Representation and Analysis of Kinship, Based on the Naming Pattern

This chapter discusses the integration of information retrieval information from two sources: a social network and a document reference network, for enhancing reference based search engine rankings. In particular, current models of information retrieval are blind to the social context that surrounds information resources thus do not consider the trustworthiness of their authors when they present the query results to the users. Following this point we elaborate on the basic intuitions that highlight the contribution of the social context – as can be mined from social network positions for instance – into the improvement of the rankings provided in reference based search engines. A review on ranking models in web search engine retrieval along with social network metrics of importance such as prestige and centrality is provided as a background. Then a presentation of recent research models that utilize both contexts is provided along with a case study in the internet based encyclopedia Wikipedia based on the social network metrics.

‘Entanglement’ – A new dynamic metric to measure team flow

Humans are deeply social, and in early life, classroom friendships shape academic and socio-emotional development. Yet, research has only begun to explore how social network structures are related to classroom-level outcomes. Using a large-scale data set of over 46,000 students in nearly 2,000 classrooms, we extracted 313 social network metrics from four different network types (i.e., friendship, rejection, help, and break-time contacts) and applied machine learning to predict various classroom-level educational outcomes, including academic achievement, motivation, and social integration. Classroom networks predicted both academic achievement and well-being, beyond school tracks, socioeconomic status, and demographics. The predictive power for different educational outcomes was highly dependent upon the type of network and categories of network metrics. These insights position social networks as a powerful tool for understanding educational environments and informing network-based interventions.

Patients with cerebellar ataxia experience fatigue, impaired executive function, and psychosocial deficits. Personal social networks affect physical and mental well-being but there are no data on their effect on quality of life (QOL) and function in ataxia. We examined social network metrics in patients with cerebellar ataxia to test the hypothesis that supportive relationships enhance quality of life and physical function. We used a cross-sectional, survey-interview design with the Personal Network Survey for Clinical Research, World Health Organization QOL-BREF (WHOQOL), Functional Staging Scale for Ataxia, Friedreich's Ataxia Rating Scale-Activities of Daily Living (FARS-ADL), and Patient-Reported Outcome Measure of Ataxia (PROM-Ataxia). We used univariate and bivariate descriptive statistics and bivariate correlations to explore relationships between social network characteristics and QOL, and multivariable linear regression for associations between them. In 106 ataxia patients (56 ± 15.3years), social network size averaged 7.6 ± 2.8 people, mostly friends (52%) and family (33%). Social networks were dense (0.7 ± 0.3) and constrained (0.5 ± 0.1). Omnibus test showed that positive relationships, camaraderie, weekly communication, and high levels of emotional support correlated with PROM-Ataxia Total (p = 0.03), PROM-Ataxia Mental health (p = 0.05), WHOQOL (p = 0.03), FARS-ADL and Functional Staging. Those with constrained social networks and fewer positive relationships reported low QOL, as did those with frequent therapy/counseling and organizational involvement. Positive relationships within social networks of cerebellar ataxia patients positively influence QOL and functional measures. Counterintuitive negative associations with external sources of support need further study to explore causality. Network interventions to enhance emotional support and camaraderie may improve quality of life.

Social Network Metrics: The Boardex Case Study

In the context of crowdsourcing contests, where winners take all, attracting high-quality solvers and solutions presents a significant challenge. A key issue in this environment is protecting solvers’ intellectual property and preventing fraud risks such as solution plagiarism and theft. Addressing these challenges is essential for maintaining the integrity of the platform and encouraging innovation. This study applies social network analysis to examine the structural characteristics of fraudulent seekers and investigate whether they exhibit distinct social network features compared to legitimate users. Specifically, we focus on centrality, cohesion, and structural equivalence to identify potential markers of fraudulent intent. Using a dataset from 9,282 contest projects initiated in China in 2014, involving 6,241 active users and 246 fraudulent seekers, we tested a fraud detection model based on social network metrics. The results reveal significant differences in degree centrality, betweenness centrality, closeness centrality, and clustering coefficients between fraudulent and non-fraudulent nodes. The findings demonstrate that social network features, particularly centrality measures, can effectively differentiate fraudulent seekers from legitimate users. This study contributes to the theoretical understanding of fraud detection in crowdsourcing and offers practical insights for the development of more robust fraud detection strategies.

