Opinion evolution in society with stubborn agents using Temporal Model for Opinion and Triad Network Formation (TMOTNF)

2021 ◽  
Vol 9 (5) ◽  
Author(s):  
Eeti Jain ◽  
Anurag Singh
Keyword(s):  
Social Network ◽  
Information Diffusion ◽  
Network Formation ◽  
Threshold Value ◽  
Network Information ◽  
The Social ◽  
Convergence Point ◽  
Temporal Modelling ◽  
Temporal Model ◽  
Opinion Evolution

Abstract Information diffusion is an important part of the social network. Information flows between the individuals in the social networks to shape and update their opinions about various topics. The updated opinion values of them further spread the information in the network. The social network is always evolving by nature, leading to the dynamics of the network. Connections keep on changing among the individuals based on the various characteristics of the networks and individuals. Opinions of individuals may again be affected by the changes in the network which leads to dynamics on the network. Therefore, the co-evolving nature of dynamics on/of the network is proposed. Co-evolving Temporal Model for Opinion and Triad Network Formation is modelled to evaluate the opinion convergence. Some fully stubborn agents are chosen in the network to affect opinion evolution, framing society’s opinion. It is also analysed how these agents can divert the whole network towards their opinion values. When temporal modelling is done using all the three conditions, Triadic Closure, Opinion Threshold value and the Page Rank value over the network, the network does not reach consensus at the convergence point. Various individuals with different opinion values still exist.

scholarly journals Influence Maximization in Social Networks using Deterministic Crowding Algorithm

2019 ◽  
Vol 8 (11) ◽  
pp. 4258-4265
Keyword(s):  
Social Network ◽  
Information Diffusion ◽  
Influence Maximization ◽  
Maximization Problem ◽  
Linear Threshold ◽  
The Social ◽  
Spread Of Influence ◽  
Influential Users ◽  
High Influence ◽  
Influence Maximization Problem

In a social network the individuals connected to one another become influenced by one another, while some are more influential than others and able to direct groups of individuals towards a move, an idea and an entity. These individuals are named influential users. Attempt is made by the social network researchers to identify such individuals because by changing their behaviors and ideologies due to communications and the high influence on one another would change many others' behaviors and ideologies in a given community. In information diffusion models, at all stages, individuals are influenced by their neighboring people. These influences and impressions thereof are constructive in an information diffusion process. In the Influence Maximization problem, the goal is to finding a subset of individuals in a social network such that by activating them, the spread of influence is maximized. In this work a new algorithm is presented to identify most influential users under the linear threshold diffusion model. It uses explicit multimodal evolutionary algorithms. Four different datasets are used to evaluate the proposed method. The results show that the precision of our method in average is improved 4.8% compare to best known previous works.

Competence Management over Social Networks Through Dynamic Taxonomies

2013 ◽  
pp. 103-120
Author(s):  
Giuseppe Berio ◽  
Antonio Di Leva ◽  
Mounira Harzallah ◽  
Giovanni M. Sacco
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Real World ◽  
Reference Model ◽  
Effective Solution ◽  
Network Information ◽  
The Social ◽  
Competence Management

The exploitation and integration of social network information in a competence reference model (CRAI, Competence, Resource, Aspect, Individual) are discussed. The Social-CRAI model, which extends CRAI to social networks, provides an effective solution to this problem and is discussed in detail. Finally, dynamic taxonomies, a model supporting explorative conceptual search, are introduced and their use in the context of the Social-CRAI model for exploring retrieved information available in social networks is discussed. A real-world example is provided.

Social Network Mining from the Web

2011 ◽  
pp. 149-175 ◽  
Author(s):  
Yutaka Matsuo ◽  
Junichiro Mori ◽  
Mitsuru Ishizuka
Keyword(s):  
Social Network ◽  
Search Engine ◽  
Relevant Information ◽  
Web Content ◽  
Message Boards ◽  
Network Information ◽  
Network Mining ◽  
Social Network Mining ◽  
The Social ◽  
The Web

This chapter describes social network mining from the Web. Since the end of the 1990s, several attempts have been made to mine social network information from e-mail messages, message boards, Web linkage structure, and Web content. In this chapter, we specifically examine the social network extraction from the Web using a search engine. The Web is a huge source of information about relations among persons. Therefore, we can build a social network by merging the information distributed on the Web. The growth of information on the Web, in addition to the development of a search engine, opens new possibilities to process the vast amounts of relevant information and mine important structures and knowledge.

