scholarly journals Detection of Fraud in Mobile Advertising using Machine Learning

2020 ◽  
Vol 9 (6) ◽  
pp. 855-858
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Test Sample ◽  
Generation Process ◽  
Analysis Model ◽  
Mobile Advertising ◽  
Advertising Industry ◽  
Click Fraud ◽  
Wide Range

With ongoing advancements in the field of technology, mobile advertising has emerged as a platform for publishers to earn profit from their free applications. An online attack commonly known as click fraud or ad fraud has added up to the issue of concerns surfacing mobile advertising. Click fraud is the act of generating illegitimate clicks or data events in order to earn illegal income. Generally, click frauds are generated by infusing the genuine code with some illegitimate bot, which clicks on the ad acting as a potential customer. These click frauds are usually planted by the advertisers or the advertising company so that the number of clicks on the ad increases which will give them the ability to charge the publishers with a hefty sum per number of clicks. A number of studies have determined the risks that click fraud poses to mobile advertising and a few solutions have been proposed to detect click frauds. The solution proposed in this paper comprises of a social network analysis model – to detect and categorize fraudulent clicks and then test sample datasets. This social network analysis model takes into consideration a wide range of parameters from a large group of users. A detailed study is conducted for analyzing these parameters in order to separate the parameters, which affect the click fraud generation process largely. These parameters are then tested and categorized into sample datasets. The mobile advertising industry forms a large part of the revenue generated by the advertising industry. Hence, detection of click fraud in mobile advertising is important to ensure that no illegitimate sources are used to generate this revenue. To be precise, the proposed method touches an accuracy of about 92%.

scholarly journals A Brief Overview of Social Network Analysis and its Current State within Romanian Sociology

2013 ◽  
Vol 3 (3) ◽  
pp. 5-11
Author(s):  
Marian-Gabriel Hâncean
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
International Relations ◽  
Early Spring ◽  
Research Topics ◽  
Political Sciences ◽  
Current State ◽  
Wide Range ◽  
Network Studies

Abstract The field of social network studies has been growing within the last 40 years, gathering scholars from a wide range of disciplines (biology, chemistry, geography, international relations, mathematics, political sciences, sociology etc.) and covering diverse substantive research topics. Using Google metrics, the scientific production within the field it is shown to follow an ascending trend since the late 60s. Within the Romanian sociology, social network analysis is still in his early spring, network studies being low in number and rather peripheral. This note gives a brief overview of social network analysis and makes some short references to the current state of the network studies within Romanian sociology

scholarly journals USING PAGERANK AND SOCIAL NETWORK ANALYSIS TO SPECIFY MENTAL HEALTH FACTORS

2021 ◽  
Vol 1 ◽  
pp. 3379-3388
Author(s):  
Arsineh Boodaghian Asl ◽  
Jayanth Raghothama ◽  
Adam Darwich ◽  
Sebastiaan Meijer
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Well Being ◽  
Influential Factors ◽  
System Perspective ◽  
Novel Approach ◽  
Wide Range ◽  
Mental Health Factors ◽  
Mental Well Being

AbstractVarious factors influence mental well-being, and span individual, social and familial levels. These factors are connected in many ways, forming a complex web of factors and providing pathways for developing programs to improve well-being and for further research. These factors can be studied individually using traditional methods and mapped together to be analyzed holistically from a complex system perspective. This study provides a novel approach using PageRank and social network analysis to understand such maps. The motives are: (1) to realize the most influential factors in such complex networks, (2) to understand factors that influence variations from different network aspects. A previously developed map for children's mental well-being was adopted to evaluate the approach. To achieve our motives, we have developed an approach using PageRank and Social Network Analysis. The results indicate that regardless of the network scale, two key factors called "Quantity and Quality of Relationships" and "Advocacy" can influence children's mental well-being significantly. Moreover, the divergence analysis reveals that one factor, "Recognition/Value Placed on well-being at School" causes a wide range of diffusion throughout the system.

scholarly journals Addressing problematic consumption: an action research experience from the Social Network Analysis

