Important Role In Social Networks Research Articles

Hiding ourselves from community detection through genetic algorithms

Community structure plays an important role in social networks, which can reveal potential social relationships and deliver vast economic benefits to enterprises and organizations. Many efficient community detection algorithms have been proposed by researchers. However, effective community detection algorithms are accompanied by a growing problem of privacy disclosure. People have started to worry that their private information will be overexposed by community detection algorithms, so determining how to hide the community structure in the network to resist community detection algorithms has become an important issues. In view of this, we develop effective strategies to attack community detection algorithms through invisible disturbances to the network, namely, adding and removing a small number of connections, thus achieving privacy protection. In particular, a hiding strategy named “community hiding based on genetic algorithms using NMI (CGN)” is proposed in this paper. The algorithm uses normalized mutual information (NMI) as the fitness function and achieves an efficient global hiding effect by introducing a gene pool with prior information. We launched attacks based on CGN against four community detection algorithms on multiple real-world networks. By comparing with several state-of-the-art baseline algorithms, our CGN achieved the optimal results in NMI reduction. By visualizing the attack effect, it is proven that our CGN can achieve the community division error of nodes irrelevant to the connection changes by changing a very small number of connections, which fully reflects the concealment of community hiding. In addition, we further test the transferability and find that the modified network obtained by CGN on a specific community detection algorithm also shows extraordinary hiding effects when extended to other algorithms.

ВПЛИВ СОЦІАЛЬНИХ МЕРЕЖ НА РОЗВИТОК ФІЗИЧНОЇ КУЛЬТУРИ І СПОРТУ

There are contradictions between the modern deve-lopment of social networks and the lack of a system of knowledge about the influence of networks on physical activity and sports mana-gement. There is an insufficient level of preparedness of specialists in the field of sports to participate in professional social networks. Objective:determining the impact of social networks in the development of physical culture, physical rehabilitation and sports. Material & Methods: analysis of scientific sources and questionnaire. Students of the Black Sea National University named after Petro Mohyla took part in the survey (n = 91). Conclusions: It was found that students of the Faculty of Physical Education and Sports (n=35) spend from 10 to 90% of the total viewing time on social networks. According to 80% of students (specialty 017 “Physical Culture and Sports”) and 92 % (specialty 227 “Physical therapy, occupational therapy”) the most popular social network is YouTube. YouTube for professional purposes is also viewed by 96.4 % of 1st year students (n=56). 8.6 % of the students of the Faculty of Physical Education and Sports noted that they were not familiar with LinkedIn as the social professional network at all. The majority of students (74.3 %) would approve of the introduction of a course on using LinkedIn, believing that this will help them improve their skills. In a survey of students studying in the specialty 017 “Physical Culture and Sports”, it was found that 28 % of respondents believe that social networks can positively affect the development of extreme sports. According to 40 % of students, it is possible to improve the level of qualification in the field of sports. The possibility of using networks as a scientific method was noted by 68% of respondents. It should be noted that the majority of faculty students (80 %) note the important role of social networks in increasing the level of motivation for sports. Results: 1) The attention of young people in social networks to extreme sports leads to an increase in competition and the development of these types. 2) Scientists have noted that participation in social networks contributes to an increase in the level of motivation for systematic physical activity, sports, although some research results state the opposite, which requires further research. 3) The influence of social networks on the improvement of professional training of specialists in the field of sports was determined; it is recommended to conduct courses on using the professional network LinkedIn. 4) The analysis of social networks is becoming a modern way of scientific research (access, use by networks). 5) The use of social networks can stimulate the development of sports management, allows you to increase the level of popularization of sports facilities and the effectiveness of their management

Examining online social behavior changes after migration: An empirical study based on OSN big data