Classic collaborative filtering methods suffer from lack of accuracy when it comes to todays complicated ecommerce websites. Recommender systems can be more efficient and effective by considering each user behavior model individually and in groups with other people that can be interpreted as their social relations. This paper presents a new context aware and behavior-based recommender system which utilizes social network analysis to enhance recommending results. The general idea is extracting user behavior patterns in different context based on user's past interactions with the system for further classifications. In this study we use the idea of using social network analysis with an improvement; we consider multiple relations among users and use a multi-layer social network to make our groups of preferences richer. Different social network metrics such as centralization, modularity class and so on can help us find similar groups of users in social network. We combine all the results and suggest top-K items to the user based on his/her nearest neighbor opinions. Evaluating the results shown significant improvement in accuracy of recommendations for proposed approach in contrast to previous techniques.

Scale-free networks, as one major class of complex networks, are characterized by a specific structural feature of power-law degree distributions. Examples of scale-free real-world networks include World Wide Web (WWW), email networks, citation and co-author scientific networks, social networks (Facebook, Twitter), networks of Internet routers, and protein-protein interaction networks. In this paper we model and evaluate the reliability of scale-free complex networks using a binary decision diagram (BDD)-based method. Two-terminal reliability is considered, which is defined as the probability that a specified pair of nodes can communication through at least one fault-tree path in the network. This reliability metric can measure, for example, how reliable the information can be shared between two parties or in general how reliable the information can be disseminated within a social network. The Barabasi-Albert model is used for generating the sample scale-free networks studied in this paper. The reliability performance of scale-free networks is compared to that of random networks under the Erdos-Renyi model. Degree distributions and clustering coefficients (average fraction of pairs of neighbors of a node which are also neighbors of each other) of sample scale-free and random networks are illustrated and compared. Their effects on the performance of the BDD-based method for the reliability analysis of both scale-free and random networks are also discussed.

The article considers the problem of detecting anomalies in the time series of media content metrics obtained from Telegram channels. The task of detecting anomalies is relevant in the context of combating disinformation and analyzing the dynamics of content distribution in social networks. Anomalies in metrics, such as the number of views, shares, comments, and reactions, may indicate manipulative actions, including the use of bots, falsification of reach, or the spread of disinformation. A dataset obtained through the official Telegram API was used for the analysis. A feature of the data is the lack of retrospective values of metrics, which complicates the analysis of their dynamics. This problem was partially solved by collecting data at fixed time intervals after the publication of each post. The collected data was grouped by the channels of origin of the posts and time intervals after publication to ensure the accuracy of the analysis. Since the data were unlabeled, manual processing was used to remove outliers and ensure the reliability of the modeling. The article analyzes the functionality of five Python libraries for detecting anomalies in time series: PyOD, TODS, PySAD, Darts, and Prophet. Their compliance with the requirements was assessed, in particular, with respect to working with time series, processing incomplete data, real-time support, seasonality, and computational efficiency. A comparison was made based on tables and graphs that demonstrate the results of using each library. In particular, the PyOD library is a well-known tool for detecting anomalies, but does not support direct work with time series. TODS has the potential to detect anomalies in streaming data, but its development has been discontinued. PySAD specializes in streaming data analysis, but requires a fixed frequency of input data, which limits its application. The Darts library offers a wide set of algorithms for time series analysis, but requires pre-filling of missing values, which creates an additional load on the model. The best results were achieved using the Prophet model, which is able to work with irregular time series without the need for additional data augmentation. The experiments conducted showed that Prophet provides the best balance between forecast accuracy and computational efficiency. The choice of the amount of historical data for modeling is crucial, since excessive data increases processing time, and insufficient data reduces forecast accuracy.The results of the study indicate that none of the considered libraries is a universal solution for all tasks.However, Prophet showed the greatest potential for detecting anomalies in the time series of Telegram metrics, which makes it the best candidate for further development and adaptation in media content monitoring tasks.

