scholarly journals HM-EIICT: Fairness-aware link prediction in complex networks using community information

2021 ◽  
Author(s):  
Akrati Saxena ◽  
George Fletcher ◽  
Mykola Pechenizkiy
Keyword(s):  
Network Topology ◽  
Online Social Networks ◽  
Link Prediction ◽  
Prediction Method ◽  
High Accuracy ◽  
Community Based ◽  
Research Directions ◽  
Open Research ◽  
Community Information ◽  
Network Modularity

AbstractThe evolution of online social networks is highly dependent on the recommended links. Most of the existing works focus on predicting intra-community links efficiently. However, it is equally important to predict inter-community links with high accuracy for diversifying a network. In this work, we propose a link prediction method, called HM-EIICT, that considers both the similarity of nodes and their community information to predict both kinds of links, intra-community links as well as inter-community links, with higher accuracy. The proposed framework is built on the concept that the connection likelihood between two given nodes differs for inter-community and intra-community node-pairs. The performance of the proposed methods is evaluated using link prediction accuracy and network modularity reduction. The results are studied on real-world networks and show the effectiveness of the proposed method as compared to the baselines. The experiments suggest that the inter-community links can be predicted with a higher accuracy using community information extracted from the network topology, and the proposed framework outperforms several measures especially proposed for community-based link prediction. The paper is concluded with open research directions.

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.

A New Real-Time Link Prediction Method Based on User Community Changes in Online Social Networks

2019 ◽  
Vol 63 (3) ◽  
pp. 448-459
Author(s):  
Amin Mahmoudi ◽  
Mohd Ridzwan Yaakub ◽  
Azuraliza Abu Bakar
Keyword(s):  
Real Time ◽  
Online Social Networks ◽  
Link Prediction ◽  
Structural Information ◽  
Online Social Network ◽  
Dynamic Environment ◽  
Area Under The Curve ◽  
Prediction Method ◽  
Weight Density ◽  
User Communities

Abstract The link prediction problem is becoming an important area of online social network (OSN) research. The existing methods that have been developed to address this problem mostly try to predict links based on structural information about the whole of the user lifespan. In addition, most of them do not consider user attributes such as user weight, density of interaction and geo-distance, all of which have an influence on the prediction of future links in OSNs due to the human-centric nature of these networks. Moreover, an OSN is a dynamic environment because users join and leave communities based on their interests over time. Therefore, it is necessary to predict links in real time. Therefore, the current study proposes a new method based on time and user attributes to predict links based on changes in user communities, where the changes in the user communities are indicative of users’ interests. The proposed method is tested on the UKM dataset and its performance is compared with that of 10 well-known methods and another community-based method. The area-under-the-curve results show that the proposed method is more accurate than all of the compared methods.

Link prediction based on the tie connection strength of common neighbor

2019 ◽  
Vol 30 (11) ◽  
pp. 1950089 ◽  
Author(s):  
Yujie Yang ◽  
Jianhua Zhang ◽  
Xuzhen Zhu ◽  
Jinming Ma ◽  
Xin Su
Keyword(s):  
Network Topology ◽  
Link Prediction ◽  
Free Parameter ◽  
Prediction Method ◽  
Large Degree ◽  
Local Information ◽  
Connection Strength ◽  
Common Neighbor ◽  
Multiple Paths ◽  
The Common

Traditional link prediction indices focus on the degree of the common neighbor and consider that the common neighbor with large degree contributes less to the similarity of two unconnected endpoints. Therefore, some of the local information-based methods only restrain the common neighbor with large degree for avoiding the influence dissipation. We find, however, if the large degree common neighbor connects with two unconnected endpoints through multiple paths simultaneously, these paths actually serve as transmission influences instead of dissipation. We regard these paths as the tie connection strength (TCS) of the common neighbor, and larger TCS can promote two unconnected endpoints to link with each other. Meanwhile, we notice that the similarity of node-pairs also relates to the network topology structure. Thus, in order to study the influences of TCS and the network structure on similarity, we introduce a free parameter and propose a novel link prediction method based on the TCS of the common neighbor. The experiment results on 12 real networks suggest that the proposed TCS index can improve the accuracy of link prediction.

scholarly journals Analyzing the Bills-Voting Dynamics and Predicting Corruption-Convictions Among Brazilian Congressmen Through Temporal Networks

