Missing Link Prediction using Common Neighbor and Centrality based Parameterized Algorithm

Social Networks progress over time by the addition of new nodes and links, form associations with one community to the other community. Over a few decades, the fast expansion of Social Networks has attracted many researchers to pay more attention towards complex networks, the collection of social data, understand the social behaviors of complex networks and predict future conflicts. Thus, Link prediction is imperative to do research with social networks and network theory. The objective of this research is to find the hidden patterns and uncovered missing links over complex networks. Here, we developed a new similarity measure to predict missing links over social networks. The new method is computed on common neighbors with node-to-node distance to get better accuracy of missing link prediction. We tested the proposed measure on a variety of real-world linked datasets which are formed from various linked social networks. The proposed approach performance is compared with contemporary link prediction methods. Our measure makes very effective and intuitive in predicting disappeared links in linked social networks.

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

Advances in Business Information Systems and Analytics - Social Network Analytics for Contemporary Business Organizations ◽

10.4018/978-1-5225-5097-6.ch011 ◽

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.

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

International Journal of Modern Physics B ◽

10.1142/s0217979216502544 ◽

2017 ◽

Vol 31 (02) ◽

pp. 1650254 ◽

Cited By ~ 8

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.

Link Prediction Investigation of Dynamic Information Flow in Epilepsy

Journal of Healthcare Engineering ◽

10.1155/2018/8102597 ◽

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.

Link Prediction and Node Classification Based on Multitask Graph Autoencoder

Wireless Communications and Mobile Computing ◽

10.1155/2021/5537651 ◽

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.

Susceptible-infected-spreading-based network embedding in static and temporal networks

EPJ Data Science ◽

10.1140/epjds/s13688-020-00248-5 ◽

2020 ◽

Vol 9 (1) ◽

Author(s):

Xiu-Xiu Zhan ◽

Ziyu Li ◽

Naoki Masuda ◽

Petter Holme ◽

Huijuan Wang

Keyword(s):

Random Walk ◽

Link Prediction ◽

Large Scale ◽

Language Model ◽

Network Evolution ◽

Temporal Networks ◽

Network Embedding ◽

Comparison Task ◽

Missing Link ◽

Sample Paths

Abstract Link prediction can be used to extract missing information, identify spurious interactions as well as forecast network evolution. Network embedding is a methodology to assign coordinates to nodes in a low-dimensional vector space. By embedding nodes into vectors, the link prediction problem can be converted into a similarity comparison task. Nodes with similar embedding vectors are more likely to be connected. Classic network embedding algorithms are random-walk-based. They sample trajectory paths via random walks and generate node pairs from the trajectory paths. The node pair set is further used as the input for a Skip-Gram model, a representative language model that embeds nodes (which are regarded as words) into vectors. In the present study, we propose to replace random walk processes by a spreading process, namely the susceptible-infected (SI) model, to sample paths. Specifically, we propose two susceptible-infected-spreading-based algorithms, i.e., Susceptible-Infected Network Embedding (SINE) on static networks and Temporal Susceptible-Infected Network Embedding (TSINE) on temporal networks. The performance of our algorithms is evaluated by the missing link prediction task in comparison with state-of-the-art static and temporal network embedding algorithms. Results show that SINE and TSINE outperform the baselines across all six empirical datasets. We further find that the performance of SINE is mostly better than TSINE, suggesting that temporal information does not necessarily improve the embedding for missing link prediction. Moreover, we study the effect of the sampling size, quantified as the total length of the trajectory paths, on the performance of the embedding algorithms. The better performance of SINE and TSINE requires a smaller sampling size in comparison with the baseline algorithms. Hence, SI-spreading-based embedding tends to be more applicable to large-scale networks.

Predicting Missing Links Based on a New Triangle Structure

Complexity ◽

10.1155/2018/7312603 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 5

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.

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

10.1101/2020.11.14.382655 ◽

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.

Missing Link Prediction in Social Networks

Advances in Neural Networks – ISNN 2018 - Lecture Notes in Computer Science ◽

10.1007/978-3-319-92537-0_40 ◽

2018 ◽

pp. 346-354 ◽

Cited By ~ 4

Author(s):

Jin Zhou ◽

Chiman Kwan

Keyword(s):

Social Networks ◽

Link Prediction ◽

Missing Link