This paper applies an integrated methodology to combine different components of intellectual capital. We focus on specific metrics of social network analysis that provide an overview of the social capital growth in the context of clinical care teams working with chronically ill children. The use of an integrated method to combine social network metrics with tangible and intangible indicators has a great potential to complement the view provided by traditional strategic scorecards. The ultimate goal of our research is to build a framework for integrating social network indicators with intellectual capital metrics.

Dolphins restructure social system after reduction of commercial fisheries

Social network analysis (SNA) is widely used to analyze social interactions in asynchronous discussions. A well established model that represents a clear picture of the knowledge-building processes occurring in online discussions is the Community of Inquiry. SNA has recently emerged as an analytical tool for exploring the development of a Community of Inquiry in online learning. However, the application of SNA has been limited and lacks appropriate pedagogical grounding. In this paper, we investigate a) the relationship between social networks and community learning as well as b) the impact of gamification, based on social network metrics, on community learning. According to the results, strong relationships have been revealed mainly between SNA metrics and the social presence, as well as the exploration phase of the cognitive presence of a Community of Inquiry. The evidence also highlights the potential of gamification for empowering the evolution of a Community of Inquiry.

While privacy preservation of data mining approaches has been an important topic for a number of years, privacy of social network data is a relatively new area of interest. Previous research has shown that anonymization alone may not be sufficient for hiding identity information on certain real world data sets. In this paper, we focus on understanding the impact of network topology and node substructure on the level of anonymity present in the network. We present a new measure, topological anonymity, that quantifies the amount of privacy preserved in different topological structures. The measure uses a combination of known social network metrics and attempts to identify when node and edge inference breeches arise in these graphs.

Funding Acknowledgements Type of funding sources: None. Purpose. To study the possibility of conducting the ‘Primary and Secondary Prevention of Cardiovascular Diseases’ educational programme aimed at promoting health literacy among cardiology blog followers on Instagram. Material and methods. In the present study the methodology for conducting the educational programme on Instagram included the following: choosing the agents of influence and format of audience engagement; determining the most relevant topics and time frames; identifying the frequency and characteristics depending on the selected format of audience engagement; developing evaluation metrics; conducting the programme; evaluation of study results; defining the scaling strategy. The educational programme was conducted daily from 19 to 30March 2020 through professional medical accounts on Instagram. The programme format included posts of up to 4,000 characters on primary (healthy diet, smoking cessation, physical activity, obesity, vegetarian diet) and secondary prevention (drug treatment) of cardiovascular diseases. The study results were evaluated by descriptive statistics for standard social network metrics. Results. The total number of cardiology blog followers is 367,727. The audience of professional doctors" blogs is mostly female (89 to 95%), the age group of 25 to 34 years accounts for 40 to 47% of the followers. Ten posts of the educational programme were viewed by a total of 104,794 users, gathering a total of 9,692 audience engagements (comments, shares, likes and saves). Such topics as consumption of vegetables, salt and sugar, physical activity and obesity accounted for the highest audience engagement (1,146; 1,100; 2,195; 1,052 and 1,534 users, respectively). Conclusions. The Instagram social network can be used for educational programmes aimed at promoting health literacy among Russian people. It is necessary to further improve the methodology for conducting research in social networks in order to select the most effective technologies that contribute to the preservation of public health.

This paper proposes an alternate ranking system based on social network metrics and their evaluation in a composite distributed framework. The most important aspect of social network domain is voluminous data. In order to know key trends, predictive analysis of huge sets of raw data can be effective. Again these analytics are generally very compute-intensive. The most efficient solution of such compute-intensive analysis is to map such problems in a distributed domain. The main contributions of this paper are twofold. First, the analysis of cricket community from the viewpoint of social network and finding ranking of players and countries based on the properties of graph centrality measures. Second, the paper proposes a comprehensive distributed framework to offer infrastructural support for the large data analysis as well as graph processing. Hadoop is an open-source framework to store and process large data sets in a cloud environment. MapReduce is a popular programming model for processing that data in a distributed manner. However, MapReduce alone is not efficient for graph processing. Giraph is an alternative programming paradigm for graph processing in Hadoop. Using a practical case study of social network analysis of cricket community, this paper captures the significance of the alternate ranking in this sport, as well as shows effectiveness of the proposed framework in the process of analyzing voluminous data.