scholarly journals Election Control in Social Networks via Edge Addition or Removal

2020 ◽  
Vol 34 (02) ◽  
pp. 1878-1885
Author(s):  
Matteo Castiglioni ◽  
Diodato Ferraioli ◽  
Nicola Gatti
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Positive Result ◽  
Optimization Problem ◽  
Optimal Solution ◽  
The Social ◽  
Wide Range ◽  
Local Modification ◽  
Edge Addition ◽  
Over Time

We focus on the scenario in which messages pro and/or against one or multiple candidates are spread through a social network in order to affect the votes of the receivers. Several results are known in the literature when the manipulator can make seeding by buying influencers. In this paper, instead, we assume the set of influencers and their messages to be given, and we ask whether a manipulator (e.g., the platform) can alter the outcome of the election by adding or removing edges in the social network. We study a wide range of cases distinguishing for the number of candidates or for the kind of messages spread over the network. We provide a positive result, showing that, except for trivial cases, manipulation is not affordable, the optimization problem being hard even if the manipulator has an unlimited budget (i.e., he can add or remove as many edges as desired). Furthermore, we prove that our hardness results still hold in a reoptimization variant, where the manipulator already knows an optimal solution to the problem and needs to compute a new solution once a local modification occurs (e.g., in bandit scenarios where estimations related to random variables change over time).

scholarly journals Election Manipulation on Social Networks: Seeding, Edge Removal, Edge Addition

2021 ◽  
Vol 71 ◽  
pp. 1049-1090
Author(s):  
Matteo Castiglioni ◽  
Diodato Ferraioli ◽  
Nicola Gatti ◽  
Giulia Landriani
Keyword(s):  
Social Network ◽  
Social Influence ◽  
Optimal Solution ◽  
Worst Case ◽  
Edge Removal ◽  
Independent Cascade Model ◽  
The Social ◽  
Special Cases ◽  
Wide Range ◽  
Local Modification

We focus on the election manipulation problem through social influence, where a manipulator exploits a social network to make her most preferred candidate win an election. Influence is due to information in favor of and/or against one or multiple candidates, sent  by seeds and spreading through the network according to the independent cascade model.  We provide a comprehensive theoretical study of the election control problem, investigating  two forms of manipulations: seeding to buy influencers given a social network and removing  or adding edges in the social network given the set of the seeds and the information sent.  In particular, we study a wide range of cases distinguishing in the number of candidates or  the kind of information spread over the network. Our main result shows that the election manipulation problem is not affordable in  the worst-case, even when one accepts to get an approximation of the optimal margin of  victory, except for the case of seeding when the number of hard-to-manipulate voters is not  too large, and the number of uncertain voters is not too small, where we say that a voter  that does not vote for the manipulator's candidate is hard-to-manipulate if there is no way  to make her vote for this candidate, and uncertain otherwise. We also provide some results showing the hardness of the problems in special cases.  More precisely, in the case of seeding, we show that the manipulation is hard even if the  graph is a line and that a large class of algorithms, including most of the approaches  recently adopted for social-influence problems (e.g., greedy, degree centrality, PageRank, VoteRank), fails to compute a bounded approximation even on elementary networks, such  as undirected graphs with every node having a degree at most two or directed trees. In the  case of edge removal or addition, our hardness results also apply to election manipulation  when the manipulator has an unlimited budget, being allowed to remove or add an arbitrary  number of edges, and to the basic case of social influence maximization/minimization in  the restricted case of finite budget. Interestingly, our hardness results for seeding and edge removal/addition still hold  in a re-optimization variant, where the manipulator already knows an optimal solution  to the problem and computes a new solution once a local modification occurs, e.g., the  removal/addition of a single edge.

scholarly journals An efficient hybrid system for anomaly detection in social networks

Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Md. Shafiur Rahman ◽  
Sajal Halder ◽  
Md. Ashraf Uddin ◽  
Uzzal Kumar Acharjee
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Anomaly Detection ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
Security And Privacy ◽  
Support Vector ◽  
The Social ◽  
Wide Range

