Online Networks Research Articles

Online and Approximate Network Construction from Bounded Connectivity Constraints

The Network Construction problem, studied by Angluin et al., Hosoda et al., and others, asks for a minimum-cost network satisfying a set of connectivity constraints which specify subsets of the vertices in the network that have to form connected subgraphs. More formally, given a set [Formula: see text] of vertices, construction costs for all possible edges between pairs of vertices from [Formula: see text], and a sequence [Formula: see text] of connectivity constraints, the objective is to find a set [Formula: see text] of edges such that each [Formula: see text] induces a connected subgraph of the graph [Formula: see text] and the total cost of [Formula: see text] is minimized. First, we study the online version where every constraint must be satisfied immediately after its arrival and edges that have already been added can never be removed. We give an [Formula: see text]-competitive and [Formula: see text]-competitive polynomial-time algorithms, where [Formula: see text] is an upper bound on the size of constraints, while [Formula: see text] denote the number of constraints and the number of vertices, respectively. On the other hand, we observe that an [Formula: see text]-competitive lower bound as well as an [Formula: see text]-competitive lower bound in the cost-uniform case are implied by the known lower bounds for unbounded constraints. For the cost-uniform case with unbounded constraints, we provide an [Formula: see text]-competitive upper bound with high probability. The latter bound is against an oblivious adversary while our other randomized competitive bounds are against an adaptive adversary. Next, we discuss a hybrid approximation method for the (offline) Network Construction problem combining an approximation algorithm of Hosoda et al. with one of Angluin et al. and an application of the hybrid method to bioinformatics. Finally, we consider a natural strengthening of the connectivity requirements in the Network Construction problem, where each constraint has to induce a subgraph (of the constructed graph) of diameter at most [Formula: see text]. Among other things, we provide a polynomial-time [Formula: see text]-approximation algorithm for the Network Construction problem with the [Formula: see text]-diameter requirements, when each constraint has at most [Formula: see text] vertices, and show the APX-completeness of this variant.

A Gradient Boosted Decision Tree-Based Influencer Prediction in Social Network Analysis

Twitter, Instagram and Facebook are expanding rapidly, reporting on daily news, social activities and regional or international actual occurrences. Twitter and other platforms have gained popularity because they allow users to submit information, links, photos and videos with few restrictions on content. As a result of technology advances (“big” data) and an increasing trend toward institutionalizing ethics regulation, social network analysis (SNA) research is currently confronted with serious ethical challenges. A significant percentage of human interactions occur on social networks online. In this instance, content freshness is essential, as content popularity declines with time. Therefore, we investigate how influencer content (i.e., posts) generates interactions, as measured by the number of likes and reactions. The Gradient Boosted Decision Tree (GBDT) and the Chaotic Gradient-Based Optimizer are required for estimation (CGBO). Using earlier group interactions, we develop the Influencers Prediction issue in this study’s setting of SN-created groups. We also provide a GBDT-CGBO framework and an efficient method for identifying users with the ability to influence the future behaviour of others. Our contribution is based on logic, experimentation and analytic techniques. The goal of this paper is to find domain-based social influencers using a framework that uses semantic analysis and machine learning modules to measure and predict users’ credibility in different domains and at different times. To solve these problems, future research will have to focus on co-authorship networks and economic networks instead of online social networks. The results show that our GBDT-CGBO method is both useful and effective. Based on the test results, the GBDT-CGBO model can correctly classify unclear data, which speeds up processing and makes it more efficient.

Socialization of Pokdarwis Website as Manager of the Medokan Ayu Mangrove Gardens, Surabaya

Medokan Ayu Mangrove Botanical Garden is managed by the Medokan Ayu Tourism Awareness Group (Pokdarwis). Pokdarwis has 54 Micro, Small and Medium Enterprises (MSMEs). The management of the Pokdarwis organization and the Medokan Ayu Mangrove Gardens is still in manual form. Pokdarwis management `s views they need information systems and technology to overcome them. This condition was increasingly needed when the Medokan Ayu Pokdarwis had to temporarily close the Medokan Ayu Mangrove Botanical Gardens due to restrictions on community activities to stop the spread of Covid-19. Perbanas Hayam Wuruk University (UHW) partnership team provides a solution by presenting the Pokdarwis website with an online network. The website is socialized on how to operate, equipped with features that are integrated with MSMEs. The results of the socialization based on the evaluation provided benefits for a number of participants, so the PKM team was still providing assistance for the operation of the website. Pokdarwis Medokan Ayu also hopes that the socialization will continue in the future.

