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.

scholarly journals Social Network Analysis Visualization

2015 ◽  
pp. 257-270
Author(s):  
Mohana Shanmugam ◽  
Yusmadi Yah Jusoh ◽  
Rozi Nor Haizan Nor ◽  
Marzanah A. Jabar
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Social Network ◽  
Future Research ◽  
Research Gaps ◽  
The Social ◽  
Network Studies ◽  
Academic Study

The social network surge has become a mainstream subject of academic study in a myriad of disciplines. This chapter posits the social network literature by highlighting the terminologies of social networks and details the types of tools and methodologies used in prior studies. The list is supplemented by identifying the research gaps for future research of interest to both academics and practitioners. Additionally, the case of Facebook is used to study the elements of a social network analysis. This chapter also highlights past validated models with regards to social networks which are deemed significant for online social network studies. Furthermore, this chapter seeks to enlighten our knowledge on social network analysis and tap into the social network capabilities.

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.

Building Online Social Networks to Engage Female Students in Information Systems

2015 ◽  
Vol 10 (4) ◽  
pp. 33-51
Author(s):  
Jaymeen R. Shah ◽  
Hsun-Ming Lee
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Information Systems ◽  
Online Social Networks ◽  
Social Networking Sites ◽  
Role Models ◽  
Online Social Network ◽  
Research Process ◽  
Female Students ◽  
The Social

During the next decade, enrollment growth in Information Systems (IS) related majors is unlikely to meet the predicted demand for qualified IS graduates. Gender imbalance in the IS related program makes the situation worse as enrollment and retention of women in the IS major has been proportionately low compared to male. In recent years, majority of high school and college students have integrated social networking sites in their daily life and habitually use these sites. Providing female students access to role models via an online social network may enhance their motivation to continue as an IS major and pursue a career in IS field. For this study, the authors follow the action research process – exploration of information systems development. In particular, a Facebook application was developed to build the social network connecting role models and students. Using the application, a basic framework is tested based on the gender of participants. The results suggest that it is necessary to have adequate number of role models accessible to students as female role-models tend to select fewer students to develop relationships with a preference for female students. Female students likely prefer composite role models from a variety of sources. This pilot study yields valuable lessons to provide informal learning fostered by role modeling via online social networks. The Facebook application may be further expanded to enhance female students' interests in IS related careers.

scholarly journals Understanding attribute and social circle correlation in social networks

2019 ◽  
pp. 1228-1242
Author(s):  
PRANAV NERURKAR ◽  
MADHAV CHANDANE ◽  
SUNIL BHIRUD
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Network ◽  
Real Life ◽  
Coarse Grained ◽  
Unified Framework ◽  
Community Discovery ◽  
Social Circle ◽  
The Social ◽  
Circle Formation

Social circles, groups, lists, etc. are functionalities that allow users of online social network (OSN) platforms to manually organize their social media contacts. However, this facility provided by OSNs has not received appreciation from users due to the tedious nature of the task of organizing the ones that are only contacted periodically. In view of the numerous benefits of this functionality, it may be advantageous to investigate measures that lead to enhancements in its efficacy by allowing for automatic creation of customized groups of users (social circles, groups, lists, etc). The field of study for this purpose, i.e. creating coarse-grained descriptions from data, consists of two families of techniques, community discovery and clustering. These approaches are infeasible for the purpose of automation of social circle creation as they fail on social networks. A reason for this failure could be lack of knowledge of the global structure of the social network or the sparsity that exists in data from social networking websites. As individuals do in real life, OSN clients dependably attempt to broaden their groups of contacts in order to fulfill different social demands. This means that ‘homophily’ would exist among OSN users and prove useful in the task of social circle detection. Based on this intuition, the current inquiry is focused on understanding ‘homophily’ and its role in the process of social circle formation. Extensive experiments are performed on egocentric networks (ego is user, alters are friends) extracted from prominent OSNs like Facebook, Twitter, and Google+. The results of these experiments are used to propose a unified framework: feature extraction for social circles discovery (FESC). FESC detects social circles by jointly modeling ego-net topology and attributes of alters. The performance of FESC is compared with standard benchmark frameworks using metrics like edit distance, modularity, and running time to highlight its efficacy.

