scholarly journals A Method of Vehicle Route Prediction Based on Social Network Analysis

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Ning Ye ◽  
Zhong-qin Wang ◽  
Reza Malekian ◽  
Ying-ya Zhang ◽  
Ru-chuan Wang
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Model Parameters ◽  
Data Set ◽  
Vehicle Route ◽  
Route Prediction ◽  
The Matrix ◽  
The Difference ◽  
Leave One Out

A method of vehicle route prediction based on social network analysis is proposed in this paper. The difference from proposed work is that, according to our collected vehicles’ past trips, we build a relationship model between different road segments rather than find the driving regularity of vehicles to predict upcoming routes. In this paper, firstly we depend on graph theory to build an initial road network model and modify related model parameters based on the collected data set. Then we transform the model into a matrix. Secondly, two concepts from social network analysis are introduced to describe the meaning of the matrix and we process it by current software of social network analysis. Thirdly, we design the algorithm of vehicle route prediction based on the above processing results. Finally, we use the leave-one-out approach to verify the efficiency of our algorithm.

Who is the boss? Identifying key roles in telecom fraud network via centrality-guided deep random walk

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi-Chun Chang ◽  
Kuan-Ting Lai ◽  
Seng-Cho T. Chou ◽  
Wei-Chuan Chiang ◽  
Yuan-Chen Lin
Keyword(s):  
Social Network ◽  
Random Walk ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Random Walks ◽  
Interpersonal Relationships ◽  
Interaction Network ◽  
Data Set ◽  
Content Type ◽  
Structural Equivalence

PurposeTelecommunication (telecom) fraud is one of the most common crimes and causes the greatest financial losses. To effectively eradicate fraud groups, the key fraudsters must be identified and captured. One strategy is to analyze the fraud interaction network using social network analysis. However, the underlying structures of fraud networks are different from those of common social networks, which makes traditional indicators such as centrality not directly applicable. Recently, a new line of research called deep random walk has emerged. These methods utilize random walks to explore local information and then apply deep learning algorithms to learn the representative feature vectors. Although effective for many types of networks, random walk is used for discovering local structural equivalence and does not consider the global properties of nodes.Design/methodology/approachThe authors proposed a new method to combine the merits of deep random walk and social network analysis, which is called centrality-guided deep random walk. By using the centrality of nodes as edge weights, the authors’ biased random walks implicitly consider the global importance of nodes and can thus find key fraudster roles more accurately. To evaluate the authors’ algorithm, a real telecom fraud data set with around 562 fraudsters was built, which is the largest telecom fraud network to date.FindingsThe authors’ proposed method achieved better results than traditional centrality indices and various deep random walk algorithms and successfully identified key roles in a fraud network.Research limitations/implicationsThe study used co-offending and flight record to construct a criminal network, more interpersonal relationships of fraudsters, such as friendships and relatives, can be included in the future.Originality/valueThis paper proposed a novel algorithm, centrality-guided deep random walk, and applied it to a new telecom fraud data set. Experimental results show that the authors’ method can successfully identify the key roles in a fraud group and outperform other baseline methods. To the best of the authors’ knowledge, it is the largest analysis of telecom fraud network to date.

scholarly journals COVID-19 and the “Film Your Hospital” Conspiracy Theory: Social Network Analysis of Twitter Data (Preprint)

2020 ◽  
Author(s):  
Wasim Ahmed ◽  
Francesc López Seguí ◽  
Josep Vidal-Alaball ◽  
Matthew S Katz
Keyword(s):  
Public Health ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Information Source ◽  
Conspiracy Theory ◽  
Sources Of Information ◽  
Data Set ◽  
Conspiracy Theories ◽  
Twitter Data