Self-Supervised Learning With Data-Efficient Supervised Fine-Tuning for Crowd Counting

Due to the expensive and laborious annotations of labeled data required by fully-supervised learning in the crowd counting task, it is desirable to explore a method to reduce the labeling burden. There exists a large number of unlabeled images in the wild that can be easily obtained compared to labeled datasets. Based on the characteristics of consistent spatial transformation with the annotations of heads and image, this paper proposes a self-supervised learning framework with unlabeled and limited labeled data for pre-training and fine-tuning crowd counting model (SSL-FT). It includes an online network and a target network that receive the same image but are randomly processed by two defined augmentation transformations. We leverage unlabeled data to pre-train the online network based on a self-supervised loss and small-scale labeled data to transfer the model to a specific domain based on a fully-supervised loss. We demonstrate the effectiveness of the SSL-FT on four public datasets including ShanghaiTech PartA, PartB, UCF-QNRF and WorldExpo'10 utilizing a classical counting model. Experimental results show that our approach performs better than state-of-art semi-supervised methods.

ТЕОРЕТИКО-МЕТОДОЛОГІЧНИЙ АНАЛІЗ ІННОВАЦІЙНИХ ФОРМ І МЕТОДІВ ВЕДЕННЯ ІНФОРМАЦІЙНОЇ БОРОТЬБИ: ВИКЛИКИ ТА ЗАГРОЗИ КІБЕРБЕЗПЕЦІ

With the beginning of full-scale and open aggression on the part of the Russian Federation, improving the system of ensuring Ukraine's national security became an urgent issue. At the same time, ensuring cyber security is one of the priorities in the national security system. The implementation of the specified priority should be carried out by strengthening the capabilities of the national cyber security system to counter cyber threats in the modern security environment. Since now almost every inhabitant of the planet has access to the Internet, there are many more opportunities to influence our thoughts and actions. Information aggressors now do not just tell tales about the enemy, they form a different vision of the world, replace reality with such a well-thought-out hoax that it is almost impossible to distinguish it from the truth. At the end of the 20th beginning of the 21st century. the world received a new form of confrontation  a hybrid war. One of the leading countries that actively uses the tools of hybrid warfare today is Russia. The basic components of the Russian strategy and tactics of modern hybrid warfare were formulated in 2013. In the context of the functioning of the online network space, as a means of conducting information warfare, any format and topic of projects can be used. The best tool for penetrating the enemy's information field is the so-called media viruses - information carriers (events, scandals, rumors, activities of organizations and individuals) that carry veiled ideas and messages in a hidden form. The modern development of the media and online environment makes certain adjustments to our lives. Gradually, new forms of relations between objects and subjects of society in its various spheres: economic, political, social and other are perceived as the norm. Informational and psychological confrontation is no exception. One of the forms of such confrontation is informational online network war, which should be understood as a complex of informational influences between social systems (groups) aimed at obtaining certain advantages in economic, military, political, cultural and public confrontations.

Neo-Gnosticism and the American Dissident Right

Neo-Gnosticism and the American Dissident Right

Can Spectral Information Work While Extracting Spatial Distribution?-An Online Spectral Information Compensation Network for HSI Classification.