A Novel Method to Dynamically Fix Threshold for Node Neighbourhood Based Link Prediction Techniques

2020 ◽  
Vol 15 (1) ◽  
pp. 17-34
Author(s):  
Anand Kumar Gupta ◽  
Neetu Sardana
Keyword(s):  
Social Network ◽  
Link Prediction ◽  
Binary Classification ◽  
Online Social Network ◽  
Threshold Value ◽  
Classification Problem ◽  
Similarity Score ◽  
The Social ◽  
Comparative Results ◽  
Prediction Techniques

The objective of an online social network is to amplify the stream of information among the users. This goal can be accomplished by maximizing interconnectivity among users using link prediction techniques. Existing link prediction techniques uses varied heuristics such as similarity score to predict possible connections. Link prediction can be considered a binary classification problem where probable class outcomes are presence and absence of connections. One of the challenges in classification is to decide threshold value. Since the social network is exceptionally dynamic in nature and each user possess different features, it is difficult to choose a static, common threshold which decides whether two non-connected users will form interconnectivity. This article proposes a novel technique, FIXT, that dynamically decides the threshold value for predicting the possibility of new link formation. The article evaluates the performance of FIXT with six baseline techniques. The comparative results depict that FIXT achieves accuracy up to 93% and outperforms baseline techniques.

scholarly journals Detecting the Influencer on Social Networks Using Passion Point and Measures of Information Propagation †

2020 ◽  
Vol 12 (7) ◽  
pp. 3064 ◽  
Author(s):  
Tai Huynh ◽  
Hien Nguyen ◽  
Ivan Zelinka ◽  
Dac Dinh ◽  
Xuan Hau Pham
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Marketing Strategy ◽  
Online Social Network ◽  
Modern Method ◽  
Information Propagation ◽  
The Social ◽  
User Data ◽  
The Impact ◽  
Positive Results

Influencer marketing is a modern method that uses influential users to approach goal customers easily and quickly. An online social network is a useful platform to detect the most effective influencer for a brand. Thus, we have an issue: how can we extract user data to determine an influencer? In this paper, a model for representing a social network based on users, tags, and the relationships among them, called the SNet model, is presented. A graph-based approach for computing the impact of users and the speed of information propagation, and measuring the favorite brand of a user and sharing the similar brand characteristics, called a passion point, is proposed. Therefore, we consider two main influential measures, including the extent of the influence on other people by the relationships between users and the concern to user’s tags, and the tag propagation through social pulse on the social network. Based on these, the problem of determining the influencer of a specific brand on a social network is solved. The results of this method are used to run the influencer marketing strategy in practice and have obtained positive results.

Analysis of Social Network Mutate in Start-Up Enterprises Evolution

2011 ◽  
Vol 181-182 ◽  
pp. 9-13
Author(s):  
Min Wang ◽  
Hua Tao Peng
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Environmental Changes ◽  
Network Evolution ◽  
Reverse Direction ◽  
Start Up ◽  
Mutation Theory ◽  
Basic Concepts ◽  
Definition Of ◽  
Social Network Evolution

In the process of evolution in Start-up Enterprises, its social networks will often occurred Mutations phenomenon because of environmental changes, the changing conditions, the reverse direction, management of error, etc. This paper analyzed the evolution in social network of Start-up Enterprises through the basic concepts of the theory of mutations, definition of mutations in Start-up Enterprises′ social network evolution, and the using of swallow-tail mutation theory; proposed countermeasures of how to make full use of social network to facilitate Start-up Enterprises timely and accurately identify mutations, take measures, reduce the loss which mutations brought.