With fast urbanization and decreasing transportation cost, migration becomes more common. Previous studies have shown the important role of social networks in the process of migration, but little is known about the effect of migration on social networks. To fill the research gap, this study examines the effect of migration on online social behaviors (in terms of network evolution and social interaction), as well as the moderating effect of migrants' characteristics. We collect a four-month big dataset with 2.29 million records from one of the largest online social networks in China. We apply the propensity score matching combined with the difference-in-differences method to compare online social behavior changes after migration. Our results show that, for network evolution behavior, migration positively impacts on the number of tie formation, but non-significantly impacts on the number of tie decay; for social interaction behavior, migration increases the number of contacts but decreases the number of messages. We also find some moderating effects of migrants’ characteristics, including gender, age, and degree. This study provides big data empirical evidence and some new insights to our understandings of the impact of migration on online social network behavior.

Social network research hotspots and trends in public health: A bibliometric and visual analysis

ObjectivesTo understand the research landscape and identify the research hotspots and trends of the application of social network theory and analysis to public health. Study designA bibliometric study of publications regarding application of social network theory and analysis to public health. MethodsChoosing 1607 articles about the application of social network theory and analysis to public health from the core collection database of Web of Science published from 1991 to 2020 as the research sample. A bibliometric and visual analysis of publication quantity and content was performed to analyze time trends, spatial distribution, cooperation networks, influential references, and keyword co-occurrence, clusters, and emergence. ResultsThere is an increasing trend in the use of social network theory and analysis in the public health field, with the United States taking the lead. Research focuses include on transmission of diseases or behavior through social networks and the influence of social networks on population health at different ages. Current research frontiers primarily include the role of social networks in tracking of emerging infectious diseases like COVID-19, preventing and controlling chronic diseases, and carrying out healthy behavioral interventions. ConclusionsThis study provides a comprehensive quantitative overview of the historic development of and latest topics in the application of social network theory and analysis method to public health. More attention should be paid to the important role of social networks in tracing the emergence of serious infectious diseases like COVID-19, as well as preventing and controlling chronic diseases and intervening in health behaviors, considering the increasing challenges and opportunities presented by online social networking.

Interpersonal relationships and creativity at work: A network building perspective

AbstractThe important role of social networks in creativity has long been recognized in the literature. However, research to date has primarily focused on the structural, informational, and relational dimensions of social networks in creativity. Acknowledging the importance of employees themselves, past research has also called for further research to study the human side of social networks in addition to their structural, informational, and relational dimensions. Addressing this research need, we studied the indirect relationship between employees’ tertius iungens orientation (i.e., their strategic, purposeful, and effortful behavior to either connect otherwise disconnected coworkers in their organization or facilitate preexisting relationships among them) and their creativity through their interpersonal relationships with their coworkers. In addition, we studied how this indirect relationship is moderated by the expectations of both the focal employees and their coworkers. Based on a multi‐source and multi‐wave survey study in one of the major Internet companies in the world, our results showed that employees with a tertius iungens orientation enjoyed high‐quality interpersonal relationships with their coworkers and in turn exhibited high levels of creativity. The results also indicated that while employees’ job aspirations positively moderated the relationship between their tertius iungens orientation and interpersonal relationships, their organizational status positively moderated the relationship between their interpersonal relationships and creativity. These results expand extant research in the area by showing how and under what conditions employees’ creativity is influenced by their strategic, purposeful, and effortful behaviors to take advantage of the benefits of social networks. Considering firms’ interest and efforts to increase employee creativity, our study also suggests that firms can increase employee creativity by hiring people with a strong tertius iungens orientation and training their existing employees about the value of such an orientation.

