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.

scholarly journals Research on Sentiment Tendency and Evolution of Public Opinions in Social Networks of Smart City

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Yanni Liu ◽  
Dongsheng Liu ◽  
Yuwei Chen
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Sentiment Analysis ◽  
Smart City ◽  
Topic Model ◽  
Rapid Development ◽  
Mobile Internet ◽  
Time Curve ◽  
Public Opinions ◽  
The Social

With the rapid development of mobile Internet, the social network has become an important platform for users to receive, release, and disseminate information. In order to get more valuable information and implement effective supervision on public opinions, it is necessary to study the public opinions, sentiment tendency, and the evolution of the hot events in social networks of a smart city. In view of social networks’ characteristics such as short text, rich topics, diverse sentiments, and timeliness, this paper conducts text modeling with words co-occurrence based on the topic model. Besides, the sentiment computing and the time factor are incorporated to construct the dynamic topic-sentiment mixture model (TSTS). Then, four hot events were randomly selected from the microblog as datasets to evaluate the TSTS model in terms of topic feature extraction, sentiment analysis, and time change. The results show that the TSTS model is better than the traditional models in topic extraction and sentiment analysis. Meanwhile, by fitting the time curve of hot events, the change rules of comments in the social network is obtained.

Genetic Algorithm Applied in the Social Network Data Analysis

2013 ◽  
Vol 427-429 ◽  
pp. 2188-2191
Author(s):  
Lei Liu ◽  
Quan Bao Gao
Keyword(s):  
Genetic Algorithm ◽  
Social Networks ◽  
Social Network ◽  
Data Analysis ◽  
Rapid Development ◽  
Virtual Community ◽  
Social Network Data ◽  
Starting Point ◽  
The Social ◽  
Optimal Data Analysis

The rapid development of network and information technology makes the network become the indispensable part in people's life. Network design uses email as a starting point, instead of actual letters. Then Happy Nets, BBS etc. are evolved from it, with virtual as their major feature. In the process of social networks evolution, the personal image transformed from the actual into the virtual one. All this has contributed to the birth of the social network, which then makes the contacts among people presenting the feature of network expansion and cost reduction. The popular social network nowadays is considered to be social plus network, namely, through the network, as a carrier, people are connected to form a virtual community with certain characteristics. Based on the genetic algorithm and genetic coding technology, the article is designed to make the optimal data analysis and create a optimistic cyber environment in the process of the social networks explosive development.

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 A Weight based Scheme for Improving the Accuracy of Relationship in Social Network

2019 ◽  
Vol 8 (11) ◽  
pp. 3040-3043 ◽  
Keyword(s):  
Social Media ◽  
Social Network ◽  
Link Prediction ◽  
Performance Metrics ◽  
Prediction Algorithm ◽  
Relationship Matrix ◽  
Community Network ◽  
The Social ◽  
Relationship Value ◽  
The Relationship

The usage of social media has become unavoidable in the last decade. The social media is highly dynamic in nature and grows rapidly. The community network offers a rich expedient of various data. The detection of communities is based on the frequency in the networks which is usually represented by graphs. The vertices (nodes) are representing the social actor and the edges (links) represent the relation between those actors. The community link detection is as hard as the graph increases up to millions of vertices and edges. The accuracy of link prediction for inferring missing (erased or broken) links is very complex due to the dynamic nature of links. The links are updated from time to time and the new links are established dynamically. As the links are appeared and disappeared dynamically, the accuracy of identifying the edges of the social network graph of the user is complex in nature. Many efforts have been put up in developing link prediction algorithms in the past, but still there is a lacuna in accuracy in predicting inferred / broken links. A weight based link prediction algorithm is proposed to improve the accuracy of the link prediction on inferred / broken links in the social media. In this method, a weight based link analysis is employed to quantify the relative value between two nodes in the community network. The correlation value for relationship is also determined over a period of time using the designed relationship matrix. The relationship value between the nodes is computed by a Euclidian distance approach. The relationship value of each node is determined by the relationship equation using weight values. The proposed approach is experimented in constrained environment for 2 users’ Facebook usages over a period of a year. The accuracy of relationship is used as performance metrics. The results shown that the accuracy is improved 2.35% more than random predictor method

Emotive Trending And Tracking of Tweets

2017 ◽  
Vol 7 (11) ◽  
pp. 25
Author(s):  
Sanjay Chhataru Gupta
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
The Social ◽  
The World ◽  
Complex Events

Popularity of the social media and the amount of importance given by an individual to social media has significantly increased in last few years. As more and more people become part of the social networks like Twitter, Facebook, information which flows through the social network, can potentially give us good understanding about what is happening around in our locality, state, nation or even in the world. The conceptual motive behind the project is to develop a system which analyses about a topic searched on Twitter. It is designed to assist Information Analysts in understanding and exploring complex events as they unfold in the world. The system tracks changes in emotions over events, signalling possible flashpoints or abatement. For each trending topic, the system also shows a sentiment graph showing how positive and negative sentiments are trending as the topic is getting trended.

