Link prediction based on local major path degree

2018 ◽  
Vol 32 (29) ◽  
pp. 1850348
Author(s):  
Xu-Hua Yang ◽  
Xuhua Yang ◽  
Fei Ling ◽  
Hai-Feng Zhang ◽  
Duan Zhang ◽  
...  
Keyword(s):  
Data Mining ◽  
Biological Networks ◽  
Link Prediction ◽  
Prediction Performance ◽  
Local Information ◽  
Network Nodes ◽  
And Topology ◽  
Similarity Indices ◽  
High Prediction ◽  
Similarity Information

Link prediction can estimate the probablity of the existence of an unknown or future edges between two arbitrary disconnected nodes (two seed nodes) in complex networks on the basis of information regarding network nodes, edges and topology. With the important practical value in many fields such as social networks, electronic commerce, data mining and biological networks, link prediction is attracting considerable attention from scientists in various fields. In this paper, we find that degree distribution and strength of two- and three-step local paths between two seed nodes can reveal effective similarity information between the two nodes. An index called local major path degree (LMPD) is proposed to estimate the probability of generating a link between two seed nodes. To indicate the efficiency of this algorithm, we compare it with nine well-known similarity indices based on local information in 12 real networks. Results show that the LMPD algorithm can achieve high prediction performance.

scholarly journals Ordinal classification for efficient plant stress prediction in hyperspectral data

2014 ◽  
Vol XL-7 ◽  
pp. 29-36 ◽  
Author(s):  
J. Behmann ◽  
P. Schmitter ◽  
J. Steinrücken ◽  
L. Plümer
Keyword(s):  
Linear Models ◽  
Plant Stress ◽  
Crop Protection ◽  
Local Stress ◽  
Prediction Performance ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Support Vector ◽  
Data Set ◽  
High Prediction

Detection of crop stress from hyperspectral images is of high importance for breeding and precision crop protection. However, the continuous monitoring of stress in phenotyping facilities by hyperspectral imagers produces huge amounts of uninterpreted data. In order to derive a stress description from the images, interpreting algorithms with high prediction performance are required. Based on a static model, the local stress state of each pixel has to be predicted. Due to the low computational complexity, linear models are preferable. <br><br> In this paper, we focus on drought-induced stress which is represented by discrete stages of ordinal order. We present and compare five methods which are able to derive stress levels from hyperspectral images: One-vs.-one Support Vector Machine (SVM), one-vs.-all SVM, Support Vector Regression (SVR), Support Vector Ordinal Regression (SVORIM) and Linear Ordinal SVM classification. The methods are applied on two data sets - a real world set of drought stress in single barley plants and a simulated data set. It is shown, that Linear Ordinal SVM is a powerful tool for applications which require high prediction performance under limited resources. It is significantly more efficient than the one-vs.-one SVM and even more efficient than the less accurate one-vs.-all SVM. Compared to the very compact SVORIM model, it represents the senescence process much more accurate.

scholarly journals Candidate gene prioritization using graph embedding

2020 ◽  
Author(s):  
Quan Do ◽  
Pierre Larmande
Keyword(s):  
Link Prediction ◽  
Graph Embedding ◽  
Prediction Performance ◽  
Knowledge Graph ◽  
Learning Techniques ◽  
Number Of Genes ◽  
Important Amount ◽  
Candidate Gene Prioritization ◽  
Gene Information ◽  
Embedding Methods

AbstractCandidate genes prioritization allows to rank among a large number of genes, those that are strongly associated with a phenotype or a disease. Due to the important amount of data that needs to be integrate and analyse, gene-to-phenotype association is still a challenging task. In this paper, we evaluated a knowledge graph approach combined with embedding methods to overcome these challenges. We first introduced a dataset of rice genes created from several open-access databases. Then, we used the Translating Embedding model and Convolution Knowledge Base model, to vectorize gene information. Finally, we evaluated the results using link prediction performance and vectors representation using some unsupervised learning techniques.

Link prediction via layer relevance of multiplex networks

2017 ◽  
Vol 28 (08) ◽  
pp. 1750101 ◽  
Author(s):  
Yabing Yao ◽  
Ruisheng Zhang ◽  
Fan Yang ◽  
Yongna Yuan ◽  
Qingshuang Sun ◽  
...  
Keyword(s):  
Structural Properties ◽  
Link Prediction ◽  
Structural Information ◽  
Similarity Index ◽  
Single Layer ◽  
Structural Features ◽  
Prediction Performance ◽  
Multiplex Networks ◽  
Multiplex Network ◽  
Node Similarity

In complex networks, the existing link prediction methods primarily focus on the internal structural information derived from single-layer networks. However, the role of interlayer information is hardly recognized in multiplex networks, which provide more diverse structural features than single-layer networks. Actually, the structural properties and functions of one layer can affect that of other layers in multiplex networks. In this paper, the effect of interlayer structural properties on the link prediction performance is investigated in multiplex networks. By utilizing the intralayer and interlayer information, we propose a novel “Node Similarity Index” based on “Layer Relevance” (NSILR) of multiplex network for link prediction. The performance of NSILR index is validated on each layer of seven multiplex networks in real-world systems. Experimental results show that the NSILR index can significantly improve the prediction performance compared with the traditional methods, which only consider the intralayer information. Furthermore, the more relevant the layers are, the higher the performance is enhanced.