An optimized deep belief network to detect anomalous behavior in social media

The rapid development of online social networks such as Reddit, Twitter, Facebook, and so on are widely expanded. The anomaly detection plays an important role in social networks due to different kinds of malicious activities involving misleading crowdsourcing, contact lists, and spamming results with fake accounts. In this paper, Deep Belief Neural based Interactive Autodidactic School (DBN-IAS) algorithm is proposed to detect anomalies from social networks. Particularly, the novel IAS algorithm is used for optimization of optimal hidden layers from DBN. The experimental details are obtained from both benchmark and real-time datasets. Experimentally, the DBN-IAS approach is compared with other existing methods such as GCN, RBM + SVM with SDN, and LAD. Finally, the DBN-IAS algorithm demonstrated optimal anomaly detection results from social networks and the proposed algorithm shows the detection rate of 99.32%, which is far better than the existing approaches.

Social Network Analysis for Coronavirus (COVID-19) in the United States.

ObjectivesThis study explores how public key players play an important role in social networks for coronavirus (COVID‐19).MethodsThis study employs social network analyses based on 2,864 Twitter users and 2,775 communications of Twitter.ResultsThis study finds that President Trump plays the most important role in social networks among the top 20 key players for both in‐degree centrality and content in tweets. Second, Donald Trump and Barak Obama show the opposite result for the in‐degree centrality and follower analysis. The result shows that the topic‐based networks and the person‐based networks play a different role in social networks. This study demonstrates that the presidents, the World Health Organization (WHO) and its regional offices, the Centers for Disease Control, and news channels play a crucial role in the news of COVID‐19 for people. Key players, such as Donald Trump, Barack Obama, and BBC, are located in the central networks. In contrast, U.S. news channels and WHO and its regional offices have independent channels.ConclusionsGovernments should understand the characteristics of public key players to provide information for COVID‐19 in a timely manner.

GA-Based Q-Attack on Community Detection

Community detection plays an important role in social networks, since it can help to naturally divide the network into smaller parts so as to simplify network analysis. However, on the other hand, it arises the concern that individual information may be over-mined, and the concept community deception thus is proposed to protect individual privacy on social networks. Here, we introduce and formalize the problem of community detection attack and develop efficient strategies to attack community detection algorithms by rewiring a small number of connections, leading to individual privacy protection. In particular, we first give two heuristic attack strategies, i.e., Community Detection Attack (CDA) and Degree Based Attack (DBA), as baselines, utilizing the information of detected community structure and node degree, respectively. And then we propose a Genetic Algorithm (GA) based Q-Attack, where the modularity Q is used to design the fitness function. We launch community detection attack based on the above three strategies against three modularity based community detection algorithms on two social networks. By comparison, our Q-Attack method achieves much better attack effects than CDA and DBA, in terms of the larger reduction of both modularity Q and Normalized Mutual Information (NMI). Besides, we find that the adversarial networks obtained by Q-Attack on a specific community detection algorithm can be still effective on others, no matter whether they are modularity based or not, indicating its strong transferability.

A Novel Community Detection Algorithm Based on Local Similarity of Clustering Coefficient in Social Networks

Community structures are integral and independent parts in a network. Community detection plays an important role in social networks for understanding the structure and predicting user behaviors. Many algorithms have been devised for accurate and efficient community detecting, but there are few community detection algorithms using node similarity. In most real-world networks, nodes tend to create tightly knit groups characterized by a relatively high density of ties. The higher the clustering coefficient of a node, the more aggregative the neighboring nodes are. In this paper, we propose an adjacent node similarity optimization combination connectivity algorithm (ASOCCA) for accurate community detection. ASOCCA utilizes the local similarity measure based on clustering coefficient to identify the closest neighbors of each node, then obtains several sets of connected components by combining different pairs of nodes, and finally forms initial communities. In addition, the community merging strategy is applied to further optimize the community structure. To evaluate the performance of the proposed algorithm, six real-world networks and two LFR networks with diverse network size are used to compare ASOCCA with five state-of-the-art community detection algorithms. The experimental results show that ASOCCA achieves better detection accuracy than several existing algorithms.

