Link Prediction by Network Analysis

2017 ◽  
pp. 97-114 ◽  
Author(s):  
Salim Afra ◽  
Alper Aksaç ◽  
Tansel Õzyer ◽  
Reda Alhajj
Keyword(s):  
Network Analysis ◽  
Link Prediction

Synonymy Expansion Using Link Prediction Methods: A Case Study of Assamese WordNet

2022 ◽  
Vol 21 (1) ◽  
pp. 1-21
Author(s):  
Bornali Phukon ◽  
Akash Anil ◽  
Sanasam Ranbir Singh ◽  
Priyankoo Sarmah
Keyword(s):  
Network Analysis ◽  
Target Word ◽  
Complex Network ◽  
Link Prediction ◽  
Expansion Method ◽  
Prediction Methods ◽  
Network Embedding ◽  
Per Se ◽  
Local Proximity

WordNets built for low-resource languages, such as Assamese, often use the expansion methodology. This may result in missing lexical entries and missing synonymy relations. As the Assamese WordNet is also built using the expansion method, using the Hindi WordNet, it also has missing synonymy relations. As WordNets can be visualized as a network of unique words connected by synonymy relations, link prediction in complex network analysis is an effective way of predicting missing relations in a network. Hence, to predict the missing synonyms in the Assamese WordNet, link prediction methods were used in the current work that proved effective. It is also observed that for discovering missing relations in the Assamese WordNet, simple local proximity-based methods might be more effective as compared to global and complex supervised models using network embedding. Further, it is noticed that though a set of retrieved words are not synonyms per se, they are semantically related to the target word and may be categorized as semantic cohorts.

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 Hidden Link Prediction in Criminal Networks Using the Deep Reinforcement Learning Technique

Computers ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 8 ◽  
Author(s):  
Marcus Lim ◽  
Azween Abdullah ◽  
NZ Jhanjhi ◽  
Mahadevan Supramaniam
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Network Analysis ◽  
Prediction Model ◽  
Supervised Learning ◽  
Link Prediction ◽  
Supervised Machine Learning ◽  
Criminal Networks ◽  
Criminal Network ◽  
Learning Technique

Criminal network activities, which are usually secret and stealthy, present certain difficulties in conducting criminal network analysis (CNA) because of the lack of complete datasets. The collection of criminal activities data in these networks tends to be incomplete and inconsistent, which is reflected structurally in the criminal network in the form of missing nodes (actors) and links (relationships). Criminal networks are commonly analyzed using social network analysis (SNA) models. Most machine learning techniques that rely on the metrics of SNA models in the development of hidden or missing link prediction models utilize supervised learning. However, supervised learning usually requires the availability of a large dataset to train the link prediction model in order to achieve an optimum performance level. Therefore, this research is conducted to explore the application of deep reinforcement learning (DRL) in developing a criminal network hidden links prediction model from the reconstruction of a corrupted criminal network dataset. The experiment conducted on the model indicates that the dataset generated by the DRL model through self-play or self-simulation can be used to train the link prediction model. The DRL link prediction model exhibits a better performance than a conventional supervised machine learning technique, such as the gradient boosting machine (GBM) trained with a relatively smaller domain dataset.

scholarly journals Joint Link Prediction and Network Alignment via Cross-graph Embedding

2019 ◽  
Author(s):  
Xingbo Du ◽  
Junchi Yan ◽  
Hongyuan Zha
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Link Prediction ◽  
Graph Embedding ◽  
Network Alignment ◽  
Initial Vertex ◽  
Information Propagation ◽  
Graph Node ◽  
Embedding Technique

Link prediction and network alignment are two important problems in social network analysis and other network related applications. Considerable efforts have been devoted to these two problems while often in an independent way to each other. In this paper we argue that these two tasks are relevant and present a joint link prediction and network alignment framework, whereby a novel cross-graph node embedding technique is devised to allow for information propagation. Our approach can either work with a few initial vertex correspondence as seeds, or from scratch. By extensive experiments on public benchmark, we show that link prediction and network alignment can benefit to each other especially for improving the recall for both tasks.

