks, including large ones with ground-truth communities. Results show that our model outperforms state-of-the-art approaches. |
| Researcher Affiliation | Academia | 1Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications, Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China 2Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong |
| Pseudocode | Yes | Algorithm 1 Community Detection via LNMF; Algorithm 2 Sampling Strategy |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described, nor does it explicitly state that the code is publicly available. |
| Open Datasets | Yes | Six benchmark networks collected by Newman1 are used as our datasets. [...] 1http://www-personal.umich.edu/~mejn/netdata/; Moreover, we choose three large networks with groundtruth communities collected by SNAP2 [Yang and Leskovec, 2012] to test the scalability of our model. [...] 2http://snap.stanford.edu/data/ |
| Dataset Splits | Yes | In details, we reserve 10% of nodes as validation set at first. |
| Hardware Specification | Yes | We conduct our experiments on a computer with a Xeon 2.60GHz CPU and 64GB memory. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers, such as programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | Yes | We set the regularization coefficient to be 0.5 and the convergence parameter ϵ to be 0.001 for all experiments. The sample size t is determined according to data size. For Newman s datasets, we set t = m, i.e., the number of links. For SNAP datasets, we set t = 10 n in order to finish one iteration without taking too much time, where n is the number of nodes. The maximum times of iteration is set to 100, though in fact all datasets converge before reaching the limit. |