Maximizing profit of multiple adoptions in social networks with a martingale approach

Information propagation plays an important role in social network, which helps shaping consumer’s purchasing decisions. Most of existing works focus on maximizing the influence of one product. But in our reality life, the majority of the companies produce various products for meeting customer needs. So it is important to learn about how to distribute the limited budget to maximize the companies profits. In this paper, we use the martingale technique to handle the Profit Maximization with Multiple Adoptions ( $$PM^{2}A$$ ) problem, which aims to identify a seed set for each product with overall activation cost at most B such that the expected total profit is maximized. We design a $$PM^{2}AM$$ algorithm which returns a $$(\frac{1}{2}(1-\frac{1}{e^{2}})-\varepsilon )$$ -approximate solution and runs in $$O\left( (k^{*}+\ell )(m+n)nqp_{max}\ln nq/ (\varepsilon ^{2}\cdot p_{min})\right) $$ expected time.

Application of Fuzzy Analytical Hierarchy Process based on Geometric Mean Method to Prioritize Social Capital Network Indicators

Vietnam is striving to develop dynamically and overcome many human resource challenges. As the economy expands, the demand for jobs and human resource development has become increasingly critical. The pressures from reform and international integration are forcing many changes. New graduates must provide proof of their academic capabilities, while also actively developing their social capital to support their job search process. In fact, social capital is an essential capital for personal development as well as professional development of new graduates. This paper applies Fuzzy Analytical Hierarchy Process based on Geometric Mean Methodology to evaluate factors for measuring the social capital of graduates at Ho Chi Minh City Open University in Vietnam. The research results highlight the important role of social networks for graduates, in which a linking network is the most important, a bonding network is the second most important, and a bridging network is the third most important. In addition, the research shows that trust plays an even more important role than networks; and specific belief is more important than general belief.

Measuring Social Connectedness

We introduce a new measure of social connectedness between U.S. county-pairs, as well as between U.S. counties and foreign countries. Our measure, which we call the Connectedness Index (SCI), is based on the number of friendship links on Facebook, the world's largest online social networking service. Within the U.S., social connectedness is strongly decreasing in geographic distance between counties: for the population of the average county, 62.8% of friends live within 100 miles. The populations of counties with more geographically dispersed social networks are generally richer, more educated, and have a higher life expectancy. Region-pairs that are more socially connected have higher trade flows, even after controlling for geographic distance and the similarity of regions along other economic and demographic measures. Higher social connectedness is also associated with more cross-county migration and patent citations. Social connectedness between U.S. counties and foreign countries is correlated with past migration patterns, with social connectedness decaying in the time since the primary migration wave from that country. Trade with foreign countries is also strongly related to social connectedness. These results suggest that the SCI captures an important role of social networks in facilitating both economic and social interactions. Our findings also highlight the potential for the SCI to mitigate the measurement challenges that pervade empirical research on the role of social interactions across the social sciences.

Towards fast and lightweight spam account detection in mobile social networks through fog computing

Now, mobile devices play an increasingly important role in social networks by sharing information quickly, such as mobile phones and wearable health surveillance devices. Mobile social networks are vulnerable to spammers because of the fragile security policies of mobile operating systems. Especially, social networks on mobile devices face many difficulties in defending against spammers due to their low computing power, poor network quality and long response time. Since graph-based algorithms require huge computing power, machine-learning classifiers require very short response time, and existing PC-based research is not suitable for mobile devices, we need a lightweight and fast response method for mobile devices to detect spammers in mobile social networks. Regarded as the extension of cloud computing, fog computing puts the data, data processing and applications in the devices that are at the edge of the Internet (without storing all of them in the cloud), which leads to a better real-time performance, adapts to the wide geographical distribution and the high mobility of mobile devices. In this paper, we propose COLOR + , a method based on fog computing that performs most computations at terminal (mobile devices). It only uses the interaction between the account and its neighbors, which makes it easy to store and calculate a local graph on a mobile device. Each interaction value can be applied to any request. COLOR + detects spammers based on a threshold of the suspicion degree. We collect 50 million normal accounts and about 40,000 spammers from Twitter. Experiments show that the accuracy of COLOR + is about 85.95%, whose average time to detect an account is 0.01s. Therefore, COLOR + is an effective detection method that can be quickly applied.

