Link prediction based on heterogeneous degree penalization with extending neighbors and clustering coefficient

2021 ◽  
Author(s):  
Rongrong Song ◽  
Guang Ling ◽  
Qingju Fan ◽  
Ming-Feng Ge ◽  
Fang Wang
Keyword(s):  
Local Structure ◽  
Link Prediction ◽  
Similarity Index ◽  
Similarity Measures ◽  
Clustering Coefficient ◽  
The Other ◽  
Challenging Problem ◽  
Structure Information ◽  
Node Similarity ◽  
A Current

Link prediction, aiming to find missing links in a current network or to predict some possible new links in a future network, is a challenging problem in complex networks. Many existing link prediction algorithms perform the task by optimizing the node similarity measures, and then determining the possibility of the link between any pair of similar nodes. In this paper, we propose a novel node similarity index named heterogeneous degree penalization (HDP), which incorporates the quasi-local structure information of extending neighborhood of each pair of nodes to be predicted and the clustering coefficient of their common neighbors. For specific networks with different statistical properties, we can achieve a good performance of link prediction through adjusting the penalty weights. The experiment results show that, comparing with the other existing approaches, the proposed method can remarkably improve the accuracy of link prediction.

SIMILARITY INDEX BASED ON THE INFORMATION OF NEIGHBOR NODES FOR LINK PREDICTION OF COMPLEX NETWORK

2013 ◽  
Vol 27 (06) ◽  
pp. 1350039 ◽  
Author(s):  
JING WANG ◽  
LILI RONG
Keyword(s):  
Link Prediction ◽  
High Efficiency ◽  
Similarity Index ◽  
Similarity Measures ◽  
Nearest Neighbors ◽  
Clustering Coefficient ◽  
Local Similarity ◽  
Common Neighbor ◽  
Similarity Indices ◽  
Node Similarity

Link prediction in complex networks has attracted much attention recently. Many local similarity measures based on the measurements of node similarity have been proposed. Among these local similarity indices, the neighborhood-based indices Common Neighbors (CN), Adamic-Adar (AA) and Resource Allocation (RA) index perform best. It is found that the node similarity indices required only information on the nearest neighbors are assigned high scores and have very low computational complexity. In this paper, a new index based on the contribution of common neighbor nodes to edges is proposed and shown to have competitively good or even better prediction than other neighborhood-based indices especially for the network with low clustering coefficient with its high efficiency and simplicity.

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.

Discovering spurious links in multiplex networks based on interlayer relevance

2019 ◽  
Vol 7 (5) ◽  
pp. 641-658 ◽  
Author(s):  
Zeynab Samei ◽  
Mahdi Jalili
Keyword(s):  
Social Networks ◽  
Social Networking ◽  
Link Prediction ◽  
Large Scale ◽  
Transportation Networks ◽  
Similarity Index ◽  
Similarity Measures ◽  
Computation Complexity ◽  
Multiplex Networks ◽  
Large Scale Networks

Abstract Many real-world complex systems can be better modelled as multiplex networks, where the same individuals develop connections in multiple layers. Examples include social networks between individuals on multiple social networking platforms, and transportation networks between cities based on air, rail and road networks. Accurately predicting spurious links in multiplex networks is a challenging issue. In this article, we show that one can effectively use interlayer information to build an algorithm for spurious link prediction. We propose a similarity index that combines intralayer similarity with interlayer relevance for the link prediction purpose. The proposed similarity index is used to rank the node pairs, and identify those that are likely to be spurious. Our experimental results show that the proposed metric is much more accurate than intralayer similarity measures in correctly predicting the spurious links. The proposed method is an unsupervised method and has low computation complexity, and thus can be effectively applied for spurious link prediction in large-scale networks.

scholarly journals Proximity Measures as Graph Convolution Matrices for Link Prediction in Biological Networks

2020 ◽  
Author(s):  
Mustafa Coşkun ◽  
Mehmet Koyutürk
Keyword(s):  
Link Prediction ◽  
Similarity Measures ◽  
Graph Representation ◽  
Supplementary Information ◽  
Great Promise ◽  
Network Embedding ◽  
Common Neighbor ◽  
Node Similarity ◽  
Topological Characteristics ◽  
Low Dimensional

AbstractMotivationLink prediction is an important and well-studied problem in computational biology, with a broad range of applications including disease gene prioritization, drug-disease associations, and drug response in cancer. The general principle in link prediction is to use the topological characteristics and the attributes–if available– of the nodes in the network to predict new links that are likely to emerge/disappear. Recently, graph representation learning methods, which aim to learn a low-dimensional representation of topological characteristics and the attributes of the nodes, have drawn increasing attention to solve the link prediction problem via learnt low-dimensional features. Most prominently, Graph Convolution Network (GCN)-based network embedding methods have demonstrated great promise in link prediction due to their ability of capturing non-linear information of the network. To date, GCN-based network embedding algorithms utilize a Laplacian matrix in their convolution layers as the convolution matrix and the effect of the convolution matrix on algorithm performance has not been comprehensively characterized in the context of link prediction in biomedical networks. On the other hand, for a variety of biomedical link prediction tasks, traditional node similarity measures such as Common Neighbor, Ademic-Adar, and other have shown promising results, and hence there is a need to systematically evaluate the node similarity measures as convolution matrices in terms of their usability and potential to further the state-of-the-art.ResultsWe select 8 representative node similarity measures as convolution matrices within the single-layered GCN graph embedding method and conduct a systematic comparison on 3 important biomedical link prediction tasks: drug-disease association (DDA) prediction, drug–drug interaction (DDI) prediction, protein–protein interaction (PPI) prediction. Our experimental results demonstrate that the node similarity-based convolution matrices significantly improves GCN-based embedding algorithms and deserve more attention in the future biomedical link predictionAvailabilityOur method is implemented as a python library and is available at [email protected] informationSupplementary data are available at Bioinformatics online.