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 Understanding the Social Networks That Form within the Context of an Obesity Prevention Intervention

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Sabina B. Gesell ◽  
Kimberly D. Bess ◽  
Shari L. Barkin
Keyword(s):  
Social Networks ◽  
Social Network ◽  
School Readiness ◽  
Obesity Prevention ◽  
Network Formation ◽  
Group Level ◽  
The Social ◽  
Similar Body ◽  
Child Bmi ◽  
Social Network Formation

Background. Antiobesity interventions have generally failed. Research now suggests that interventions must be informed by an understanding of the social environment.Objective. To examine if new social networks form between families participating in a group-level pediatric obesity prevention trial.Methods. Latino parent-preschool child dyads (N=79) completed the 3-month trial. The intervention met weekly in consistent groups to practice healthy lifestyles. The control met monthly in inconsistent groups to learn about school readiness. UCINET and SIENA were used to examine network dynamics.Results. Children’s mean age was 4.2 years (SD=0.9), and 44% were overweight/obese (BMI≥85th percentile). Parents were predominantly mothers (97%), with a mean age of 31.4 years (SD=5.4), and 81% were overweight/obese (BMI≥25). Over the study, a new social network evolved among participating families. Parents selectively formed friendship ties based on child BMI z-score, (t=2.08;P<.05). This reveals the tendency for mothers to form new friendships with mothers whose children have similar body types.Discussion. Participating in a group-level intervention resulted in new social network formation. New ties were greatest with mothers who had children of similar body types. This finding might contribute to the known inability of parents to recognize child overweight.

Centrality measure based approach for detection of malicious nodes in twitter social network

2018 ◽  
Vol 7 (4.5) ◽  
pp. 518 ◽  
Author(s):  
Krishna Das ◽  
Smriti Kumar Sinha
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Information Diffusion ◽  
Opinion Mining ◽  
Theoretical Method ◽  
Short Paper ◽  
Malicious Nodes ◽  
Data Set ◽  
Centrality Measure ◽  
The Social

In this short paper, network structural measure called centrality measure based mathematical approach is used for detection of malicious nodes in twitter social network. One of the objectives in analysing social networks is to detect malicious nodes which show anomaly behaviours in social networks. There are different approaches for anomaly detection in social networks such as opinion mining methods, behavioural methods, network structural approach etc. Centrality measure, a graph theoretical method related to social network structure, can be used to categorize a node either as popular and influential or as non-influential and anomalous node. Using this approach, we have analyzed twitter social network to remove anomalous nodes from the nodes-edges twitter data set. Thus removal of these kinds of nodes which are not important for information diffusion in the social network, makes the social network clean & speedy in fast information propagation.   

scholarly journals Event-Based User Classification in Weibo Media

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Liang Guo ◽  
Wendong Wang ◽  
Shiduan Cheng ◽  
Xirong Que
Keyword(s):  
Social Network ◽  
Real Data ◽  
Network Information ◽  
User Classification ◽  
Social Events ◽  
The Social ◽  
Access Information ◽  
Primary Groups ◽  
Event Based ◽  
Content Information

Weibo media, known as the real-time microblogging services, has attracted massive attention and support from social network users. Weibo platform offers an opportunity for people to access information and changes the way people acquire and disseminate information significantly. Meanwhile, it enables people to respond to the social events in a more convenient way. Much of the information in Weibo media is related to some events. Users who post different contents, and exert different behavior or attitude may lead to different contribution to the specific event. Therefore, classifying the large amount of uncategorized social circles generated in Weibo media automatically from the perspective of events has been a promising task. Under this circumstance, in order to effectively organize and manage the huge amounts of users, thereby further managing their contents, we address the task of user classification in a more granular, event-based approach in this paper. By analyzing real data collected from Sina Weibo, we investigate the Weibo properties and utilize both content information and social network information to classify the numerous users into four primary groups: celebrities, organizations/media accounts, grassroots stars, and ordinary individuals. The experiments results show that our method identifies the user categories accurately.

scholarly journals Simulating activation propagation in social networks using the graph theory

2010 ◽  
Vol 58 (3) ◽  
pp. 21-28 ◽  
Author(s):  
František Dařena ◽  
Alexander Troussov ◽  
Jan Žižka
Keyword(s):  
Social Networks ◽  
Graph Theory ◽  
Social Network ◽  
Network Formation ◽  
Directed Graphs ◽  
Good Method ◽  
Spreading Activation ◽  
The Social ◽  
Spreading Activation Algorithm ◽  
Social Network Formation

The social-network formation and analysis is nowadays one of objects that are in a focus of intensive research. The objective of the paper is to suggest the perspective of representing social networks as graphs, with the application of the graph theory to problems connected with studying the network-like structures and to study spreading activation algorithm for reasons of analyzing these structures. The paper presents the process of modeling multidimensional networks by means of directed graphs with several characteristics. The paper also demonstrates using Spreading Activation algorithm as a good method for analyzing multidimensional network with the main focus on recommender systems. The experiments showed that the choice of parameters of the algorithm is crucial, that some kind of constraint should be included and that the algorithm is able to provide a stable environment for simulations with networks.

Ontology Combined Based on the Social Network Information

2015 ◽  
pp. 821-828
Author(s):  
Zhengyang Wu ◽  
Yong Tang ◽  
Shaowen Hong ◽  
Chengzhe Yuan ◽  
Huiqiang Mai
Keyword(s):  
Social Network ◽  
Network Information ◽  
The Social
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