Mining relationships between performance of link prediction algorithms and network structure

2021 ◽  
Vol 153 ◽  
pp. 111485
Author(s):  
Yongxiang Xia ◽  
Wenbo Pang ◽  
Xuejun Zhang
Keyword(s):  
Network Structure ◽  
Link Prediction ◽  
Prediction Algorithms

scholarly journals The Influence of Network Structural Preference on Link Prediction

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Yongcheng Wang ◽  
Yu Wang ◽  
Xinye Lin ◽  
Wei Wang
Keyword(s):  
Network Structure ◽  
Private Information ◽  
Link Prediction ◽  
Low Cost ◽  
Small Degree ◽  
Privacy And Security ◽  
Protein Protein Interaction ◽  
Prediction Algorithms ◽  
The Social

Link prediction in complex networks predicts the possibility of link generation between two nodes that have not been linked yet in the network, based on known network structure and attributes. It can be applied in various fields, such as friend recommendation in social networks and prediction of protein-protein interaction in biology. However, in the social network, link prediction may raise concerns about privacy and security, because, through link prediction algorithms, criminals can predict the friends of an account user and may even further discover private information such as the address and bank accounts. Therefore, it is urgent to develop a strategy to prevent being identified by link prediction algorithms and protect privacy, utilizing perturbation on network structure at a low cost, including changing and adding edges. This article mainly focuses on the influence of network structural preference perturbation through deletion on link prediction. According to a large number of experiments on the various real networks, edges between large-small degree nodes and medium-medium degree nodes have the most significant impact on the quality of link prediction.

A transportation network evolution model based on link prediction

2021 ◽  
Author(s):  
Shuang Gu ◽  
Keping Li ◽  
Yan Liang ◽  
Dongyang Yan
Keyword(s):  
Network Structure ◽  
Link Prediction ◽  
Transportation Networks ◽  
Network Evolution ◽  
Evolution Model ◽  
Civil Aviation ◽  
Node Degree ◽  
Network Efficiency ◽  
Evolution Mechanism ◽  
Proposed Model

An effective and reliable evolution model can provide strong support for the planning and design of transportation networks. As a network evolution mechanism, link prediction plays an important role in the expansion of transportation networks. Most of the previous algorithms mainly took node degree or common neighbors into account in calculating link probability between two nodes, and the structure characteristics which can enhance global network efficiency are rarely considered. To address these issues, we propose a new evolution mechanism of transportation networks from the aspect of link prediction. Specifically, node degree, distance, path, expected network structure, relevance, population and GDP are comprehensively considered according to the characteristics and requirements of the transportation networks. Numerical experiments are done with China’s high-speed railway network, China’s highway network and China’s inland civil aviation network. We compare receiver operating characteristic curve and network efficiency in different models and explore the degree and hubs of networks generated by the proposed model. The results show that the proposed model has better prediction performance and can effectively optimize the network structure compared with other baseline link prediction methods.

scholarly journals GLIDE: combining local methods and diffusion state embeddings to predict missing interactions in biological networks

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i464-i473
Author(s):  
Kapil Devkota ◽  
James M Murphy ◽  
Lenore J Cowen
Keyword(s):  
Network Structure ◽  
Biological Networks ◽  
Link Prediction ◽  
Prediction Method ◽  
Global Network ◽  
Local Network ◽  
Supplementary Information ◽  
Network Data ◽  
Ppi Network ◽  
Diffusion State

Abstract Motivation One of the core problems in the analysis of biological networks is the link prediction problem. In particular, existing interactions networks are noisy and incomplete snapshots of the true network, with many true links missing because those interactions have not yet been experimentally observed. Methods to predict missing links have been more extensively studied for social than for biological networks; it was recently argued that there is some special structure in protein–protein interaction (PPI) network data that might mean that alternate methods may outperform the best methods for social networks. Based on a generalization of the diffusion state distance, we design a new embedding-based link prediction method called global and local integrated diffusion embedding (GLIDE). GLIDE is designed to effectively capture global network structure, combined with alternative network type-specific customized measures that capture local network structure. We test GLIDE on a collection of three recently curated human biological networks derived from the 2016 DREAM disease module identification challenge as well as a classical version of the yeast PPI network in rigorous cross validation experiments. Results We indeed find that different local network structure is dominant in different types of biological networks. We find that the simple local network measures are dominant in the highly connected network core between hub genes, but that GLIDE’s global embedding measure adds value in the rest of the network. For example, we make GLIDE-based link predictions from genes known to be involved in Crohn’s disease, to genes that are not known to have an association, and make some new predictions, finding support in other network data and the literature. Availability and implementation GLIDE can be downloaded at https://bitbucket.org/kap_devkota/glide. Supplementary information Supplementary data are available at Bioinformatics online.

On the Temporal Network Analysis With Link Prediction

2018 ◽  
Author(s):  
Bin Wu ◽  
C. Steve Suh
Keyword(s):  
Link Prediction ◽  
Probabilistic Models ◽  
Factorization Method ◽  
Prediction Algorithm ◽  
Specific Information ◽  
Evolving Networks ◽  
Prediction Algorithms ◽  
Node Attribute ◽  
The Matrix ◽  
Maximum Likelihood Methods

Literature review shows that much effort has been given to model physical systems involving a large number of interacting constituents. As a network evolves its constituents (or nodes) and associated links would either increase or decrease or both. It is a challenge to extract the specifics that underlie the evolution of a network or indicate the addition and/or removal of links in time. Similarity-based algorithm, Maximum likelihood methods, and Probabilistic models are 3 mainstream methods for link prediction. Methods incorporating topological feature and node attribute are shown to be more effective than most strategies for link prediction. However, to improve prediction accuracy, an effective prediction strategy of practicality is still being sought that captures the characteristics fundamental to a complex system. Many link prediction algorithms have been developed that handle large networks of complexity. These algorithms usually assume that a network is static. They are also computationally inefficient. All these limitations inevitably lead to poor predictions. This paper addresses the link prediction problem by incorporating microscopic dynamics into the matrix factorization method to extract specific information from a time-evolving network with improved link prediction. Numerical experiments in applying static methods to temporal networks show that existing link prediction algorithms all demonstrate unsatisfactory performances in link prediction, thus suggesting that a new prediction algorithm viable for time-evolving networks is required.

Efficient incremental dynamic link prediction algorithms in social network

2017 ◽  
Vol 132 ◽  
pp. 226-235 ◽  
Author(s):  
Zhongbao Zhang ◽  
Jian Wen ◽  
Li Sun ◽  
Qiaoyu Deng ◽  
Sen Su ◽  
...  
Keyword(s):  
Social Network ◽  
Link Prediction ◽  
Prediction Algorithms ◽  
Dynamic Link
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