scholarly journals Taxonomy of Link Prediction for Social Network Analysis: A Review

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 183470-183487
Author(s):  
Herman Yuliansyah ◽  
Zulaiha Ali Othman ◽  
Azuraliza Abu Bakar
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Link Prediction

scholarly journals Directed closure coefficient and its patterns

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253822
Author(s):  
Mingshan Jia ◽  
Bogdan Gabrys ◽  
Katarzyna Musial
Keyword(s):  
Network Analysis ◽  
Complex Networks ◽  
Link Prediction ◽  
Clustering Coefficient ◽  
Directed Networks ◽  
Distinctive Features ◽  
Prediction Task ◽  
Significant Element ◽  
Node Level ◽  
End Node

The triangle structure, being a fundamental and significant element, underlies many theories and techniques in studying complex networks. The formation of triangles is typically measured by the clustering coefficient, in which the focal node is the centre-node in an open triad. In contrast, the recently proposed closure coefficient measures triangle formation from an end-node perspective and has been proven to be a useful feature in network analysis. Here, we extend it by proposing the directed closure coefficient that measures the formation of directed triangles. By distinguishing the direction of the closing edge in building triangles, we further introduce the source closure coefficient and the target closure coefficient. Then, by categorising particular types of directed triangles (e.g., head-of-path), we propose four closure patterns. Through multiple experiments on 24 directed networks from six domains, we demonstrate that at network-level, the four closure patterns are distinctive features in classifying network types, while at node-level, adding the source and target closure coefficients leads to significant improvement in link prediction task in most types of directed networks.

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.

A Survey of Network Representation Learning Methods for Link Prediction in Biological Network

2020 ◽  
Vol 26 (26) ◽  
pp. 3076-3084 ◽  
Author(s):  
Jiajie Peng ◽  
Guilin Lu ◽  
Xuequn Shang
Keyword(s):  
Network Analysis ◽  
Biological Networks ◽  
Link Prediction ◽  
Biological Network ◽  
Representation Learning ◽  
Learning Methods ◽  
Future Directions ◽  
Value Network ◽  
Network Representation ◽  
Important Practical Application

Background: Networks are powerful resources for describing complex systems. Link prediction is an important issue in network analysis and has important practical application value. Network representation learning has proven to be useful for network analysis, especially for link prediction tasks. Objective: To review the application of network representation learning on link prediction in a biological network, we summarize recent methods for link prediction in a biological network and discuss the application and significance of network representation learning in link prediction task. Method & Results: We first introduce the widely used link prediction algorithms, then briefly introduce the development of network representation learning methods, focusing on a few widely used methods, and their application in biological network link prediction. Existing studies demonstrate that using network representation learning to predict links in biological networks can achieve better performance. In the end, some possible future directions have been discussed.

scholarly journals Community detection in complex networks using link prediction

2018 ◽  
Vol 32 (01) ◽  
pp. 1850004 ◽  
Author(s):  
Hui-Min Cheng ◽  
Yi-Zi Ning ◽  
Zhao Yin ◽  
Chao Yan ◽  
Xin Liu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Complex Networks ◽  
Community Detection ◽  
Link Prediction ◽  
Detection Algorithm ◽  
The Other ◽  
Data Sets ◽  
Topological Structures ◽  
Benchmark Data ◽  
Community Detection Algorithm

Community detection and link prediction are both of great significance in network analysis, which provide very valuable insights into topological structures of the network from different perspectives. In this paper, we propose a novel community detection algorithm with inclusion of link prediction, motivated by the question whether link prediction can be devoted to improving the accuracy of community partition. For link prediction, we propose two novel indices to compute the similarity between each pair of nodes, one of which aims to add missing links, and the other tries to remove spurious edges. Extensive experiments are conducted on benchmark data sets, and the results of our proposed algorithm are compared with two classes of baselines. In conclusion, our proposed algorithm is competitive, revealing that link prediction does improve the precision of community detection.

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