The Oxford Handbook of Social Networks

2020 ◽  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Life ◽  
Information Science ◽  
Computational Social Science ◽  
The Social ◽  
Diverse Field ◽  
The Rich

Social networks fundamentally shape our lives. Networks channel the ways that information, emotions, and diseases flow through populations. Networks reflect differences in power and status in settings ranging from small peer groups to international relations across the globe. Network tools even provide insights into the ways that concepts, ideas and other socially generated contents shape culture and meaning. As such, the rich and diverse field of social network analysis has emerged as a central tool across the social sciences. This Handbook provides an overview of the theory, methods, and substantive contributions of this field. The thirty-three chapters move through the basics of social network analysis aimed at those seeking an introduction to advanced and novel approaches to modeling social networks statistically. The Handbook includes chapters on data collection and visualization, theoretical innovations, links between networks and computational social science, and how social network analysis has contributed substantively across numerous fields. As networks are everywhere in social life, the field is inherently interdisciplinary and this Handbook includes contributions from leading scholars in sociology, archaeology, economics, statistics, and information science among others.

Comparing the social networks of service users with long term mental health needs living in community with those in a general adult in-patient unit

2021 ◽  
pp. 002076402110175
Author(s):  
Roberto Rusca ◽  
Ike-Foster Onwuchekwa ◽  
Catherine Kinane ◽  
Douglas MacInnes
Keyword(s):  
Mental Health ◽  
Social Networks ◽  
Social Network ◽  
Patient Group ◽  
Mental Health Problems ◽  
Well Being ◽  
Health Problems ◽  
Practical Support ◽  
The Social

Background: Relationships are vital to recovery however, there is uncertainty whether users have different types of social networks in different mental health settings and how these networks may impact on users’ wellbeing. Aims: To compare the social networks of people with long-term mental illness in the community with those of people in a general adult in-patient unit. Method: A sample of general adult in-patients with enduring mental health problems, aged between 18 and 65, was compared with a similar sample attending a general adult psychiatric clinic. A cross-sectional survey collected demographic data and information about participants’ social networks. Participants also completed the Short Warwick Edinburgh Mental Well-Being Scale to examine well-being and the Significant Others Scale to explore their social network support. Results: The study recruited 53 participants (25 living in the community and 28 current in-patients) with 339 named as important members of their social networks. Both groups recorded low numbers in their social networks though the community sample had a significantly greater number of social contacts (7.4 vs. 5.4), more monthly contacts with members of their network and significantly higher levels of social media use. The in-patient group reported greater levels of emotional and practical support from their network. Conclusions: People with serious and enduring mental health problems living in the community had a significantly greater number of people in their social network than those who were in-patients while the in-patient group reported greater levels of emotional and practical support from their network. Recommendations for future work have been made.

scholarly journals The switching mechanisms of social network densification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Teruyoshi Kobayashi ◽  
Mathieu Génois
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Context ◽  
Social Interactions ◽  
Regime Switching ◽  
Face To Face ◽  
The Social ◽  
Switching Property ◽  
Distinctive Type ◽  
Insight Into

AbstractDensification and sparsification of social networks are attributed to two fundamental mechanisms: a change in the population in the system, and/or a change in the chances that people in the system are connected. In theory, each of these mechanisms generates a distinctive type of densification scaling, but in reality both types are generally mixed. Here, we develop a Bayesian statistical method to identify the extent to which each of these mechanisms is at play at a given point in time, taking the mixed densification scaling as input. We apply the method to networks of face-to-face interactions of individuals and reveal that the main mechanism that causes densification and sparsification occasionally switches, the frequency of which depending on the social context. The proposed method uncovers an inherent regime-switching property of network dynamics, which will provide a new insight into the mechanics behind evolving social interactions.

Social Networks, Their Basic Factors, as Well as Their Role in Society and the State in the Context of Digital Transformation

2020 ◽  
Vol 144 ◽  
pp. 26-35
Author(s):  
Rem V. Ryzhov ◽  
◽  
Vladimir A. Ryzhov ◽  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Life Support ◽  
Social Institutions ◽  
The State ◽  
Main Task ◽  
Organizational Models ◽  
Control Center ◽  
The Social ◽  
Single Strategy

Society is historically associated with the state, which plays the role of an institution of power and government. The main task of the state is life support, survival, development of society and the sovereignty of the country. The main mechanism that the state uses to implement these functions is natural social networks. They permeate every cell of society, all elements of the country and its territory. However, they can have a control center, or act on the principle of self-organization (network centrism). The web is a universal natural technology with a category status in science. The work describes five basic factors of any social network, in particular the state, as well as what distinguishes the social network from other organizational models of society. Social networks of the state rely on communication, transport and other networks of the country, being a mechanism for the implementation of a single strategy and plan. However, the emergence of other strong network centers of competition for state power inevitably leads to problems — social conflicts and even catastrophes in society due to the destruction of existing social institutions. The paper identifies the main pitfalls using alternative social networks that destroy the foundations of the state and other social institutions, which leads to the loss of sovereignty, and even to the complete collapse of the country.

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