Uncovering the Formation of Triadic Closure in Social Networks
Authors: Zhanpeng Fang, Jie Tang
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed model on a large collaboration network, and the experimental results show that our method improves the accuracy of decoding triadic closure by up to 20% over that of several alternative methods. |
| Researcher Affiliation | Academia | Zhanpeng Fang and Jie Tang Department of Computer Science and Technology, Tsinghua University Tsinghua National Laboratory for Information Science and Technology (TNList) Jiangsu Collaborative Innovation Center for Language Ability, Jiangsu Normal University, China fzp13@mails.tsinghua.edu.cn, jietang@tsinghua.edu.cn |
| Pseudocode | Yes | Algorithm 1: Learning algorithm for the De Triad model. |
| Open Source Code | Yes | All codes and data used in this paper are publicly available.1 |
| Open Datasets | Yes | The dataset is from Arnet Miner [Tang et al., 2008]. |
| Dataset Splits | No | We randomly divide the closed triads into two subsets of even size: training and test. The paper does not explicitly mention a validation split. |
| Hardware Specification | Yes | All algorithms are implemented in C++, and all experiments are performed on an x64 machine with E5-4650 2.70GHz Intel Xeon CPU (with 64 cores) and 1TB RAM. The operating system is Ubuntu 12.04. |
| Software Dependencies | No | All algorithms are implemented in C++, and all experiments are performed on an x64 machine... For SVM, we use svm-perf [Joachims, 2006]. We employ liblinear [Fan et al., 2008] here. The paper names software but does not provide specific version numbers for reproducibility. |
| Experiment Setup | No | The paper mentions a “learning rate η” in Algorithm 1 but does not provide specific numerical values for hyperparameters or training configurations within the main text. |