AWARI ◽  
2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Juan José Vera ◽  
Nicolás Barroso
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Community Treatment ◽  
Social Intervention ◽  
Social Suffering ◽  
Research Experience ◽  
Treatment Groups ◽  
The Social ◽  
Wide Range

This paper is classified under the trend of studies that make use of ‘Social Network Analysis’ (SNA) to serve as guidelines for social intervention. Within the field of SNA, work has been carried out based on what is referred to as the socio-centric approach, with the aim of revealing a type of complete network, the Subjective Communities Networks, which are built from Community Treatment Groups pertaining to the Argentine Office of Drug-related Comprehensive Policies (SEDRONAR for its Spanish acronym) in order to address problematic abuse in socially vulnerable backgrounds. These groups belong to the ECO2 model, which was devised to intervene in a wide range of social suffering phenomena, and uses the SNA as a theoretical and methodological viewpoint for assessing people and communities. This thought is an attempt to answer the following question: how does SNA help formulate social intervention strategies for SEDRONAR groups in the Province of Mendoza?

scholarly journals Social Network Analysis in Russian Literary Studies

2020 ◽  
pp. 517-536
Author(s):  
Frank Fischer ◽  
Daniil Skorinkin
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Literary Texts ◽  
Literary Studies ◽  
War And Peace ◽  
The Social ◽  
Wide Range ◽  
Russian Literary ◽  
Simple Fashion

AbstractNetwork analysis as a method has applications in a wide range of fields from physics to epidemiology and from sociology to political science, and in the meantime has also reached the literary studies. Networks can be leveraged to examine intertextual relations or even artistic influences, but the main application so far has been the analysis of social formations and character interactions within fictional worlds. To make this possible, texts have to be formalized into a set of nodes and edges, where nodes represent characters and edges describe the relations between these characters in a very simple fashion: Do they or don’t they interact? Based on a selection of Russian plays and Tolstoy’s novel War and Peace, we will describe approaches to the social network analysis of literary texts.

scholarly journals Social Network Analysis using RLVECN: Representation Learning via Knowledge-Graph Embeddings and Convolutional Neural-Network

2020 ◽  
Author(s):  
Bonaventure C. Molokwu ◽  
Ziad Kobti
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Representation Learning ◽  
Knowledge Graph ◽  
Graph Embeddings ◽  
Social Graph ◽  
Wide Range ◽  
Social Graphs ◽  
Prediction Problems

Social Network Analysis (SNA) has become a very interesting research topic with regard to Artificial Intelligence (AI) because a wide range of activities, comprising animate and inanimate entities, can be examined by means of social graphs. Consequently, classification and prediction tasks in SNA remain open problems with respect to AI. Latent representations about social graphs can be effectively exploited for training AI models in a bid to detect clusters via classification of actors as well as predict ties with regard to a given social network. The inherent representations of a social graph are relevant to understanding the nature and dynamics of a given social network. Thus, our research work proposes a unique hybrid model: Representation Learning via Knowledge-Graph Embeddings and ConvNet (RLVECN). RLVECN is designed for studying and extracting meaningful representations from social graphs to aid in node classification, community detection, and link prediction problems. RLVECN utilizes an edge sampling approach for exploiting features of the social graph via learning the context of each actor with respect to its neighboring actors.

scholarly journals A Social Network Analysis of Tweets Related to Masks during the COVID-19 Pandemic

2020 ◽  
Vol 17 (21) ◽  
pp. 8235
Author(s):  
Wasim Ahmed ◽  
Josep Vidal-Alaball ◽  
Francesc Lopez Segui ◽  
Pedro A. Moreno-Sánchez
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Communication Channel ◽  
The Public ◽  
Twitter Data ◽  
Wide Range ◽  
Media Research ◽  
Systematic Random Sample