In the past few years, deep learning-based methods have shown commendable performance for hyperspectral image (HSI) classification. Many works focus on designing independent spectral and spatial branches and then fusing the output features from two branches for category prediction. In this way, the correlation that exists between spectral and spatial information is not completely explored, and spectral information extracted from one branch is always not sufficient. Some studies also try to directly extract spectral-spatial features using 3D convolutions but are accompanied by the severe over-smoothing phenomenon and poor representation ability of spectral signatures. Unlike the above-mentioned approaches, in this paper, we propose a novel online spectral information compensation network (OSICN) for HSI classification, which consists of a candidate spectral vector mechanism, progressive filling process, and multi-branch network. To the best of our knowledge, this paper is the first to online supplement spectral information into the network when spatial features are extracted. The proposed OSICN makes the spectral information participate in network learning in advance to guide spatial information extraction, which truly processes spectral and spatial features in HSI as a whole. Accordingly, OSICN is more reasonable and more effective for complex HSI data. Experimental results on three benchmark datasets demonstrate that the proposed approach has more outstanding classification performance compared with the state-of-the-art methods, even with a limited number of training samples.

Why Does Science Need Losers?

The article raises the problem of functionality and rational explanation of large arrays of communicative and unclaimed scientific knowledge. To solve the problem and explain this phenomenon, the resources of the system-communicative theory of scientific communication and social-evolutionist approaches are involved. The ability of the system-communicative theory itself to explain this phenomenon is considered as a possibility of its verification. In conclusion, a working hypothesis is proposed linking the existence of a class of unclaimed research and researchers with the function of meta-observation: through their online network reactions on appropriate electronic platforms (downloads, citations, readings, reviews, recommendations, etc.), the distribution of scientific reputations of science leaders and selection of the best scientific knowledge is ensured. This function, according to the author, compensates for the lack of an external audience or public capable of understanding and adequately evaluating scientific achievements in the scientific communication system. It is concluded that the past “collegial and deliberative” assessment of scientific achievements, the appropriate distribution of reputations and support for research projects is incompatible with the dynamic conditions of the “publication market”.

The role of evidence-based misogyny in antifeminist online communities of the ‘manosphere’

In recent years, there have been a growing number of online and offline attacks linked to a loosely connected network of misogynist and antifeminist online communities called ‘the manosphere’. Since 2016, the ideas spread among and by groups of the manosphere have also become more closely aligned with those of other Far-Right online networks. In this commentary, I explore the role of what I term ‘evidence-based misogyny’ for mobilization and radicalization into the antifeminist and misogynist subcultures of the manosphere. Evidence-based misogyny is a discursive strategy, whereby members of the manosphere refer to (and misinterpret) knowledge in the form of statistics, studies, news items and pop-culture and mimic accepted methods of knowledge presentation to support their essentializing, polarizing views about gender relations in society. Evidence-based misogyny is a core aspect for manosphere-related mobilization as it provides a false sense of authority and forges a collective identity, which is framed as a supposed ‘alternative’ to mainstream gender knowledge. Due to its core function to justify and confirm the misogynist sentiments of users, evidence-based misogyny serves as connector between the manosphere and both mainstream conservative as well as other Far-Right and conspiratorial discourses.

Analysis of the statistics of information processes in telecommunication networks from the point of view of a neural-network model of the noosphere

It was found that the distribution of users of social networks by the number of contacts, as well as the statistics of telephone conversations by duration, obtained for different cities on the basis of experimental data, obey the same laws, expressed through a differential equation of the first order. This differential equation contains two phenomenological parameters, one of which is interpreted as the fractal dimension of the communication space, and the second is directly expressed through the Dunbar number or its analogue. Essentially, for all cities under investigation the numerical value of the parameter, interpreted as the fractal dimension of the communication space, remains constant with high accuracy. At the same time, the analogue of the Dunbar number, which describes the distribution of users of social online networks by the number of contacts, correlates with the population of the city. The results obtained are discussed from the point of view of the formation of a global communication network - the noosphere, which, due to the development of telecommunication technologies, is already de facto man-machine system. It is shown that the results obtained confirm the conclusion about the existence of objective laws describing the behavior of human-machine systems, made earlier on the basis of general philosophical considerations.