BACKGROUND During the COVID-19 pandemic, a number of conspiracy theories have emerged. A popular theory posits that the pandemic is a hoax and suggests that certain hospitals are “empty.” Research has shown that accepting conspiracy theories increases the likelihood that an individual may ignore government advice about social distancing and other public health interventions. Due to the possibility of a second wave and future pandemics, it is important to gain an understanding of the drivers of misinformation and strategies to mitigate it. OBJECTIVE This study set out to evaluate the #FilmYourHospital conspiracy theory on Twitter, attempting to understand the drivers behind it. More specifically, the objectives were to determine which online sources of information were used as evidence to support the theory, the ratio of automated to organic accounts in the network, and what lessons can be learned to mitigate the spread of such a conspiracy theory in the future. METHODS Twitter data related to the #FilmYourHospital hashtag were retrieved and analyzed using social network analysis across a 7-day period from April 13-20, 2020. The data set consisted of 22,785 tweets and 11,333 Twitter users. The Botometer tool was used to identify accounts with a higher probability of being bots. RESULTS The most important drivers of the conspiracy theory are ordinary citizens; one of the most influential accounts is a Brexit supporter. We found that YouTube was the information source most linked to by users. The most retweeted post belonged to a verified Twitter user, indicating that the user may have had more influence on the platform. There was a small number of automated accounts (bots) and deleted accounts within the network. CONCLUSIONS Hashtags using and sharing conspiracy theories can be targeted in an effort to delegitimize content containing misinformation. Social media organizations need to bolster their efforts to label or remove content that contains misinformation. Public health authorities could enlist the assistance of influencers in spreading antinarrative content.

scholarly journals COVID-19 and the “Film Your Hospital” Conspiracy Theory: Social Network Analysis of Twitter Data

10.2196/22374 ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. e22374 ◽  
Author(s):  
Wasim Ahmed ◽  
Francesc López Seguí ◽  
Josep Vidal-Alaball ◽  
Matthew S Katz
Keyword(s):  
Public Health ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Information Source ◽  
Conspiracy Theory ◽  
Sources Of Information ◽  
Data Set ◽  
Conspiracy Theories ◽  
Twitter Data

Background During the COVID-19 pandemic, a number of conspiracy theories have emerged. A popular theory posits that the pandemic is a hoax and suggests that certain hospitals are “empty.” Research has shown that accepting conspiracy theories increases the likelihood that an individual may ignore government advice about social distancing and other public health interventions. Due to the possibility of a second wave and future pandemics, it is important to gain an understanding of the drivers of misinformation and strategies to mitigate it. Objective This study set out to evaluate the #FilmYourHospital conspiracy theory on Twitter, attempting to understand the drivers behind it. More specifically, the objectives were to determine which online sources of information were used as evidence to support the theory, the ratio of automated to organic accounts in the network, and what lessons can be learned to mitigate the spread of such a conspiracy theory in the future. Methods Twitter data related to the #FilmYourHospital hashtag were retrieved and analyzed using social network analysis across a 7-day period from April 13-20, 2020. The data set consisted of 22,785 tweets and 11,333 Twitter users. The Botometer tool was used to identify accounts with a higher probability of being bots. Results The most important drivers of the conspiracy theory are ordinary citizens; one of the most influential accounts is a Brexit supporter. We found that YouTube was the information source most linked to by users. The most retweeted post belonged to a verified Twitter user, indicating that the user may have had more influence on the platform. There was a small number of automated accounts (bots) and deleted accounts within the network. Conclusions Hashtags using and sharing conspiracy theories can be targeted in an effort to delegitimize content containing misinformation. Social media organizations need to bolster their efforts to label or remove content that contains misinformation. Public health authorities could enlist the assistance of influencers in spreading antinarrative content.

Do shareholder's independency and board size affect synergies from cross-border bank mergers and acquisitions? International evidence from social network analysis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sheng-Hung Chen ◽  
Feng-Jui Hsu ◽  
Ying-Chen Lai
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Ordinary Least Squares ◽  
Standard Errors ◽  
Board Size ◽  
Content Type ◽  
Cross Border ◽  
The Difference

