Structural link prediction using community information on Twitter

2012 ◽  
Author(s):  
Jorge Valverde-Rebaza ◽  
Alneu de Andrade Lopes
Keyword(s):  
Link Prediction ◽  
Community Information

scholarly journals Link Prediction Based on Community Information and Its Parallelization

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 62633-62645 ◽  
Author(s):  
Jingwei Wang ◽  
Yunlong Ma ◽  
Min Liu ◽  
Weiming Shen
Keyword(s):  
Link Prediction ◽  
Community Information

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.

Link Prediction in Complex Networks

2020 ◽  
pp. 1196-1236
Author(s):  
Manisha Pujari ◽  
Rushed Kanawati
Keyword(s):  
Complex Networks ◽  
Link Prediction ◽  
Class Imbalance ◽  
Rank Aggregation ◽  
General Description ◽  
Multiplex Network ◽  
Community Information ◽  
Target Network ◽  
Definition Of ◽  
Prediction Approach

This chapter presents the problem of link prediction in complex networks. It provides general description, formal definition of the problem and applications. It gives a state-of-art of various existing link prediction approaches concentrating more on topological approaches. It presents the main challenges of link prediction task in real networks. There is description of our new link prediction approach based on supervised rank aggregation and our attempts to deal with two of the challenges to improve the prediction results. One approach is to extend the set of attributes describing an example (pair of nodes) calculated in a multiplex network that includes the target network. Multiplex networks have a layered structure, each layer having different kinds of links between same sets of nodes. The second way is to use community information for sampling of examples to deal with the problem of class imbalance. All experiments have been conducted on real networks extracted from well-known DBLP bibliographic database.

Link Prediction in Complex Networks

2016 ◽  
pp. 58-97 ◽  
Author(s):  
Manisha Pujari ◽  
Rushed Kanawati
Keyword(s):  
Complex Networks ◽  
Link Prediction ◽  
Class Imbalance ◽  
Rank Aggregation ◽  
General Description ◽  
Multiplex Network ◽  
Community Information ◽  
Target Network ◽  
Definition Of ◽  
Prediction Approach

This chapter presents the problem of link prediction in complex networks. It provides general description, formal definition of the problem and applications. It gives a state-of-art of various existing link prediction approaches concentrating more on topological approaches. It presents the main challenges of link prediction task in real networks. There is description of our new link prediction approach based on supervised rank aggregation and our attempts to deal with two of the challenges to improve the prediction results. One approach is to extend the set of attributes describing an example (pair of nodes) calculated in a multiplex network that includes the target network. Multiplex networks have a layered structure, each layer having different kinds of links between same sets of nodes. The second way is to use community information for sampling of examples to deal with the problem of class imbalance. All experiments have been conducted on real networks extracted from well-known DBLP bibliographic database.

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.

Design Parameters Of A Comfortable Living Environment In The Normative Documentation

2020 ◽  
pp. 51-56
Keyword(s):  
Russian Federation ◽  
Living Environment ◽  
National Standards ◽  
Design Parameters ◽  
Internal Space ◽  
Environment Quality ◽  
Indoor Environment Quality ◽  
Community Information ◽  
Normative Documentation ◽  
The Russian Federation

Currently, the professional construction community information field is largely filled with the topic of creating a comfortable living environment. However, architectural and engineering design that corresponds to the concept of sustainable development is currently hindered due to the lack of a formed conceptual framework that reveals the meaning of the term "comfort", as well as a criteria list that determines the indoor environment quality in the Russian Federation regulatory and technical framework. The article offers some components of a comfortable living environment, within which the parameters of designing the internal environment of premises are highlighted. A comparative analysis of the national standards of the Russian Federation regulating the design of the internal space of residential and public buildings, with international "green" standards for a number of parameters was carried out. It is concluded that it is necessary to update the Russian regulatory and technical base taking into account the international experience of "green" standards.

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
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