Trust evaluation between users of social networks using the quality of service requirements and call log histories

Purpose As social networking services are popular, the need for recognizing reliable people has become the main concern. Quantifying the relationships between the users of social networks is also one of the important challenges with the increasing number of users. So, trust plays an important role in social networks in order to recognize trustworthy people. The purpose of this paper is to propose an approach to recognize trustworthy users and protect users from abuse by untrustable users. Design/methodology/approach In this paper, the authors suggest a method to measure the trust value between users of social networks based on call log histories such as abundance, novelty, and sincerity and quality of service (QoS) requirements such as accessibility, response ability, tend to respond, and agility. After that, the authors calculate error-hit, precision, and recall to evaluate the trust value. Findings The results indicate that the proposed approach has better performance in terms of error-hit, precision, and recall than FIFO, combined, QoS-based, and call log-based method. Originality/value In this paper, the trust issue in social networks is pointed out and a new approach to evaluate the trust value is proposed based on call log histories and QoS requirements.

A Greedy Algorithm Approach for Mobile Social Network

With the proliferation of mobile devices and wireless technologies, mobile social network systems used more. A mobile social network has important role in social network. The Process of finding influential nodes is NP-hard. Greedy rule with demonstrable approximation guarantees will provide smart approximation. A divide-and-conquer method with parallel computing mechanism has been used. Communitybased Greedy rule for mining top-K influential nodes is used first. It has two parts: dividing the largescale mobile social network into many communities by taking under consideration data diffusion. Communities select influential nodes by a dynamic programming. Performance is to be increased by considering the influence propagation supported communities and take into account the influence propagation crossing communities. Experiments on real large-scale mobile social networks show that the proposed algorithm is quicker than previous algorithms. General Terms Mobile social network, influence maximization, PCA greedy algorithm,

A collaborative learning approach for geographic information retrieval based on social networks

Nowadays, spatial and temporal data play an important role in social networks. These data are distributed and dispersed in several heterogeneous data sources. These peculiarities make that geographic information retrieval being a non-trivial task, considering that the spatial data are often unstructured and built by different collaborative communities from social networks. The problem arises when user queries are performed with different levels of semantic granularity. This fact is very typical in social communities, where users have different levels of expertise. In this paper, a novelty approach based on three matching-query layers driven by ontologies on the heterogeneous data sources is presented. A technique of query contextualization is proposed for addressing to available heterogeneous data sources including social networks. It consists of contextualizing a query in which whether a data source does not contain a relevant result, other sources either provide an answer or in the best case, each one adds a relevant answer to the set of results. This approach is a collaborative learning system based on experience level of users in different domains. The retrieval process is achieved from three domains: temporal, geographical and social, which are involved in the user-content context. The work is oriented towards defining a GIScience collaborative learning for geographic information retrieval, using social networks, web and geodatabases.

Finding overlapping communities in a complex network of social linkages and Internet of things