PurposeThere is little known globally on the association among the independent shareholder, board size and merger and acquisition (M&A) performance. This paper addresses the global issue about cross-border M&A in banking sector, particularly exploring the role of difference in the independent shareholder and board size between acquirer and target banks on synergy gains based on the international study.Design/methodology/approachBased on cross-border bank M&As data on 59 deals from 1995 to 2009, we initially apply social network analysis techniques to explore the country connectedness of the acquirer-target banks in cross-border M&As. Ordinary least squares (OLS) with robust standard errors is further used to investigate synergy gains within the difference in the degree of bank independent shareholder and board sizes between the acquirer and target banks.FindingsOur results indicate that the acquiring banks are generally interconnected with the targeted banks and that some of acquiring banks are clearly concentrated in Asian countries including China, Hong Kong, and Philippines. Moreover, we find that cross-border M&As with larger difference in independent shareholders between the bidder and target bank would result in higher synergy gains in all cases of takeover premiums on 1 day, 1 week and 4 weeks. In addition, financial differences between the bidder and target banks have a significant impact on synergetic gains, a topic not explored in previous studies. There is no evidence that institutional and governance differences between bidder and target bank have significant cross-border impacts on takeover premiums with respect to 1 day, 1 week and 4 weeks, respectively.Originality/valueThis paper contributes to the literature by exploring the international issue about the role of difference in the degree of bank independent shareholder and board sizes between acquirer and target banks on synergy gains. Based on bank cross-border M&As data on 59 deals from 1995 to 2009, we initially apply social network analysis to explore the country connectedness of acquirer-target bank in cross-border M&As, while ten ordinary least squares (OLS) with robust standard errors is used to investigate synergy gains within the difference in the degree of bank independent shareholder and board sizes between acquirer and target banks.

The Rise of Fashion Informatics: A Case of Data-Mining-Based Social Network Analysis in Fashion

2018 ◽  
Vol 37 (2) ◽  
pp. 87-102 ◽  
Author(s):  
Li Zhao ◽  
Chao Min
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Large Scale ◽  
Dynamic Network ◽  
Fashion Industry ◽  
Cognitive Computing ◽  
Data Set

With the advent of modern cognitive computing technologies, fashion informatics researchers contribute to the academic and professional discussion about how a large-scale data set is able to reshape the fashion industry. Data-mining-based social network analysis is a promising area of fashion informatics to investigate relations and information flow among fashion units. By adopting this pragmatic approach, we provide dynamic network visualizations of the case of Paris Fashion Week. Three time periods were researched to monitor the formulation and mobilization of social media users’ discussions of the event. Initial textual data on social media were crawled, converted, calculated, and visualized by Python and Gephi. The most influential nodes (hashtags) that function as junctions and the distinct hashtag communities were identified and represented visually as graphs. The relations between the contextual clusters and the role of junctions in linking these clusters were investigated and interpreted.

scholarly journals Prediction of MicroRNA-Disease Associations Based on Social Network Analysis Methods

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Quan Zou ◽  
Jinjin Li ◽  
Qingqi Hong ◽  
Ziyu Lin ◽  
Yun Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Cross Validation ◽  
Supervised Machine Learning ◽  
Rna Molecules ◽  
Disease Associations ◽  
Endogenous Genes ◽  
Leave One Out

MicroRNAs constitute an important class of noncoding, single-stranded, ~22 nucleotide long RNA molecules encoded by endogenous genes. They play an important role in regulating gene transcription and the regulation of normal development. MicroRNAs can be associated with disease; however, only a few microRNA-disease associations have been confirmed by traditional experimental approaches. We introduce two methods to predict microRNA-disease association. The first method, KATZ, focuses on integrating the social network analysis method with machine learning and is based on networks derived from known microRNA-disease associations, disease-disease associations, and microRNA-microRNA associations. The other method, CATAPULT, is a supervised machine learning method. We applied the two methods to 242 known microRNA-disease associations and evaluated their performance using leave-one-out cross-validation and 3-fold cross-validation. Experiments proved that our methods outperformed the state-of-the-art methods.