Background: High compliance in wearing a mask is a crucial factor for stopping the transmission of COVID-19. Since the beginning of the pandemic, social media has been a key communication channel for citizens. This study focused on analyzing content from Twitter related to masks during the COVID-19 pandemic. Methods: Twitter data were collected using the keyword “mask” from 27 June 2020 to 4 July 2020. The total number of tweets gathered were n = 452,430. A systematic random sample of 1% (n = 4525) of tweets was analyzed using social network analysis. NodeXL (Social Media Research Foundation, California, CA, USA) was used to identify users ranked influential by betweenness centrality and was used to identify key hashtags and content. Results: The overall shape of the network resembled a community network because there was a range of users conversing amongst each other in different clusters. It was found that a range of accounts were influential and/or mentioned within the network. These ranged from ordinary citizens, politicians, and popular culture figures. The most common theme and popular hashtags to emerge from the data encouraged the public to wear masks. Conclusion: Towards the end of June 2020, Twitter was utilized by the public to encourage others to wear masks and discussions around masks included a wide range of users.

scholarly journals Temporal and Atemporal Provider Network Analysis in a Breast Cancer Cohort from an Academic Medical Center (USA)

Informatics ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 34 ◽  
Author(s):  
Bryan Steitz ◽  
Mia Levy
Keyword(s):  
Breast Cancer ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Early Stage ◽  
Medical Center ◽  
Clinical Care ◽  
Academic Medical Center ◽  
Breast Cancer Patients ◽  
Analysis Model

Social network analysis (SNA) is a quantitative approach to study relationships between individuals. Current SNA methods use static models of organizations, which simplify network dynamics. To better represent the dynamic nature of clinical care, we developed a temporal social network analysis model to better represent care temporality. We applied our model to appointment data from a single institution for early stage breast cancer patients. Our cohort of 4082 patients were treated by 2190 providers. Providers had 54,695 unique relationships when calculated using our temporal method, compared to 249,075 when calculated using the atemporal method. We found that traditional atemporal approaches to network modeling overestimate the number of provider-provider relationships and underestimate common network measures such as care density within a network. Social network analysis, when modeled accurately, is a powerful tool for organizational research within the healthcare domain.

scholarly journals Exploring the Interdisciplinary Nature of Precision Medicine with Social Network Analysis and Co-Occurrence Analysis (Preprint)

2020 ◽  
Author(s):  
Xin Xu ◽  
Xiaoguang Lyu ◽  
Jiming Hu ◽  
He Huang ◽  
Xingyu Cheng
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Precision Medicine ◽  
Interdisciplinary Collaboration ◽  
Time Frame ◽  
Interdisciplinary Cooperation ◽  
Collaboration Network ◽  
Developmental Trend ◽  
Wide Range

BACKGROUND From the perspective of medical informatics, interdisciplinary research is an important feature of PM. However, a detailed and accurate assessment of the cross-disciplinary status of PM is still lacking. OBJECTIVE The aim of this study is to present the nature of interdisciplinary collaboration in precision medicine (PM) based on co-occurrences and social network analysis. METHODS PM studies published between 2010 and 2019 were collected from the Web of Science database. We analyzed interdisciplinarity with descriptive statistics, co-occurrence and social network analysis. An evolutionary graph and strategic diagram were created to clarify the development of streams and trends in disciplinary communities. RESULTS The results indicate that many disciplines are involved in PM research and cover a wide range. However, the disciplinary distribution is unbalanced. Current cross-disciplinary collaboration in PM mainly focuses on clinical application and technology-associated disciplines. The characteristics of the disciplinary collaboration network are as follows: (1) disciplinary cooperation in PM is not mature or centralized; (2) the leading disciplines are absent; (3) the pattern of disciplinary cooperation is mostly indirect rather than direct. There are seven interdisciplinary communities in the PM collaboration network; however, their positions in the network differ. Community 4, with disciplines such as genetics & heredity in the core position, is the most central and cooperative discipline in the interdisciplinary network. This indicates that Community 4 represents a relatively mature direction in interdisciplinary cooperation in PM. Finally, according to the evolution graph, we clearly present the development streams of disciplinary collaborations in PM. We describe the scale and the time frame for development trends and distributions in detail. Importantly, we accurately estimate the developmental trend of PM based on evolution graphs, such as biological big data processing, molecular imaging and widespread clinical applications. CONCLUSIONS This study can help researchers, clinicians, and policymakers comprehensively understand the overall network of interdisciplinary cooperation in PM. More importantly, we quantitatively and precisely present the history of interdisciplinary cooperation and accurately predict the developing trends of interdisciplinary cooperation in PM.

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