Privacy Threat MOdeling Language

Online Social Networks (OSNs) are becoming pervasive in today’s world. Millions of people worldwide are involved in different forms of online networking. However, this ease of use of OSNs comes with a cost in terms of privacy. Users of OSNs become victims of identity theft, cyberstalking, and information leakage, which are real threats to privacy. Consequently, new solutions need to be developed for addressing the threat scenarios to which a user is potentially exposed. In this sense, this paper presents PTMOL (Privacy Threat MOdeling Language) as an approach for modeling privacy threats in an OSN domain. The proposed language is related to the attempt to mitigate privacy threats at the design level, thus promoting concern about threats in the stages preceding the development of OSNs. Two studies were conducted to evaluate the use of PTMOL at the design stages, which provided insights into the correctness, completeness, ease of use, usefulness, user satisfaction, and feasibility of the proposal. The results indicated that PTMOL can be incorporated into software development during the design phase. Via the language, we expect to support designers in making more pre-emptive decisions about user privacy risk, and help them to introduce privacy early in the development cycle of OSNs.

Consumer attitude and engagement with eWOM on social online network

Consumer attitude and engagement with eWOM on social online network

National health surveys: overview of sampling techniques and data collected using complex designs.

This article aimed to present an overview of national health surveys, sampling techniques, and components of statistical analysis of data collected using complex sampling designs. Briefly, surveys aimed at assessing the nutritional status of Brazilians and maternal and child health care were described. Surveys aimed at investigating access to and use of health services and funding, those aimed at surveillance of chronic noncommunicable diseases and associated behaviors, and those focused on risk practices regarding sexually transmitted infections were also addressed. Health surveys through social networks, including online networks, deserved specific attention in the study. The conclusion is that the development of health surveys in Brazil, in different areas and using different sampling methodologies, has contributed enormously to the advancement of knowledge and to the formulation of public policies aimed at the health and well-being of the Brazilian population.

Net-ativismo ambiental nas plataformas de petições online Avaaz.org e Change.org: algumas observações

Abstract The intensive use of the Internet platforms establishes new perspectives on the social and political function of the media. This article focuses on the possibilities of alternative mobilizing actions, involving the participation of citizens around issues related to the conflicts of society and nature. We use the concept of net-activism (DI FELICE, 2012), with its features online network action. The objective is to highlight the role of the online platforms for forwarding public petitions of Avaaz.org and Change.org, the two most accessed platforms in Brazil, around social and environmental claims. After submitting the theoretical frameworks, we described the platforms, and selected 22 campaigns to perform an interpretative analysis, based on the arguments they use for demands and struggles, followed by a typology proposed by Castells (2018). In the end, it is possible to understand, even partially, the role of this social-environmental activism around online petitions.

COMMUNICATION PECULIARITIES OF POLITIZED COMMUNITIES IN RUSSIAN-LANGUAGE TELEGRAM CHANNELS

The article presents the results of the analysis of the features of political communication in the Telegram Russian-speaking political communities. Popular Russian-language political Telegram channels with a stable audience have been analyzed. When conducting the study, the case study method was used. The author proves that the traditional five-component model of political communication, developed by G. Lasswell in the 1940s, is preserved in online network communication. The author takes the position that political online communication, having received some new and strengthened the traditional characteristics of offline political communication, cannot be considered something radically different and fundamentally new. An author's typology of politicized telegram channels is proposed in terms of the dominant expert or manipulative function, the positioning model of the channel's authorship, and also taking into account such criteria as the style of the message, the presence / absence of feedback, the format of communication between the audience and the author of the posts. It is shown that the “one to many” communication model is of limited use in the Telegram messenger. Strategies for constructing the information part of telegram-channel messages are revealed. Additional metrics have been introduced to measure "digital creolized political content". The consequences of changing the paradigm adopted in the classical model of political informing the audience and strengthening such characteristics as text manipulativeness, emotiveness, expressiveness, illogicality / incoherence, ideological, syncretism, emotional pathos, eccentricity, rhetorical simplicity, efficiency, gamification of information, interactivity, instantaneous criticism, immediacy of comments. Examples of the use in popular politicized Internet channels of such techniques as "digital demagoguery", reduced speech style, theatricalization, scenario performances, manipulative creativity, the use of images of fake characters, clickbaiting are given.