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Tiago Colliri ◽  
Liang Zhao
Keyword(s):  
Link Prediction ◽  
Large Scale ◽  
Prediction Method ◽  
High Accuracy ◽  
Financial Information ◽  
Prediction Methods ◽  
Temporal Networks ◽  
Public Data ◽  
Financial Crimes ◽  
Prediction Techniques

AbstractIn this paper, we propose a network-based technique to analyze bills-voting data comprising the votes of Brazilian congressmen for a period of 28 years. The voting sessions are initially mapped into static networks, where each node represents a congressman and each edge stands for the similarity of votes between a pair of congressmen. Afterwards, the constructed static networks are converted to temporal networks. Our analyses on the temporal networks capture some of the main political changes happened in Brazil during the period of time under consideration. Moreover, we find out that the bills-voting networks can be used to identify convicted politicians, who commit corruption or other financial crimes. Therefore, we propose two conviction prediction methods, one is based on the highest weighted convicted neighbor and the other is based on link prediction techniques. It is a surprise to us that the high accuracy (up to 90% by the link prediction method) on predicting convictions is achieved only through bills-voting data, without taking into account any financial information beforehand. Such a feature makes possible to monitor congressmen just by considering their legal public activities. In this way, our work contributes to the large scale public data study using complex networks.

An effective community-based link prediction model for improving accuracy in social networks

2021 ◽  
pp. 1-17
Author(s):  
M. Mohamed Iqbal ◽  
K. Latha
Keyword(s):  
Social Networks ◽  
Prediction Model ◽  
Real World ◽  
Link Prediction ◽  
Centrality Measures ◽  
Community Based ◽  
Proposed Model ◽  
Similarity Indices ◽  
Community Information ◽  
The Given

Link prediction plays a predominant role in complex network analysis. It indicates to determine the probability of the presence of future links that depends on available information. The existing standard classical similarity indices-based link prediction models considered the neighbour nodes have a similar effect towards link probability. Nevertheless, the common neighbor nodes residing in different communities may vary in real-world networks. In this paper, a novel community information-based link prediction model has been proposed in which every neighboring node’s community information (community centrality) has been considered to predict the link between the given node pair. In the proposed model, the given social network graph can be divided into different communities and community centrality is calculated for every derived community based on degree, closeness, and betweenness basic graph centrality measures. Afterward, the new community centrality-based similarity indices have been introduced to compute the community centralities which are applied to nine existing basic similarity indices. The empirical analysis on 13 real-world social networks datasets manifests that the proposed model yields better prediction accuracy of 97% rather than existing models. Moreover, the proposed model is parallelized efficiently to work on large complex networks using Spark GraphX Big Data-based parallel Graph processing technique and it attains a lesser execution time of 250 seconds.

Analysis of Link Prediction Method for Link Recovery in Mobile Ad-hoc Network

2017 ◽  
Vol 7 (5) ◽  
pp. 849-854
Author(s):  
◽  
Jatinder Kaur ◽  
Danvir Mandal ◽  
Rajneesh Talwar ◽  
◽  
...  
Keyword(s):  
Ad Hoc Network ◽  
Link Prediction ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Prediction Method ◽  
Mobile Ad Hoc

A Mobile User Preference Prediction Method Based on Trust and Link Prediction

2014 ◽  
Vol 35 (12) ◽  
pp. 2972-2977
Author(s):  
Hua Geng ◽  
Xiang-wu Meng ◽  
Yan-cui Shi
Keyword(s):  
Link Prediction ◽  
Prediction Method ◽  
Mobile User ◽  
User Preference

scholarly journals An information theoretic approach to link prediction in multiplex networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seyed Hossein Jafari ◽  
Amir Mahdi Abdolhosseini-Qomi ◽  
Masoud Asadpour ◽  
Maseud Rahgozar ◽  
Naser Yazdani
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Large Scale ◽  
Similarity Measures ◽  
Prediction Method ◽  
General Purpose ◽  
Fast Method ◽  
Theoretic Approach ◽  
Multiplex Networks ◽  
Wide Range

AbstractThe entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method—SimBins—is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applying SimBins to various datasets from diverse domains, our findings indicate that SimBins outperforms the compared methods (both baseline and state-of-the-art methods) in most instances when predicting links. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.

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