Evidential link prediction method based on the importance of high-order path index

2021 ◽  
Author(s):  
Jingjing Xia ◽  
Guang Ling ◽  
Qingju Fan ◽  
Fang Wang ◽  
Ming-Feng Ge
Keyword(s):  
Link Prediction ◽  
Similarity Index ◽  
Information Leakage ◽  
Prediction Method ◽  
High Order ◽  
Clustering Coefficient ◽  
Dempster Shafer Theory ◽  
Path Index ◽  
Shafer Theory ◽  
Entire Network

Link prediction, aiming to find missing links in an observed network or predict those links that may occur in the future, has become a basic challenge of network science. Most existing link prediction methods are based on local or global topological attributes of the network such as degree, clustering coefficient, path index, etc. In the process of resource allocation, as the number of connections between the common neighbors of the paired nodes increases, it is easy to leak information through them. To overcome this problem, we proposed a new similarity index named ESHOPI (link prediction based on Dempster–Shafer theory and the importance of higher-order path index), which can prevent information leakage by penalizing ordinary neighbors and considering the information of the entire network and each node at the same time. In addition, high-order paths are used to improve the performance of link prediction by penalizing the longer reachable paths between the seed nodes. The effectiveness of ESHOPI is shown by the experiments on both synthetic and real-world networks.

Link prediction based on local community properties

2016 ◽  
Vol 30 (31) ◽  
pp. 1650222 ◽  
Author(s):  
Xu-Hua Yang ◽  
Hai-Feng Zhang ◽  
Fei Ling ◽  
Zhi Cheng ◽  
Guo-Qing Weng ◽  
...  
Keyword(s):  
Link Prediction ◽  
Local Community ◽  
Network Evolution ◽  
Clustering Coefficient ◽  
Prediction Algorithm ◽  
Node Degree ◽  
Common Neighbor ◽  
Node Similarity ◽  
The Common ◽  
Edge Clustering Coefficient

The link prediction algorithm is one of the key technologies to reveal the inherent rule of network evolution. This paper proposes a novel link prediction algorithm based on the properties of the local community, which is composed of the common neighbor nodes of any two nodes in the network and the links between these nodes. By referring to the node degree and the condition of assortativity or disassortativity in a network, we comprehensively consider the effect of the shortest path and edge clustering coefficient within the local community on node similarity. We numerically show the proposed method provide good link prediction results.

An effective similarity measure based on kernel spectral method for complex networks

2019 ◽  
Vol 30 (07) ◽  
pp. 1940005
Author(s):  
Longjie Li ◽  
Lu Wang ◽  
Shenshen Bai ◽  
Shiyu Fang ◽  
Jianjun Cheng ◽  
...  
Keyword(s):  
Network Analysis ◽  
Spectral Method ◽  
Similarity Measure ◽  
Link Prediction ◽  
Structural Information ◽  
Critical Role ◽  
Similarity Measures ◽  
Global Information ◽  
Kernel Matrix ◽  
Node Similarity

Node similarity measure is a special important task in complex network analysis and plays a critical role in a multitude of applications, such as link prediction, community detection, and recommender systems. In this study, we are interested in link-based similarity measures, which only concern the structural information of networks when estimating node similarity. A new algorithm is proposed by adopting the idea of kernel spectral method to quantify the similarity of nodes. When computing the kernel matrix, the proposed algorithm makes use of local structural information, but it takes advantage of global information when constructing the feature matrix. Thence, the proposed algorithm could better capture potential relationships between nodes. To show the superiority of our algorithm over others, we conduct experiments on 10 real-world networks. Experimental results demonstrate that our algorithm yields more reasonable results and better performance of accuracy than baselines.

Analysis similarity index of link prediction based on multivariate statistics

2018 ◽  
Vol 32 (28) ◽  
pp. 1850316
Author(s):  
Minghu Tang ◽  
Wenjun Wang
Keyword(s):  
Link Prediction ◽  
Great Influence ◽  
Similarity Index ◽  
The Other ◽  
Multivariate Statistical ◽  
Network Properties ◽  
Evaluation Mechanism ◽  
The One ◽  
The Relationship ◽  
Selection Of

Link prediction attracts the attention of a large number of researchers due to the extensive application in social and economic fields. Many algorithms have been proposed in recent years. They show good performance because of having own particularly selected networks. However, on the other networks, they do not necessarily have good universality. Moreover, there are no other methods to evaluate the performance of new algorithm except AUC and precision. Therefore, we cannot help questioning this phenomenon. Can it really reflect the performance of an algorithm? Which attributes of a network have great influence on the prediction effect? In this paper, we analyze 21 real networks by multivariate statistical analysis. On the one hand, we find that the heterogeneity of network plays a significant role in the result of link prediction. On the other hand, the selection of network is very essential when verifying the performance of new algorithm. In addition, a nonlinear regression model is produced by analyzing the relationship between network properties and similarity methods. Furthermore, 16 similarity methods are analyzed by means of the AUC. The results show that it is of great significance for the performance of a new algorithm to design the evaluation mechanism of classification.

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