scholarly journals Multi-mode Network Analysis under Differential Privacy

2021 ◽  
Vol 2082 (1) ◽  
pp. 012010
Author(s):  
Yuning Song ◽  
Liping Ding ◽  
Mengying Dong ◽  
Xuehua Liu ◽  
Xiao Wang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Differential Privacy ◽  
Personal Information ◽  
Rapid Development ◽  
Experimental Simulation ◽  
The Public ◽  
Centrality Analysis ◽  
Multi Mode

Abstract With the advent of the big data era and the advancement of social network analysis, the public is increasingly concerned about the privacy protection in today’s complex social networks. For the past few years, the rapid development of differential privacy (DP) technology, as a method with a reliable theoretical basis, can effectively solve the key problem of how to “disassociate” personal information in social networks. This paper focuses on the multi-mode heterogeneous network model which has attracted a lot of attention in the field of network research. It introduces differential privacy and its application in big social networks briefly first, and then proposes a centrality-analysis method based on DP in a typical social network, i.e. the multi-mode network. The calculation principle and applicable scenarios are discussed. Then, its utility is analyzed and evaluated through experimental simulation. Possible improvement of DP algorithm in multi-mode networks above is prospected in the end.

Mobility and structural change through traumatic organizational events

2014 ◽  
Vol 10 (3) ◽  
pp. 382-408 ◽  
Author(s):  
R. Drew Sellers ◽  
Timothy J. Fogerty ◽  
Larry M. Parker
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Professional Service ◽  
Public Accounting ◽  
Content Type ◽  
The Public ◽  
Arthur Andersen ◽  
Practical Implications

Purpose – This paper aims to, using evidence from a former office of the public accounting firm Arthur Andersen, to study the importance of the relational content and structure of individuals’ social connections as they transitioned to subsequent employment. The paper also examines the maintenance of their social networks through time. Implications for careers in the accounting field are offered. Practicing accountants’ connections with other individuals have often been recognized as an important resource that influences career success. However, these social networks have escaped systematic academic study in accounting. Design/methodology/approach – Social network analysis, built on survey data. Findings – The results show that who one was connected to in a previous employment was more important than one’s overall network position when deciding whether to stay or exit public accounting. However those who exited public accounting did not demonstrate a handicap in maintaining network structures after the disbanding of the firm. Research limitations/implications – This study is limited to firm members, and to a single office of a firm. Social network analysis was used as a research tool for the sociology of public accounting. Practical implications – Implications are for careers in public accounting, and the management of human resources in public accounting is offered. Social implications – The paper has implications for the successfulness of professional service provision in a general sense. Originality/value – Almost a decade of social connection is studied with a method that has not appeared in the discipline but is well regarded in management studies.

Hadoop-Based Distributed K-Shell Decomposition for Social Networks

2018 ◽  
pp. 125-145
Author(s):  
Katerina Pechlivanidou ◽  
Dimitrios Katsaros ◽  
Leandros Tassiulas
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Complex Network ◽  
Online Social Networks ◽  
Distributed Algorithm ◽  
Data Centers ◽  
Large Data ◽  
Complex Network Analysis ◽  
Centrality Analysis

Complex network analysis comprises a popular set of tools for the analysis of online social networks. Among these techniques, k-shell decomposition of a network is a technique that has been used for centrality analysis, for communities' discovery, for the detection of influential spreaders, and so on. The huge volume of input graphs and the environments where the algorithm needs to run, i.e., large data centers, makes none of the existing algorithms appropriate for the decomposition of graphs into shells. In this article, we develop for a distributed algorithm based on MapReduce for the k-shell decomposition of a graph. We furthermore, provide an implementation and assessment of the algorithm using real social network datasets. We analyze the tradeoffs and speedup of the proposed algorithm and conclude for its virtues and shortcomings.

