Link Prediction in Social Networks

2018 ◽  
pp. 164-172 ◽  
Author(s):  
Sovan Samanta ◽  
Madhumangal Pal
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Link Prediction ◽  
Research Work ◽  
Dense Networks ◽  
Friend Recommendation ◽  
Neighborhood Method

Social network is a topic of current research. Relations are broken and new relations are increased. This chapter will discuss the scope or predictions of new links in social networks. Here different approaches for link predictions are described. Among them friend recommendation model is latest. There are some other methods like common neighborhood method which is also analyzed here. The comparison among them to predict links in social networks is described. The significance of this research work is to find strong dense networks in future.

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.

Similarity-Based Indices or Metrics for Link Prediction

2019 ◽  
pp. 1-29
Author(s):  
Praveen Kumar Bhanodia ◽  
Kamal Kumar Sethi ◽  
Aditya Khamparia ◽  
Babita Pandey ◽  
Shaligram Prajapat
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Link Prediction ◽  
Online Social Network ◽  
Dynamic Networks ◽  
Relevant Information ◽  
Network Evolution ◽  
Problem Instance ◽  
The Social ◽  
Social Network Evolution

Link prediction in social network has gained momentum with the inception of machine learning. The social networks are evolving into smart dynamic networks possessing various relevant information about the user. The relationship between users can be approximated by evaluation of similarity between the users. Online social network (OSN) refers to the formulation of association (relationship/links) between users known as nodes. Evolution of OSNs such as Facebook, Twitter, Hi-Fi, LinkedIn has provided a momentum to the growth of such social networks, whereby millions of users are joining it. The online social network evolution has motivated scientists and researchers to analyze the data and information of OSN in order to recommend the future friends. Link prediction is a problem instance of such recommendation systems. Link prediction is basically a phenomenon through which potential links between nodes are identified on a network over the period of time. In this chapter, the authors describe the similarity metrics that further would be instrumental in recognition of future links between nodes.

EMPIRICAL ANALYSIS OF ATTENTION BEHAVIORS IN ONLINE SOCIAL NETWORKS

2010 ◽  
Vol 21 (07) ◽  
pp. 955-971 ◽  
Author(s):  
FANG DU ◽  
QI XUAN ◽  
TIE-JUN WU
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Metric Space ◽  
Online Social Networks ◽  
Link Prediction ◽  
Network Models ◽  
Friendship Network ◽  
Social Significance ◽  
Near Future ◽  
Type Of Behavior

Studying attention behavior has its social significance because such behavior is considered to lead the evolution of the friendship network. However, this type of behavior in social networks has attracted relatively little attention before, which is mainly because, in reality, such behaviors are always transitory and rarely recorded. In this paper, we collected the attention behaviors as well as the friendship network from Douban database and then carefully studied the attention behaviors in the friendship network as a latent metric space. The revealed similar patterns of attention behavior and friendship suggest that attention behavior may be the pre-stage of friendship to a certain extent, which can be further validated by the fact that pairwise nodes in Douban network connected by attention links beforehand are indeed far more likely to be connected by friendship links in the near future. This phenomenon can also be used to explain the high clustering of many social networks. More interestingly, it seems that attention behaviors are more likely to take place between individuals who have more mutual friends as well as more different friends, which seems a little different from the principles of many link prediction algorithms. Moreover, it is also found that forward attention is preferred to inverse attention, which is quite natural because, usually, an individual must be more interested in others that he is paying attention to than those paying attention to him. All of these findings can be used to guide the design of more appropriate social network models in the future.

scholarly journals Engagement and Performance Studies of Media Agencies Publications on Social Networks

2020 ◽  
Vol 8 (3) ◽  
pp. 4
Author(s):  
Mohcine Kodad
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Performance Studies ◽  
Research Work ◽  
Future Research ◽  
The Social ◽  
The World ◽  
And Performance ◽  
Interaction Strategy ◽  
Facebook Pages

This paper presents a study that contributes to the existing work on the social diffusion and interaction strategy in social media. The aim is to know the most shared post by some electronic media in the world from end to end social network, and also to know post nature of the most successful one, and the link between different kind of interaction these are main objectives of this study. Our work is also considered as a ground and a base for social network analysis researchers in all social networks in order to allow them to benefit and help in their future research work from all information collected and results found via this study. An empirical analysis using multiple methods is conducted based on 275 Facebook publications gathered from the Facebook pages of 5 electronics journals the best one in its original country represented 5 countries in the world. This contribution discovered a set of important information and it is also projected to confirm hypothesis addressed in pre-existing studies

scholarly journals Trust-Based Missing Link Prediction in Signed Social Networks with Privacy Preservation