Online social networks play an important role in today's Internet. These social networks contain huge amounts of data and the integrated framework of SN with Internet of things (IoT) presents a challenging problem. IoT is the ubiquitous interconnection of everyday items of interest (things), providing connectivity anytime, anywhere, and with anything. Like biological, co-authorship, and virus-spread networks, IoT and Social Network (SN) can be characterized to be complex networks containing substantial useful information. In the past few years, community detection in graphs has been an active area of research (Lee and Won in Proceedings of IEEE SoutheastCon, pp. 1---5, 2012). Many graph mining algorithms have been proposed, but none of them can help in capturing an important dimension of SNs, which is friendship. A friend circle expands with the help of mutual friends, and, thus, mutual friends play an important role in social networks' growth. We propose two graph clustering algorithms: one for undirected graphs such as Facebook and Google+, and the other for directed graphs such as Twitter. The algorithms extract communities, and based on the access control policy nodes share resources (things). In the proposed Community Detection in Integrated IoT and SN (CDIISN) algorithm, we divide the nodes/actors of complex networks into basic, and IoT nodes. We, then, execute the community detection algorithm on them. We take nodes of a graph as members of a SN, and edges depicting the relations between the nodes. The CDIISN algorithm is purely deterministic, and no fuzzy communities are formed. It is known that one community detection algorithm is not suitable for all types of networks. For different network structures, different algorithms exhibit different results, and methods of execution. However, in our proposed method, the community detection algorithm can be modified as desired by a user based on the network connections. The proposed community detection approach is unique in the sense that a user can define his community detection criteria based on the kind of network.

Immigration, Culture, and Ethnicity in Transformative Consumer Research

Immigration, culture, and ethnicity (IC&E) research has a lengthy history in consumer research, though most research focuses narrowly on identity (and related topics) and has been done at the individual level of analysis. First, the authors discuss the need for research focused on assessing well-being at the collective level and highlight the important role of social networks and communities in improving consumer well-being and creating effective policy interventions. Next, they explore the utility of the emerging intersectionality conceptual framework for research on well-being and IC&E. They offer specific suggestions for designing policy-oriented research using this approach and illustrate the process by taking a well-regarded IC&E study and reimagining its design using a process-centered approach to intersectionality.

Delivering Justice: The Changing Gendered Dynamics of Land Tenure in Botswana

This article argues for the important role of social networks in legal pluralism contexts, and in doing so for the importance of moving beyond abstract assertions about law and legal processes. It is based on an in-depth study (2009–2010) of women’s access to and control over land in Botswana, within a legal pluralist context where the state is highly heterogeneous and where customary and statutory law administer land through varying institutions that derive their power from a constellation of sources. The article shows that a much higher proportion of women now gain formal customary land titles and certificates than was the case 25 years ago. This overall reflects that access to land is no longer influenced as much by gender as by “class”. The latter represents a constellation of factors that contribute to the accumulation of human, social as well as economic capital. While improvements in women’s access to land are partly owed to changes in land law, the role of Land Boards, and the influence of human rights NGOs, access to land is strongly influenced by social networks and informal transactions. What is crucial are the factors that contribute to the social networks that individuals can draw on, which have an impact on their capacity to negotiate poverty and access to justice in both formal and informal domains. Legal pluralism within this context reflects mutually constitutive processes that draw on and intersect with the social dimensions of everyday life. These insights ought to have an important bearing on the strategies of international development agencies’ focus on the linkages between poverty reduction, access to justice and property rights.

Changement technique et émergence de nouveaux entrepreneurs aquacoles: l'introduction de la crevette dans les systèmes piscicoles de la province de Pampanga (Philippines)

In the Philippines, the coastal area of the province of Pampanga is well known for its aquaculture potential. Development of the first brackish water fish ponds for milkfish (Chanos chanos) culture dates back to the beginning of the XXth century. However, the main expansion of this sector began in 1980 with the introduction of shrimp (Panaeus monodon), a product of high commercial value. Although shrimp farming and the associated technical changes offer new opportunities to the local population, access to production means such as land and financial capital remains rather difficult with regards to land tenure disparities which are characteristic for the country. Through a socio-anthropological study of aquaculture firms in the municipality of Sasmuan, this text analyzes the modalities of integration of entrepreneurs into the local aquaculture. It shows the important role of social networks and interpersonal relations built by entrepreneurs in the access to production factors such as land and financial capital. The empirical analysis shows that the principle of reciprocity which cements exchanges within these social networks coexists with the trade principle and makes it possible to surmount the difficulties related to socioeconomic disparities while facilitating the integration of entrepreneurs into the aquaculture sector.