Vocational Rehabilitation Service Patterns: An Application of Social Network Analysis to Examine Employment Outcomes of Transition-Age Individuals With Autism

2017 ◽  
Vol 61 (3) ◽  
pp. 143-153 ◽  
Author(s):  
Nicole M. Ditchman ◽  
Jennifer L. Miller ◽  
Amanda B. Easton
Keyword(s):  
Young Adults ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Vocational Rehabilitation ◽  
Employment Outcomes ◽  
Autism Spectrum ◽  
Rehabilitation Services ◽  
Data Set ◽  
Service Patterns

Young adults with autism spectrum disorder (ASD) face poor employment outcomes following transition from school to adult life. Social network analysis is a useful approach for examining service patterns associated with employment success for this population. An advantage of this approach is its focus on the interdependence of variables rather than individual predictors. This study applies network methodology to examine the relations between vocational rehabilitation services and young adults with ASD to predict employment status. Using the Rehabilitation Services Administration (RSA-911) data set, participants included 2,219 individuals with ASD between the ages of 16 and 24 served by the public vocational rehabilitation system and closed as either competitively employed or not employed. A two-mode network was constructed such that a relation was defined for each service an individual received. Results from a core-periphery analysis indicated that of the 22 services available, core services included assessment, counseling/guidance, job placement, on-the-job support, job search support, and transportation services. Follow-up analyses suggested that the greater number of these six core services an individual received, the better the employment outcome. Findings highlight that these services should be viewed as interconnected and suggest a set of six core services that may be particularly beneficial for this population.

Data Preprocessing for Dynamic Social Network Analysis

2013 ◽  
pp. 25-39 ◽  
Author(s):  
Preeti Gupta ◽  
Vishal Bhatnagar
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Data Preprocessing ◽  
Original Data ◽  
Network Data ◽  
Data Set ◽  
Social Network Data ◽  
Dynamic Social Network ◽  
The Social

The social network analysis is of significant interest in various application domains due to its inherent richness. Social network analysis like any other data analysis is limited by the quality and quantity of data and for which data preprocessing plays the key role. Before the discovery of useful information or pattern from the social network data set, the original data set must be converted to a suitable format. In this chapter we present various phases of social network data preprocessing. In this context, the authors discuss various challenges in each phase. The goal of this chapter is to illustrate the importance of data preprocessing for social network analysis.

The Relationship between Governance Networks and Social Networks: Progress, Problems and Prospects

2017 ◽  
Vol 16 (4) ◽  
pp. 331-341 ◽  
Author(s):  
Gaby Ramia ◽  
Roger Patulny ◽  
Greg Marston ◽  
Kyla Cassells
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Dark Side ◽  
The Social ◽  
Power Differentials ◽  
Governance Networks ◽  
The Difference ◽  
The Relationship

A governance networks literature that uses social network analysis has emerged, but research tends to be more technical than conceptual. This restricts its accessibility and usefulness for non-quantitative scholars and practitioners alike. Furthermore, the literature has not adequately appreciated the importance of informal networking for the effective operation of governance networks. This can hinder inter-disciplinary analysis. Through a critical review, this article identifies four areas of challenge for the governance networks literature and offers four corresponding, complementary sets of concepts from the social network analysis field: (a) the difference between policy networks and governance networks, (b) the role and status of people in governance networks, (c) the ‘dark side’ of networks and the role of power differentials within them and (d) network evaluation and the question of ‘what works’ in network management. The article argues that a less technical, more accessible account of social network analysis offers an additional lens through which to view governance networks.

Social network analyses of positive and negative relationships among Japanese preschool classmates

2009 ◽  
Vol 33 (3) ◽  
pp. 193-201 ◽  
Author(s):  
Keiko K. Fujisawa ◽  
Nobuyuki Kutsukake ◽  
Toshikazu Hasegawa
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Positive Relationships ◽  
Network Analyses ◽  
The Difference ◽  
Negative Relationships ◽  
Social Network Analyses

Using social network analysis, we investigated the characteristics of social networks composed of positive relationships (positive network: PN) and negative relationships (negative network: NN) in classrooms of Japanese 3- and 4-year-olds. Analysis of “density” showed that PNs were denser than NNs among 4-year-olds but that this was not the case among 3-year-olds. The difference between the probability of dyads of girls forming cliques, between PNs and NNs, was larger than that for dyads of boys or mixed-sex dyads. Four-year-olds formed cliques more often in PNs than in NNs, compared to 3-year-olds. This study showed that both sex combination of dyads and age affect the quantified properties of social networks among preschoolers.

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