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Huaizhen Kou ◽  
Fan Wang ◽  
Chao Lv ◽  
Zhaoan Dong ◽  
Wanli Huang ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Privacy Protection ◽  
Link Prediction ◽  
Social Relationship ◽  
Hamming Distance ◽  
Mobile Internet ◽  
User Privacy ◽  
Missing Link ◽  
Signed Social Networks

With the development of mobile Internet, more and more individuals and institutions tend to express their views on certain things (such as software and music) on social platforms. In some online social network services, users are allowed to label users with similar interests as “trust” to get the information they want and use “distrust” to label users with opposite interests to avoid browsing content they do not want to see. The networks containing such trust relationships and distrust relationships are named signed social networks (SSNs), and some real-world complex systems can be also modeled with signed networks. However, the sparse social relationships seriously hinder the expansion of users’ social circle in social networks. In order to solve this problem, researchers have done a lot of research on link prediction. Although these studies have been proved to be effective in the unsigned social network, the prediction of trust and distrust in SSN has not achieved good results. In addition, the existing link prediction research does not consider the needs of user privacy protection, so most of them do not add privacy protection measures. To solve these problems, we propose a trust-based missing link prediction method (TMLP). First, we use the simhash method to create a hash index for each user. Then, we calculate the Hamming distance between the two users to determine whether they can establish a new social relationship. Finally, we use the fuzzy computing model to determine the type of their new social relationship (e.g., trust or distrust). In the paper, we gradually explain our method through a case study and prove our method’s feasibility.

An analysis of students’ response to changes in financial situation during the pandemic by digital traces in the VKontakte social network

2021 ◽  
pp. 54-64
Author(s):  
Е.В. РОМАНОВА ◽  
Т.Ю. КАЛАВРИЙ
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Social Network ◽  
Research Work ◽  
Low Frequency ◽  
Educational Services ◽  
Financial Situation ◽  
Negative Tone ◽  
The Cost ◽  
Additional Income

В статье представлены результаты исследования круга проблем и вопросов финансового положения студентов в период пандемии по цифровым следам в социальных сети ВКонтакте студенческой аудитории. Для анализа были использованы тексты постов в сообществах студентов и комментарии к ним за период второго учебного года в условиях пандемии. Для оценки контента обсуждаемых тем и вопросов в сообществах использованы результаты НИР Консорциума исследователей больших данных «Образование в условиях коронавируса: большие данные как инструмент измерения реакции общества» за февраль – июнь 2020 г. Подготовка данных для анализа включала выгрузку данных из социальных сетей, отбор релевантных сообщений, выявление категорий и тематических сюжетов, определение тональности сообщений. По предварительно выгруженным с использованием специализированного программного обеспечения (Polyanalyst, библиотек машинного обучения) сообщениям сообществ вузов региона была проведена разметка всех сообщений по релевантности изучаемой темы, что позволило в дальнейшем систематизировать сообщения по тематике и тональности. Контент анализируемых релевантных сообщений позволили выделить четыре основные тематические категории такие, как стипендия и материальная помощь, стоимость образовательных и дополнительных услуг, возможность получения дополнительных доходов, разное. В группе сообщений по вопросам стипендии и материальной помощи были выделены тематические сюжеты о размере и сроках выплаты стипендии и материальной помощи и о процедуре начисления стипендии. В группе сообщений по вопросам стоимость основных и дополнительных услуг были выделены тематические сюжеты о цене-качестве образовательных услуг, возврата стоимости за обучение в условиях дистанционного формата работы, а также ценообразование образовательных услуг. В группе сообщений по возможности получения дополнительных доходов студенты рассуждали преимущественно о размере стипендии в сопоставлении со сложившимися ценами на товары и услуги и о необходимости поиска дополнительных источниках доходов. В группе разное были рассмотрены низкочастотные сообщения по различным тематическим сюжетам. Тональность сообщений, в которых студенты высказывали свое мнение и оценку, преимущественно нейтральная, но негативная тональность доминировала на начало каждого учебного семестра. Полученные результаты исследования на основе выборки данных социальной сети ВКонтакте за анализируемый период могут послужить обоснованием для дальнейшего исследования сообщений в социальных сетях с целью выявления и анализа обратной связи студентов о качестве, эффективности и развитии дистанционного образования в стране, а также мониторинга появления/развития/отмирания проблем и вопросов в сфере финансового состояния студентов. The article presents the results of a survey on the range of problems and questions of students regarding financial situation based on digital traces in the VKontakte social network. The analysis was based on posts in student communities and comments to them during the second academic year during the pandemic. To assess changes in the content of the discussed topics and issues in the communities, the results of research work of the Consortium of Big Data Researchers “Education in the context of coronavirus: big data as a tool for measuring the reaction of society” for February – June 2020 were used. Preparing data for analysis included downloading data from social networks, selecting relevant messages, identifying categories and thematic plots, and determining the sentiment of messages. According to the messages from the communities of the universities in the region, previously unloaded using specialized software (Polyanalyst, machine learning libraries), all messages were marked up according to the relevance of the topic being studied, which made it possible to further systematize messages by topic and tone. The content of the analyzed relevant messages allowed us to single out four main thematic categories such as scholarships and material assistance, the cost of educational and additional services, the possibility of obtaining additional income, and miscellaneous. In the group of presentations on the issues of scholarships and material assistance, thematic stories were highlighted on the amount and timing of payment of the scholarship and material assistance and on the procedure for awarding the scholarship. In the group of messages on the cost of basic and additional services, there were highlighted thematic stories about the price-quality of educational services, the return of the cost of training in a distance format of work, as well as the pricing of educational services. In the group of messages about the possibility of obtaining additional income, students talked mainly about the amount of the scholarship in comparison with the prevailing prices for goods and services and the need to search for additional sources of income. In the Miscellaneous group, low-frequency messages were considered on various thematic topics. The tone of the messages in which the students expressed their opinion and assessment was predominantly neutral, but the negative tone dominated at the beginning of each academic semester. The results of the study based on a sample of data from the VKontakte social network for the analyzed period can serve as a rationale for further research of messages on social networks in order to identify and analyze student feedback on the quality, efficiency and development of distance education in the country, as well as monitor the emergence / development / withering away problems and questions in the field of the financial condition of students.