AbstractAnomaly detection has been an essential and dynamic research area in the data mining. A wide range of applications including different social medias have adopted different state-of-the-art methods to identify anomaly for ensuring user’s security and privacy. The social network refers to a forum used by different groups of people to express their thoughts, communicate with each other, and share the content needed. This social networks also facilitate abnormal activities, spread fake news, rumours, misinformation, unsolicited messages, and propaganda post malicious links. Therefore, detection of abnormalities is one of the important data analysis activities for the identification of normal or abnormal users on the social networks. In this paper, we have developed a hybrid anomaly detection method named DT-SVMNB that cascades several machine learning algorithms including decision tree (C5.0), Support Vector Machine (SVM) and Naïve Bayesian classifier (NBC) for classifying normal and abnormal users in social networks. We have extracted a list of unique features derived from users’ profile and contents. Using two kinds of dataset with the selected features, the proposed machine learning model called DT-SVMNB is trained. Our model classifies users as depressed one or suicidal one in the social network. We have conducted an experiment of our model using synthetic and real datasets from social network. The performance analysis demonstrates around 98% accuracy which proves the effectiveness and efficiency of our proposed system.

scholarly journals Social Networks and Social Interaction among People with Psychiatric Disabilities-Comparison of Users of Day Centres and Clubhouses

2016 ◽  
Vol 9 (6) ◽  
pp. 107 ◽  
Author(s):  
Jenny Hultqvist ◽  
Urban Markström ◽  
Carina Tjörnstrand ◽  
Mona Eklund
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Interaction ◽  
Social Functioning ◽  
Social Engagement ◽  
Self Esteem ◽  
Psychiatric Disabilities ◽  
The Social ◽  
Over Time

OBJECTIVE: The aim of the study was to compare users of community-based mental health day centres (DCs) and clubhouses in Sweden regarding reported social networks and social interaction and the stability of these over time. A further aim was to investigate social network predictors both cross-sectionally and longitudinally.METHODS: People regularly attending DCs (n=128) or clubhouses (n=57) completed questionnaires about social network and social interaction (social engagement and social functioning), self-esteem and socio-demographics at baseline and a nine-month follow-up. RESULTS: Perceived social engagement and social functioning did not differ between the groups and remained stable over time. Fewer in the DC reported having a close friend but there was no difference regarding having recently (the past week) seen a friend. When naming “someone with whom you can share your innermost thoughts and feelings”, the DC group named more professional contacts, fewer friends and more often “nobody” compared to the clubhouse group. Finally, on both occasions the DC group scored significantly lower on size of the social network compared to the clubhouse users. Self-esteem and having recently seen a friend could predict size of the social network in the cross-sectional perspective. Strong indicators of belonging to the group with a larger social network at follow-up were being a woman, attending a clubhouse programme and having scored high on social network at baseline.CONCLUSION & IMPLICATION FOR PRACTICE: Having friends and strengthening one’s self-esteem may be essential factors for the social network of people with psychiatric disabilities in a short-term perspective. Visiting clubhouses seems advantageous in a longer-term perspective.

Codiffusion of Technologies in Social Networks

2020 ◽  
Vol 12 (4) ◽  
pp. 193-228
Author(s):  
Natalia Lazzati
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Diffusion Process ◽  
Technology Diffusion ◽  
Consideration Sets ◽  
Network Structures ◽  
The Social ◽  
Over Time

This paper studies the diffusion process of two complementary technologies among people who are connected through a social network. It characterizes adoption rates over time for different initial allocations and network structures. In doing so, we provide some microfoundations for the stochastic formation of consideration sets. We are particularly interested in the following question: suppose we want to maximize technology diffusion and have a limited number of units of each of the two technologies to initially distribute—how should we allocate these units among people in the social network? (JEL D83, O33, Z13)

scholarly journals Comparison of social structures within cities of very different sizes

2016 ◽  
Vol 3 (2) ◽  
pp. 150526 ◽  
Author(s):  
P. Grindrod ◽  
T. E. Lee
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Social Structure ◽  
Social Structures ◽  
Twitter Data ◽  
Novel Approach ◽  
The Social ◽  
Wide Range ◽  
Virtual City

People make a city, making each city as unique as the combination of its inhabitants. However, some cities are similar and some cities are inimitable. We examine the social structure of 10 different cities using Twitter data. Each city is decomposed to its communities. We show that in many cases one city can be thought of as an amalgamation of communities from another city. For example, we find the social network of Manchester is very similar to the social network of a virtual city of the same size, where the virtual city is composed of communities from the Bristol network. However, we cannot create Bristol from Manchester since Bristol contains communities with a social structure that are not present in Manchester. Some cities, such as Leeds, are outliers. That is, Leeds contains a particularly wide range of communities, meaning we cannot build a similar city from communities outside of Leeds. Comparing communities from different cities, and building virtual cities that are comparable to real cities, is a novel approach to understand social networks. This has implications when using social media to inform or advise residents of a city.

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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