# Link Prediction with Node Clustering Coefficient

arXiv: https://arxiv.org/abs/1510.07819 · HTML: https://mlsanity.com/paper/1510.07819/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2015.

## Built on by (most cited first, 5 of 5)

- [Line Graph Contrastive Learning for Link Prediction](https://mlsanity.com/paper/2210.13795/) (2022)
- [Can GNNs Learn Link Heuristics? A Concise Review and Evaluation of Link Prediction Methods](https://mlsanity.com/paper/2411.14711/) (2024)
- [A Preference Random Walk Algorithm for Link Prediction through Mutual Influence Nodes in Complex Networks](https://mlsanity.com/paper/2105.09494/) (2021)
- [Temporal Link Prediction via Adjusted Sigmoid Function and 2-Simplex Sructure](https://mlsanity.com/paper/2206.09529/) (2022)
- [Enhancing Community Detection in Networks: A Comparative Analysis of Local Metrics and Hierarchical Algorithms](https://mlsanity.com/paper/2408.09072/) (2024)

## Abstract (arXiv)

Predicting missing links in incomplete complex networks efficiently and accurately is still a challenging problem. The recently proposed CAR (Cannistrai-Alanis-Ravai) index shows the power of local link/triangle information in improving link-prediction accuracy. With the information of level-2 links, which are links between common-neighbors, most classical similarity indices can be improved. Nevertheless, calculating the number of level-2 links makes CAR index not efficient enough. Inspired by the idea of employing local link/triangle information, we propose a new similarity index with more local structure information. In our method, local link/triangle structure information can be conveyed by clustering coefficient of common neighbors directly. The reason why clustering coefficient has good effectiveness in estimating the contribution of a common-neighbor is because that it employs links existing between neighbors of the common-neighbor and these links have the same structural position with the candidate link to this common-neighbor. Ten real-world networks drawn from five various fields are used to test the performance of our method against to classical similarity indices and recently proposed CAR index. Two estimators: precision and AUP, are used to evaluate the accuracy of link prediction algorithms. Generally speaking, our new index only performs competitively with CAR, but it is a good complement to CAR for networks with not very high LCP-corr, which is a measure to estimate the correlation between number of common-neighbors and number of links between common-neighbors. Besides, the proposed index is also more efficient than CAR index.