Social Networks and Employee Creativity

2021 ◽  
Author(s):  
Gamze Koseoglu ◽  
Christina E. Shalley
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Positive Relationship ◽  
Idea Generation ◽  
Weak Ties ◽  
Employee Creativity ◽  
Network Heterogeneity ◽  
Dense Networks ◽  
Sparse Networks

In the field of management, employee creativity, which is defined as the production of novel and useful ideas concerning products, processes, and services, has been found to be necessary for organizational success and survival. An employee’s relationships with others in the organization affect creativity because employees work in the presence of, and with, their coworkers. A social network approach has been taken to understand how employee relationships can affect creativity. Social networks examine the interaction of individuals with those around them, such as asking them for help or advice. Four components of social networks that have a role in employee creativity have received attention: the nature of the employee’s relationships with coworkers, the structure of the employee’s social network, the position of the employee in the organizational network, and the employee’s network heterogeneity. Regarding the nature of relationships, while some researchers have found that weaker ties are more beneficial for employee creativity, other researchers have found that stronger ties are more advantageous. In order to resolve this conflict, researchers examined the role of strong versus weak ties at different stages of the creativity process and found that, while weak ties might be more useful during idea generation, strong ties come into play during idea elaboration. There are also conflicting findings on the role of the structure of social network. Specifically, a group of researchers found support for a positive relationship between sparse networks and employee creativity, and another group found a positive relationship between dense networks and creativity. Some researchers aimed to resolve this debate, and their findings mirrored the findings on tie strength. They found that density affects different stages of the creative process in unique ways, and while sparse networks are more beneficial during idea generation, dense networks become more important during idea implementation. Compared to the previous two components, the role of network position and network heterogeneity has received less attention from researchers. Researchers found that both central and peripheral positions have certain benefits and costs for creativity. For example, on the one hand, employees located at the periphery of an organization can collect nonredundant information from outside of the organization that has not been shared by others in the organization and has a positive influence on creativity. On the other hand, employees at a central location gain benefits from fast and easy access to information based on many contracts and receiving recognition from many others, thereby improving creativity. Finally, researchers consistently found that different types of network heterogeneity, such as the diversity of one’s contacts in terms of functional background, organizational function, or nationality, positively affects employee creativity. There are many opportunities for future research on the relationship between social networks and creativity, such as examining the role of motivational and cognitive processes as mediational mechanisms, focusing on the role of alter characteristics, studying social networks in a team setting, and taking a temporal approach to understand how changes in social networks over time affect employee creativity.

