Exploiting Common Neighbor Graph for Link Prediction

2020 ◽  
Author(s):  
Hao Tian ◽  
Reza Zafarani
Keyword(s):  
Link Prediction ◽  
Common Neighbor ◽  
Neighbor Graph

An Experimental Evaluation of Link Prediction for Movie Suggestions Using Social Media Content

2018 ◽  
pp. 203-230
Author(s):  
Anu Taneja ◽  
Bhawna Gupta ◽  
Anuja Arora
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Social Networks ◽  
Experimental Evaluation ◽  
Link Prediction ◽  
Common Neighbor ◽  
Research Directions ◽  
Definition Of

The enormous growth and dynamic nature of online social networks have emerged to new research directions that examine the social network analysis mechanisms. In this chapter, the authors have explored a novel technique of recommendation for social media and used well known social network analysis (SNA) mechanisms-link prediction. The initial impetus of this chapter is to provide general description, formal definition of the problem, its applications, state-of-art of various link prediction approaches in social media networks. Further, an experimental evaluation has been made to inspect the role of link prediction in real environment by employing basic common neighbor link prediction approach on IMDb data. To improve performance, weighted common neighbor link prediction (WCNLP) approach has been proposed. This exploits the prediction features to predict new links among users of IMDb. The evaluation shows how the inclusion of weight among the nodes offers high link prediction performance and opens further research directions.

scholarly journals Missing Link Prediction using Common Neighbor and Centrality based Parameterized Algorithm

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Iftikhar Ahmad ◽  
Muhammad Usman Akhtar ◽  
Salma Noor ◽  
Ambreen Shahnaz
Keyword(s):  
Link Prediction ◽  
Parameterized Algorithm ◽  
Common Neighbor ◽  
Missing Link

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.

scholarly journals Link Prediction Investigation of Dynamic Information Flow in Epilepsy

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Yan He ◽  
Fan Yang ◽  
Yunli Yu ◽  
Celso Grebogi
Keyword(s):  
Link Prediction ◽  
Focal Epilepsy ◽  
Phase Locking ◽  
Brain Regions ◽  
Performance Comparison ◽  
Brain Disorder ◽  
Common Neighbor ◽  
Comparison Results ◽  
Ictal Activity ◽  
Phase Locking Value

As a brain disorder, epilepsy is characterized with abnormal hypersynchronous neural firings. It is known that seizures initiate and propagate in different brain regions. Long-term intracranial multichannel electroencephalography (EEG) reflects broadband ictal activity under seizure occurrence. Network-based techniques are efficient in discovering brain dynamics and offering finger-print features for specific individuals. In this study, we adopt link prediction for proposing a novel workflow aiming to quantify seizure dynamics and uncover pathological mechanisms of epilepsy. A dataset of EEG signals was enrolled that recorded from 8 patients with 3 different types of pharmocoresistant focal epilepsy. Weighted networks are obtained from phase locking value (PLV) in subband EEG oscillations. Common neighbor (CN), resource allocation (RA), Adamic-Adar (AA), and Sorenson algorithms are brought in for link prediction performance comparison. Results demonstrate that RA outperforms its rivals. Similarity, matrix was produced from the RA technique performing on EEG networks later. Nodes are gathered to form sequences by selecting the ones with the highest similarity. It is demonstrated that variations are in accordance with seizure attack in node sequences of gamma band EEG oscillations. What is more, variations in node sequences monitor the total seizure journey including its initiation and termination.

scholarly journals Link Prediction and Node Classification Based on Multitask Graph Autoencoder

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Shicong Chen ◽  
Deyu Yuan ◽  
Shuhua Huang ◽  
Yang Chen
Keyword(s):  
Loss Function ◽  
Link Prediction ◽  
Deep Level ◽  
Preferential Attachment ◽  
Similarity Index ◽  
Representation Learning ◽  
High Order ◽  
Fine Tuning ◽  
Common Neighbor ◽  
Node Classification

The goal of network representation learning is to extract deep-level abstraction from data features that can also be viewed as a process of transforming the high-dimensional data to low-dimensional features. Learning the mapping functions between two vector spaces is an essential problem. In this paper, we propose a new similarity index based on traditional machine learning, which integrates the concepts of common neighbor, local path, and preferential attachment. Furthermore, for applying the link prediction methods to the field of node classification, we have innovatively established an architecture named multitask graph autoencoder. Specifically, in the context of structural deep network embedding, the architecture designs a framework of high-order loss function by calculating the node similarity from multiple angles so that the model can make up for the deficiency of the second-order loss function. Through the parameter fine-tuning, the high-order loss function is introduced into the optimized autoencoder. Proved by the effective experiments, the framework is generally applicable to the majority of classical similarity indexes.

scholarly journals Predicting Missing Links Based on a New Triangle Structure

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Shenshen Bai ◽  
Longjie Li ◽  
Jianjun Cheng ◽  
Shijin Xu ◽  
Xiaoyun Chen
Keyword(s):  
Resource Allocation ◽  
Real World ◽  
Rapid Growth ◽  
Link Prediction ◽  
Large Degree ◽  
Missing Information ◽  
Common Neighbor ◽  
Seed Node ◽  
Structure Information ◽  
Ranking Score

With the rapid growth of various complex networks, link prediction has become increasingly important because it can discover the missing information and predict future interactions between nodes in a network. Recently, the CAR and CCLP indexes have been presented for link prediction by means of different triangle structure information. However, both indexes may lose the contributions of some shared neighbors. We propose in this work a new index to make up the weakness and then improve the accuracy of link prediction. The proposed index focuses on a new triangle structure, i.e., the triangle formed by one seed node, one common neighbor, and one other node. It emphasizes the importance of these triangles but does not ignore the contribution of any common neighbor. In addition, the proposed index adopts the theory of resource allocation by penalizing large-degree neighbors. The results of comparison with CN, AA, RA, ADP, CAR, CAA, CRA, and CCLP on 12 real-world networks show that the proposed index outperforms the compared methods in terms of AUC and ranking score.

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.

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 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.

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