Link prediction measures considering different neighbors’ effects and application in social networks

2017 ◽  
Vol 28 (03) ◽  
pp. 1750033 ◽  
Author(s):  
Peng Luo ◽  
Chong Wu ◽  
Yongli Li
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Link Prediction ◽  
Prediction Models ◽  
Network Evolution ◽  
Social Characteristics ◽  
Prediction Methods ◽  
Proposed Model ◽  
Social Network Evolution ◽  
Artificial Network

Link prediction measures have been attracted particular attention in the field of mathematical physics. In this paper, we consider the different effects of neighbors in link prediction and focus on four different situations: only consider the individual’s own effects; consider the effects of individual, neighbors and neighbors’ neighbors; consider the effects of individual, neighbors, neighbors’ neighbors, neighbors’ neighbors’ neighbors and neighbors’ neighbors’ neighbors’ neighbors; consider the whole network participants’ effects. Then, according to the four situations, we present our link prediction models which also take the effects of social characteristics into consideration. An artificial network is adopted to illustrate the parameter estimation based on logistic regression. Furthermore, we compare our methods with the some other link prediction methods (LPMs) to examine the validity of our proposed model in online social networks. The results show the superior of our proposed link prediction methods compared with others. In the application part, our models are applied to study the social network evolution and used to recommend friends and cooperators in social networks.

scholarly journals Social Bots As an Instrument of Influence in Social Networks: Typologization Problems

2021 ◽  
Author(s):  
V.V. Vasilkova ◽  
N.I. Legostaeva
Keyword(s):  
Social Networks ◽  
Content Analysis ◽  
Social Network ◽  
Human Behavior ◽  
Online Social Network ◽  
Research Trend ◽  
Political News ◽  
The Social ◽  
Different Types ◽  
New Research

Nowadays, in the field of social bots investigations, we can observe a new research trend — a shift from a technology-centered to sociology-centered interpretations. It leads to the creation of new perspectives for sociology: now the phenomenon of social bots is not only considered as one of the efficient manipulative technologies but has a wider meaning: new communicative technologies have an informational impact on the social networks space. The objective of this research is to assess the new approaches of the established social bots typologies (based on the fields of their usage, objectives, degree of human behavior imitation), and also consider the ambiguity and controversy of the use of such typologies using the example of botnets operating in the VKontakte social network. A method of botnet identification is based on comprehensive methodology developed by the authors which includes the frequency analysis of published messages, botnet profiling, statistical analysis of content, analysis of botnet structural organization, division of content into semantic units, forming content clusters, content analysis inside the clusters, identification of extremes — maximum number of unique texts published by botnets in a particular cluster for a certain period. The methodology was applied for the botnet space investigation of Russian online social network VKontakte in February and October 2018. The survey has fixed that among 10 of the most active performing botnets, three botnets were identified that demonstrate the ambiguity and controversy of their typologization according to the following criteria: botnet “Defrauded shareholders of LenSpetsStroy” — according to the field of their usage, botnet “Political news in Russian and Ukrainian languages” — according to their objectives, botnet “Ksenia Sobchak” — according to the level of human behavior imitation. The authors identified the prospects for sociological analysis of different types of bots in a situation of growing accessibility and routinization of bot technologies used in social networks. Keywords: social bots, botnets, classification, VKontakte social network

Do I Deserve to Spend? Online Social Support and Spending Pleasure

2020 ◽  
pp. 193896552097357
Author(s):  
Kawon Kim ◽  
Melissa A. Baker
Keyword(s):  
Social Support ◽  
Social Networks ◽  
Social Network ◽  
Boundary Conditions ◽  
Online Social Networks ◽  
Service Industry ◽  
Online Social Network ◽  
Online Social Support ◽  
The Social ◽  
Influence Perceptions

Despite evidence of people posting their consumption experiences to online social networks to fulfill the needs of social support, an understanding of how online social support affects post-consumption spending behaviors remains elusive. This research aims to examine how online social support from online social network friends and the firm influence perceptions of self-deservingness and spending pleasure. Across two studies, this research provides evidence that social support gained through online social networks influences consumers’ spending pleasure through perceptions of their own deservingness. Notably, this study reveals that people obtain social support in online social networks from two sources: social networks friends and firms through receiving “Likes” and “Comments” on their post. This study also explores boundary conditions for when online social support is more effective on spending pleasure. The findings not only broaden the social support literature but also address the benefit to the service industry by understanding how social support can enhance spending pleasure.

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