Friend recommendation for healthy weight in social networks

2015 ◽  
Vol 115 (7) ◽  
pp. 1251-1268 ◽  
Author(s):  
Anming Li ◽  
Eric W.T. Ngai ◽  
Junyi Chai
Keyword(s):  
Social Networks ◽  
Weight Loss ◽  
Social Network ◽  
Healthy Lifestyle ◽  
Overweight And Obesity ◽  
Healthy Weight ◽  
Data Set ◽  
Content Type ◽  
Friend Recommendation ◽  
Target User

Purpose – The purpose of this paper is to propose a new approach recommending friends to social networking users who are also using weight loss app in the context of social networks. Design/methodology/approach – Social network has been recognized as an effective way to enhance overweight and obesity interventions in past studies. However, effective measures integrating social network with weight loss are very limited in the healthcare area. To bridge this gap, this study develops a measure for friend recommendation using the data obtained by weight loss apps; designs methods to model weight-gain-related behaviors (WGRB); constructs a novel “behavior network;” and develops two measurements in experiments to examine the proposed approach. Findings – The approach for friend recommendation is based on Friend Recommendation for Health Weight (FRHW) algorithm. By running this algorithm on a real data set, the experiment results show that the algorithm can recommend a friend who has a healthy lifestyle to a target user. The advantages of the proposed mechanism have been well justified via comparisons with popular friend recommenders in past studies. Originality/value – The conventional methods for friend recommenders in social networks are only concerned with similarities of pairs rather than interactions between people. The system cannot account for the potential influences among people. The method pioneers to model a WGRB as recommendation mechanism that allow recommended friends to simultaneously fulfill two criteria. They are: first, similarity to the target person; and second, ensuring the positive influence toward weight loss. The second criterion is obviously important in practice and thus the approach is valuable to the literature.

scholarly journals Missing Link Prediction Using Non-Overlapped Features and Multiple Sources of Social Networks

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 214
Author(s):  
Pokpong Songmuang ◽  
Chainarong Sirisup ◽  
Aroonwan Suebsriwichai
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Link Prediction ◽  
Classification Model ◽  
Density Level ◽  
Missing Link ◽  
Common Features ◽  
The Common

The current methods for missing link prediction in social networks focus on using data from overlapping users from two social network sources to recommend links between unconnected users. To improve prediction of the missing link, this paper presents the use of information from non-overlapping users as additional features in training a prediction model using a machine-learning approach. The proposed features are designed to use together with the common features as extra features to help in tuning up for a better classification model. The social network data sources used in this paper are Twitter and Facebook where Twitter is a main data for prediction and Facebook is a supporting data. For evaluations, a comparison using different machine-learning techniques, feature settings, and different network-density level of data source is studied. The experimental results can be concluded that the prediction model using a combination of the proposed features and the common features with Random Forest technique gained the best efficiency using percentage amount of recovering missing links and F1 score. The model of combined features yields higher percentage of recovering link by an average of 23.25% and the F1-measure by an average of 19.80% than the baseline of multi-social network source.

scholarly journals Intelligent Link Prediction Management Based on Community Discovery and User Behavior Preference in Online Social Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jun Ge ◽  
Lei-lei Shi ◽  
Lu Liu ◽  
Hongwei Shi ◽  
John Panneerselvam
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Network Structure ◽  
Online Social Networks ◽  
Link Prediction ◽  
User Behavior ◽  
Detection Algorithm ◽  
Factors Affecting ◽  
Friend Recommendation ◽  
Dynamic Community Detection

Link prediction in online social networks intends to predict users who are yet to establish their network of friends, with the motivation of offering friend recommendation based on the current network structure and the attributes of nodes. However, many existing link prediction methods do not consider important information such as community characteristics, text information, and growth mechanism. In this paper, we propose an intelligent data management mechanism based on relationship strength according to the characteristics of social networks for achieving a reliable prediction in online social networks. Secondly, by considering the network structure attributes and interest preference of users as important factors affecting the link prediction process in online social networks, we propose further improvements in the prediction process by designing a friend recommendation model with a novel incorporation of the relationship information and interest preference characteristics of users into the community detection algorithm. Finally, extensive experiments conducted on a Twitter dataset demonstrate the effectiveness of our proposed models in both dynamic community detection and link prediction.

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