Newton’s Gravitational Law for Link Prediction in Social Networks

Ashraf, Akanda Wahid Ul; Budka, Marcin; Musial-Gabrys, Katarzyna

doi:10.1007/978-3-319-72150-7_8

Cited by 9 publications

(6 citation statements)

References 38 publications

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“…Ibrahim and Chen [69] present a method for link prediction in dynamic networks by integrating temporal information, community structure and node centrality in the network providing greater weights for frequently occurring links. Wahid-Ul-Ashraf et al [70] described the parallelism between Newton's law of universal gravitation and the link prediction tasks. To apply this law, the authors attributed nodes with the notion of mass and distance.…”

Section: Recent Developmentsmentioning

confidence: 99%

Evolving Networks and Social Network Analysis Methods and Techniques

Cordeiro¹,

Sarmento²,

Brazdil³

et al. 2018

Social Media and Journalism - Trends, Connections, Implications

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Evolving networks by definition are networks that change as a function of time. They are a natural extension of network science since almost all real-world networks evolve over time, either by adding or by removing nodes or links over time: elementary actor-level network measures like network centrality change as a function of time, popularity and influence of individuals grow or fade depending on processes, and events occur in networks during time intervals. Other problems such as network-level statistics computation, link prediction, community detection, and visualization gain additional research importance when applied to dynamic online social networks (OSNs). Due to their temporal dimension, rapid growth of users, velocity of changes in networks, and amount of data that these OSNs generate, effective and efficient methods and techniques for small static networks are now required to scale and deal with the temporal dimension in case of streaming settings. This chapter reviews the state of the art in selected aspects of evolving social networks presenting open research challenges related to OSNs. The challenges suggest that significant further research is required in evolving social networks, i.e., existent methods, techniques, and algorithms must be rethought and designed toward incremental and dynamic versions that allow the efficient analysis of evolving networks.

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Section: Recent Developmentsmentioning

confidence: 99%

Evolving Networks and Social Network Analysis Methods and Techniques

Cordeiro¹,

Sarmento²,

Brazdil³

et al. 2018

Social Media and Journalism - Trends, Connections, Implications

View full text Add to dashboard Cite

show abstract

“…The main advantage of this approach is that it allows information from various fields to be combined, such as economic, health, and transportation. Thus, with this approach, we can quantify the relationship between the different countries and model the networks that help to understand the dynamics of the system to be analyzed (in this case, the spread of COVID- 19).…”

Section: Limitations Of the Study And Discussionmentioning

confidence: 99%

“…The identification and quantification of influential nodes in complex networks is an essential activity in several application fields, such as the spread and control of diseases [14] , the identification of the most influential members of a criminal group [15] , to know how impactful are the academic publications [16] , to predict future relationships [17] – [19] , among others [20] , [21] .…”

Section: Introductionmentioning

confidence: 99%

Identification of COVID-19 Spreaders Using Multiplex Networks Approach

et al. 2020

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In this work, we present a methodology to identify COVID-19 spreaders using the analysis of the relationship between socio-cultural and economic characteristics with the number of infections and deaths caused by the COVID-19 virus in different countries. For this, we analyze the information of each country using the complex networks approach, specifically by analyzing the spreaders countries based on the separator set in 5-layer multiplex networks. The results show that, we obtain a classification of the countries based on their numerical values in socioeconomics, population, Gross Domestic Product (GDP), health and air connections; where, in the spreader set there are those countries that have high, medium or low values in the different characteristics; however, the aspect that all the countries belonging to the separator set share is a high value in air connections. INDEX TERMS Complex networks, complex systems, COVID-19, multiplex networks, optimization, social networks.

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“…e identification and quantification of influential nodes in complex networks are an important activity in several application fields, such as the spread and control of diseases [7], the identification of the most influential members of a criminal group [8], or predicting future commercial relationships [9][10][11][12].…”

Section: Complex Network Essential Nodes and Robustnessmentioning

confidence: 99%

Analysis and Characterization of the Spread of COVID-19 in Mexico through Complex Networks and Optimization Approaches

et al. 2022

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This work analyzes and characterizes the spread of the COVID-19 disease in Mexico, using complex networks and optimization approaches. Specifically, we present two methodologies based on the principle of the rupture for the GC and Newton's law of motion to quantify the robustness and identify the Mexican municipalities whose population causes a fast spread of the SARS-CoV-2 virus. Specifically, the first methodology is based on several characteristics of the original version of the Vertex Separator Problem (VSP), and the second is based on a new mathematical model (NLM). By solving VSP, we can find nodes that cause the rupture of the giant component (GC). On the other hand, solving the NLM can find more influential nodes for the entire system’s development. Specifically, we present an analysis using a coupled social network model with information about the main characteristics of the contagion and deaths caused by COVID-19 in Mexico for 19 months (January 2020–July 2021). This work aims to show through the approach of complex networks how the spread of the disease behaves, and, thus, researchers from other areas can delve into the characteristics that cause this behavior.

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Newton’s Gravitational Law for Link Prediction in Social Networks

Cited by 9 publications

References 38 publications

Evolving Networks and Social Network Analysis Methods and Techniques

Evolving Networks and Social Network Analysis Methods and Techniques

Identification of COVID-19 Spreaders Using Multiplex Networks Approach

Analysis and Characterization of the Spread of COVID-19 in Mexico through Complex Networks and Optimization Approaches

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