Internet Public Opinion Monitoring Model Based on Cloud Computing

2013 ◽  
Vol 404 ◽  
pp. 744-747
Author(s):  
Zhong Tang He ◽  
Xiao Qing Zhang ◽  
Feng Wei Zhao ◽  
Tong Kai Ji
Keyword(s):  
Social Networks ◽  
Cloud Computing ◽  
Public Opinion ◽  
Social Network ◽  
Online Social Networks ◽  
Rapid Development ◽  
The Public ◽  
Mass Data ◽  
Model Based ◽  
Monitoring Model

With the rapid development of online social networks, such as social network services, BBS, micro-blog and online community, et al., a two-way communication and new media age has been gradually coming. Each one can create their own content and publish the news quickly through online social networks on Internet. Thus, mass data has brought severe challenge to public opinion monitoring. As a kind of novel information computing model, cloud computing technology can effectively deal with the calculation and storage of mass data. In this paper, the public opinion monitoring model based on cloud computing environment is introduced, which can mine and analyze large scale collected data, realize detection and tracking of hot topics, perform social network analysis on the BBS and visualize the analysis results. The public opinion monitoring system based on cloud can provide timely sensitive information and deal with public crisis efficiently. Finally, the advantage is analyzed when cloud computing is applied to public opinion monitoring.

Introduction

2020 ◽  
pp. xxii-13
Author(s):  
Ryan Light ◽  
James Moody
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Bibliometric Analysis ◽  
Social Life ◽  
New Directions ◽  
Central Paradigm

This chapter provides an introduction to this volume on social networks. It argues that social network analysis is greater than a method or data, but serves as a central paradigm for understanding social life. The chapter offers evidence of the influence of social network analysis with a bibliometric analysis of research on social networks. This analysis underscores how pervasive network analysis has become and highlights key theoretical and methodological concerns. It also introduces the sections of the volume broadly structured around theory, methods, broad conceptualizations like culture and temporality, and disciplinary contributions. The chapter concludes by discussing several promising new directions in the field of social network analysis.

The Oxford Handbook of Social Networks

2020 ◽  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Life ◽  
Information Science ◽  
Computational Social Science ◽  
The Social ◽  
Diverse Field ◽  
The Rich

Social networks fundamentally shape our lives. Networks channel the ways that information, emotions, and diseases flow through populations. Networks reflect differences in power and status in settings ranging from small peer groups to international relations across the globe. Network tools even provide insights into the ways that concepts, ideas and other socially generated contents shape culture and meaning. As such, the rich and diverse field of social network analysis has emerged as a central tool across the social sciences. This Handbook provides an overview of the theory, methods, and substantive contributions of this field. The thirty-three chapters move through the basics of social network analysis aimed at those seeking an introduction to advanced and novel approaches to modeling social networks statistically. The Handbook includes chapters on data collection and visualization, theoretical innovations, links between networks and computational social science, and how social network analysis has contributed substantively across numerous fields. As networks are everywhere in social life, the field is inherently interdisciplinary and this Handbook includes contributions from leading scholars in sociology, archaeology, economics, statistics, and information science among others.

scholarly journals Research on Sentiment Tendency and Evolution of Public Opinions in Social Networks of Smart City

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Yanni Liu ◽  
Dongsheng Liu ◽  
Yuwei Chen
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Sentiment Analysis ◽  
Smart City ◽  
Topic Model ◽  
Rapid Development ◽  
Mobile Internet ◽  
Time Curve ◽  
Public Opinions ◽  
The Social

With the rapid development of mobile Internet, the social network has become an important platform for users to receive, release, and disseminate information. In order to get more valuable information and implement effective supervision on public opinions, it is necessary to study the public opinions, sentiment tendency, and the evolution of the hot events in social networks of a smart city. In view of social networks’ characteristics such as short text, rich topics, diverse sentiments, and timeliness, this paper conducts text modeling with words co-occurrence based on the topic model. Besides, the sentiment computing and the time factor are incorporated to construct the dynamic topic-sentiment mixture model (TSTS). Then, four hot events were randomly selected from the microblog as datasets to evaluate the TSTS model in terms of topic feature extraction, sentiment analysis, and time change. The results show that the TSTS model is better than the traditional models in topic extraction and sentiment analysis. Meanwhile, by fitting the time curve of hot events, the change rules of comments in the social network is obtained.