Low Cost Online Network Traffic Measurement With Subspace-Based Matrix Completion

Traffic Matrix (TM) is important for network operation and management. However, it is hard to measure the complete TM due to the high measurement cost. Few recent studies propose using sparse measurement with only a subset of origin and destination pairs (OD pairs) while the other OD pairs are reconstructed through matrix completion. Although effective, current sparse network monitoring schemes can hardly support online network monitoring which requires scheduling the sample taking adaptively in each new time slot one by one. To meet the online network monitoring scenario, we propose a sparse network monitoring scheme by exploiting subspace-based matrix completion. Several novel techniques are proposed in our scheme. First, to capture the dynamic rank feature, we design the scheme based on sliding window and propose an algorithm to estimate the rank of current window even though we don't know the data of the upcoming time slot. Secondly, based on the rank estimated, we propose an adaptive sampling scheduling algorithm. Finally, we propose a lightweight algorithm to speed up the reconstruction process by reusing the matrix calculation results in the previous time slot. The experimental results demonstrate that our scheme guarantees the high precision of network-wide TM monitoring while significantly reducing the measurement cost.

A recommendation system for online social semantic network using knowledge based, content based and collaborative filtering

Recommendation systems are a very popular service whose accuracy and sophistication keeps increasing every day. Yet current systems pose a limitation on personalized user recommendation, which we wish to improve. We are developing Content-Based, Collaborative Filtering and Knowledge-Based models and we wish to find the most appropriate approach to build restaurant recommendation systems. We followed steps that involved a pipeline to process reviews of restaurants obtained from a widely used online network of zomato users (India’s largest restaurant service) and calculate ratings of restaurants from reviews. Using a machine learning technique, it continuously analyses user restaurant visit patterns.

Mining and analysing online social networks: Studying the dynamics of digital peer support

In recent years, the rapid growth of user-generated content has led to much research evaluating the patterns of online information exchange. These studies demonstrate that online communities are valuable data sources which provide rich, longitudinal data that would otherwise be difficult, if not impossible to access. Given the increased research interest, mining and analysing online social networks has become an important research domain, encompassing a variety of approaches. To analyse the large number of observations commonly found in online communities, we propose to first mine the data using a so-called Webscraper and then combine Social Network Analysis (SNA) with Sentiment Analysis to explore both content and relationships. The hands-on approach described in this article is targeted at researchers without a background in technical disciplines. Instead of focusing on some of the specific algorithms that facilitate the mining and analysis of online data, we describe how to use and combine out-of-the-box solutions to collect and analyse the online network data. Moreover, we document the steps taken and present important lessons learnt throughout the process of collecting and analysing data from an online health community with 108,569 registered users who contributed to 197,980 discussions with a total of 484,250 replies.In sum, our method proposes to:•Extract all relevant data from an openly accessible online community using a Webscraper.•Determine and visualise the relationships between users and the properties of the social network as a whole using Social Network Analysis.•Conduct Sentiment Analysis to detect the emotional tone of the online contributions, and to possibly infer further variables from the text such as the personality characteristics of users.

Consumer attitude and engagement with eWOM on social online network

Consumer attitude and engagement with eWOM on social online network

현대 조형사진에서 나타나는 초현실적 공간에 관한 연구

This research aims to study the surreal space that appears in modern plastic photography by analyzing its surreality, which is a spatial characteristic of plastic photography, from a visual, figurative, and interactive perspective. Marey's chronophotographie expands the concept of space according to the superpositioning process of real space, showing a surreal space from a visual perspective through superimposed images. The surrealism of photomontage, created by Krauss' redundant process of spacing, extends images separated by the concept of desublimation in writing into unconscious acts of connection, creating a surreal space from a figurative perspective. Mitchell’s thinking about post-photography is to recognize the place and space in the photograph as a complex of interactions, centering on digital images and editing techniques performed in online networks and computer programs. Based on this way of thinking, the works of Andreas Gursky and Shinichi Maruyama, which show non-placed, non-material absent landscapes, appear as surreal spaces with a reciprocal perspective created by digital images. This study is thus significant in that it connects the surreal elements inherent in modern plastic photography with the concept of space.