An Overview of Biological Data Mining

Biotechnology ◽  
2019 ◽  
pp. 120-139
Author(s):  
Seetharaman Balaji
Keyword(s):  
Data Mining ◽  
Information Retrieval ◽  
Biological Networks ◽  
Web Mining ◽  
Biological Data ◽  
Biological Research ◽  
Digital Repository ◽  
Domain Specific ◽  
Novice Learner ◽  
Integration Data

The largest digital repository of information, the World Wide Web keeps growing exponentially and calls for data mining services to provide tailored web experiences. This chapter discusses the overview of information retrieval, knowledge discovery and data mining. It reviews the different stages of data mining and introduces the wide spread biological databanks, their explosion, integration, data warehousing, information retrieval, text mining, text repositories for biological research publications, domain specific search engines, web mining, biological networks and visualization, ontology and systems biology. This chapter also illustrates some technical jargon with picture analogy for a novice learner to understand the concepts clearly.

An Overview of Biological Data Mining

2017 ◽  
pp. 130-154
Author(s):  
Seetharaman Balaji
Keyword(s):  
Data Mining ◽  
Information Retrieval ◽  
Biological Networks ◽  
Web Mining ◽  
Biological Data ◽  
Biological Research ◽  
Digital Repository ◽  
Domain Specific ◽  
Novice Learner ◽  
Integration Data

The largest digital repository of information, the World Wide Web keeps growing exponentially and calls for data mining services to provide tailored web experiences. This chapter discusses the overview of information retrieval, knowledge discovery and data mining. It reviews the different stages of data mining and introduces the wide spread biological databanks, their explosion, integration, data warehousing, information retrieval, text mining, text repositories for biological research publications, domain specific search engines, web mining, biological networks and visualization, ontology and systems biology. This chapter also illustrates some technical jargon with picture analogy for a novice learner to understand the concepts clearly.

Link prediction based on local weighted paths for complex networks

2017 ◽  
Vol 28 (04) ◽  
pp. 1750053
Author(s):  
Yabing Yao ◽  
Ruisheng Zhang ◽  
Fan Yang ◽  
Yongna Yuan ◽  
Rongjing Hu ◽  
...  
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Link Prediction ◽  
Structural Similarity ◽  
Prediction Performance ◽  
Topological Feature ◽  
Topological Features ◽  
Node Similarity ◽  
Weighted Paths ◽  
Path Dependent

As a significant problem in complex networks, link prediction aims to find the missing and future links between two unconnected nodes by estimating the existence likelihood of potential links. It plays an important role in understanding the evolution mechanism of networks and has broad applications in practice. In order to improve prediction performance, a variety of structural similarity-based methods that rely on different topological features have been put forward. As one topological feature, the path information between node pairs is utilized to calculate the node similarity. However, many path-dependent methods neglect the different contributions of paths for a pair of nodes. In this paper, a local weighted path (LWP) index is proposed to differentiate the contributions between paths. The LWP index considers the effect of the link degrees of intermediate links and the connectivity influence of intermediate nodes on paths to quantify the path weight in the prediction procedure. The experimental results on 12 real-world networks show that the LWP index outperforms other seven prediction baselines.

Similarity indices based on link weight assignment for link prediction of unweighted complex networks

2017 ◽  
Vol 31 (02) ◽  
pp. 1650254 ◽  
Author(s):  
Shuxin Liu ◽  
Xinsheng Ji ◽  
Caixia Liu ◽  
Yi Bai
Keyword(s):  
Complex Networks ◽  
Link Prediction ◽  
Prediction Accuracy ◽  
Prediction Methods ◽  
Close Attention ◽  
Common Neighbor ◽  
Link Weight ◽  
Similarity Indices ◽  
Clustered Networks ◽  
Local Path

Many link prediction methods have been proposed for predicting the likelihood that a link exists between two nodes in complex networks. Among these methods, similarity indices are receiving close attention. Most similarity-based methods assume that the contribution of links with different topological structures is the same in the similarity calculations. This paper proposes a local weighted method, which weights the strength of connection between each pair of nodes. Based on the local weighted method, six local weighted similarity indices extended from unweighted similarity indices (including Common Neighbor (CN), Adamic-Adar (AA), Resource Allocation (RA), Salton, Jaccard and Local Path (LP) index) are proposed. Empirical study has shown that the local weighted method can significantly improve the prediction accuracy of these unweighted similarity indices and that in sparse and weakly clustered networks, the indices perform even better.

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