scholarly journals Social Network Analysis Visualization

2015 ◽  
pp. 257-270
Author(s):  
Mohana Shanmugam ◽  
Yusmadi Yah Jusoh ◽  
Rozi Nor Haizan Nor ◽  
Marzanah A. Jabar
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Social Network ◽  
Future Research ◽  
Research Gaps ◽  
The Social ◽  
Network Studies ◽  
Academic Study

The social network surge has become a mainstream subject of academic study in a myriad of disciplines. This chapter posits the social network literature by highlighting the terminologies of social networks and details the types of tools and methodologies used in prior studies. The list is supplemented by identifying the research gaps for future research of interest to both academics and practitioners. Additionally, the case of Facebook is used to study the elements of a social network analysis. This chapter also highlights past validated models with regards to social networks which are deemed significant for online social network studies. Furthermore, this chapter seeks to enlighten our knowledge on social network analysis and tap into the social network capabilities.

scholarly journals Group Relationship Mining of College Students Based on Predictive Social Network

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Huazhang Liu
Keyword(s):  
College Students ◽  
Social Networks ◽  
Social Network ◽  
Complex Network ◽  
Link Prediction ◽  
Rapid Development ◽  
Combination Method ◽  
Prediction Algorithm ◽  
The Social ◽  
Random Block

With the rapid development of the Internet, social networks have shown an unprecedented development trend among college students. Closer social activities among college students have led to the emergence of college students with new social characteristics. The traditional method of college students’ group classification can no longer meet the current demand. Therefore, this paper proposes a social network link prediction method-combination algorithm, which combines neighbor information and a random block. By mining the social networks of college students’ group relationships, the classification of college students’ groups can be realized. Firstly, on the basis of complex network theory, the essential relationship of college student groups under a complex network is analyzed. Secondly, a new combination algorithm is proposed by using the simplest linear combination method to combine the proximity link prediction based on neighbor information and the likelihood analysis link prediction based on a random block. Finally, the proposed combination algorithm is verified by using the social data of college students’ networks. Experimental results show that, compared with the traditional link prediction algorithm, the proposed combination algorithm can effectively dig out the group characteristics of social networks and improve the accuracy of college students’ association classification.

scholarly journals Ego-Centric Social Network Analysis in Marketing of Fish From Cage Culture System In Simalungun District, Indonesia

2019 ◽  
Vol 8 (4) ◽  
pp. 105-113
Author(s):  
Feriel Amelia Sembiring ◽  
Fikarwin Zuska ◽  
Bengkel Ginting ◽  
Rizabuana Ismail ◽  
Henry Sitorus
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Culture System ◽  
Qualitative Approach ◽  
Mutual Trust ◽  
Cage Culture ◽  
The Social ◽  
The Village ◽  
The Relationship

Aquaculture of Cage Culture is one of the main activities carried out by the community in the village of Haranggaol to fulfill their economic needs. This cultivation business establishes a relationship between traders and cages in terms of marketing their crops. There are 3 egocentric actors in the Haranggaol area. They are collectors (entrepreneurs/farmers who own capital), namely the Rohakinian group, the Siharo group, and the Paimaham group. Through these three egocentric actors, a social network is formed with several alters. Based on the qualitative approach with use Ucinet software, the mapping of their social networks can be seen as follows: alter actors connected to the Rohakinian group are 12 farmers in the group and 2 farmers outside the group with a density of 0.033. There are 27 alter actors connected to the Siharo group, 21 from the group and 6 from outside the group with a density of 0.014. There are 27 alter actors connected to the Paimaham group, namely 36 farmers from their groups and 10 farmers outside the group with a density of 0.005. The social networks that occur between these actors are intertwined due to the existence of kinship relationships, family or close friends who know each other among them. The relationship between family, family or close friends built with mutual trust make this network integrated.

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