Implicit Social Networking: Discovery of Hidden Relationships, Roles and Communities among Consumers

Podobnik, Vedran; Lovrek, Ignac

doi:10.1016/j.procs.2015.08.185

Cited by 11 publications

(7 citation statements)

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“…This method is commonly used in social sciences to analyse networks of individuals or organizations with common interests and their interactions using information from social networks [32]. This allows visualization of social networks, where interconnections between the actors are indicated by links and actors themselves indicated by nodes.…”

Section: Methodsmentioning

confidence: 99%

Difference between Bibliometric and Grey Data. Transdisciplinary Bioeconomy Research

Muižniece

Kubule

Žihare

et al. 2020

Environmental and Climate Technologies

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This study is a follow-up to previous research on the use of a transdisciplinary approach in the analysis of factors that influence bioeconomy development and interactions between them. It is very important to consider the significance of public opinion while performing transdisciplinary research. This study integrates public opinion in the bibliometric analysis method by using grey data from information available in the public information space: mass media and social networks. The network of factors influencing the development of bioeconomy, created with the help of social network analysis method, was compared with the one obtained from bibliometric analysis of SCOPUS database in order to find out the difference between scientific tendencies and the overall atmosphere in the information space. Although the structure of these networks varies, there is a common tendency in literature to identify the following factors as the key factors in the development of the bioeconomy: research and innovations; technology; energy and energy consumption.

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Section: Methodsmentioning

confidence: 99%

Difference between Bibliometric and Grey Data. Transdisciplinary Bioeconomy Research

Muižniece

Kubule

Žihare

et al. 2020

Environmental and Climate Technologies

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show abstract

“…It is surprising to see that the literature in this direction is very limited. To the best of the author's knowledge, other than the Podobnik and Lovrek (2015) and van de there does not exist any study that deals with this problem. From the E-Commerce house perspective, one viable data where interactions between users and items are recorded is the user-item rating data.…”

Section: Abstract Social Networkmentioning

confidence: 99%

Designing and Connectivity Checking of Implicit Social Networks from the User-Item Rating Data

Banerjee¹

2020

Preprint

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Implicit Social Network is a connected social structure among a group of persons, where two of them are linked if they have some common interest. One real-life example of such networks is the implicit social network among the customers of an online commercial house, where there exist an edge between two customers if they like similar items. Such networks are often useful for different commercial applications such as target advertisement, viral marketing, etc. In this article, we study two fundamental problems in this direction. The first one is that, given the user-item rating data of an E-Commerce house, how we can design implicit social networks among its users and the second one is at the time of designing itself can we obtain the connectivity information among the users. Formally, we call the first problem as the Implicit User Network Design Problem and the second one as Implicit User Network Design with Connectivity Checking Problem. For the first problem, we propose three different algorithms, namely 'Exhaustive Search Approach', 'Clique Addition Approach', and 'Matrix Multiplication-Based Approach'. For the second problem, we propose two different approaches. The first one is the sequential approach: designing and then connectivity checking, and the other one is a concurrent approach, which is basically an incremental algorithm that performs designing and connectivity checking simultaneously. Proposed methodologies have experimented with three publicly available rating network datasets such as Flixter, Movielens, and Epinions. Reported computational time shows that the 'Clique Addition Approach' is the fastest one for designing the implicit social network. For designing and connectivity check-The author is supported by the Post Doctoral Fellowship Grant provided by Indian Institute of Technology Gandhinagar (Project No. MIS/IITGN/PD-SCH/201415/006). A small part of this study has been previously published as Banerjee et al. (2017).

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“…The methodological procedure of SNA includes determining the cooperation among nodes and the analysis of hidden relationships (Podobnik and Lovrek, 2015;de-Marcos et al, 2016;Erfanmanesh and Hosseini, 2016;Schlattmann, 2017). Therefore, the SNA methods in this study are summarized and explained as follows:…”

Section: Modeling and Evaluation: Sna Approachmentioning

confidence: 99%

Identifying Leading Nodes of PM2.5 Monitoring Network in Taiwan with Big Data-oriented Social Network Analysis

Chang

2019

Aerosol Air Qual. Res.

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TEPA (Taiwan Environmental Protection Administration) currently has regulated six types of air pollutants based on the AQI. Among these, the three items most prone to exceeding the standard are PM 2.5 , PM 10 , and O 3 , in that order. PM 2.5 pollution episodes in Taiwan mainly occur in winter and spring when the northeast monsoon prevails. In addition to local pollution sources, transboundary air pollution affects Taiwan. Obviously, the existing AQ monitoring data analyzed by the BD-oriented perspective not only simplifies the simulation calculation and verification resources of the AQ model but also assists in realtime insight into the causal relationships between the AQ and important parameters of meteorology, pollution sources, and regions. This study integrates the BD-oriented Social Network Analysis (SNA) approach and data visualization tools to analyze the event co-occurrence and spatial correlation characteristics of two pollution scenarios for AQ monitoring stations based on two severe PM 2.5 pollution conditions: (1) the Z-value of PM 2.5 daily average concentration is higher than 1.65 (Scenario I), and (2) the daily average concentration of PM 2.5 exceeds TEPA's regulation on the AQ standard (Scenario II), to identify the regional leading nodes suitable for different pollution scenarios. Furthermore, Principal Component Analysis (PCA) and time series data are employed to verify the spatial-temporal representation of these leading nodes, which can be regarded as means to the real-time AQ management decision-making as well as instant transboundary pollution precaution in the future. This study contributes to the application of the discrete data-driven approach (SNA) and the continuous data-driven approach (PCA) in an ambient AQ monitoring network, which can clearly explain and analyze the regional high pollution characteristics of PM 2.5 in Scenarios I and II. The results of this study are consistent with previously relevant findings in Taiwan.

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Implicit Social Networking: Discovery of Hidden Relationships, Roles and Communities among Consumers

Cited by 11 publications

References 0 publications

Difference between Bibliometric and Grey Data. Transdisciplinary Bioeconomy Research

Difference between Bibliometric and Grey Data. Transdisciplinary Bioeconomy Research

Designing and Connectivity Checking of Implicit Social Networks from the User-Item Rating Data

Identifying Leading Nodes of PM2.5 Monitoring Network in Taiwan with Big Data-oriented Social Network Analysis

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