Information Flow In Social Networks Research Articles

Leading Global Cities In The Russian-Language Segment Of Internet: How Do They Look Like At The Beginning Of The 21st Century?

In the XXI century, the factors of the development of megacities are changing rapidly, the competitiveness of which increasingly depends on their image in different social environments. The purpose of the study is to identify main common features of the current image of the leading global cities (London, Paris, New York and Tokyo) in the Russian–language segment of the Internet (Runet). The research was based on a combination of methods of socio-economic geography, quantitative and qualitative methods of sociology. To reflect the objective reality of megacities, international statistics were used; to assess the role of Internet information resources influencing the formation of the image, the number of search queries in Runet and the context of publications about cities in social media were analyzed; to identify the configuration of the image, the data of a sociological survey were studied. It was revealed that the perception of megacities in Runet is based on a comparable volume of information flow in social networks (the number of published messages is 20 thousand per day for Paris and London, 15 thousand for New York, 4 thousand for Tokyo) and on similarity of publications’ context (2/5 are in the categories “entertainment” and “personality”). Despite certain limitations of the methods used and the relevance of the digital artifacts obtained, the image structure of the centers under consideration is characterized by the predominance of objective over subjective and cognitive (knowledge and ideas about “physical” givens) over affective (emotions, feelings, sensations) components with the key role of four collective categories: common features of large cities, idealized imagery of the country, prestigious urban locations and world architectural brands; as well as high stability in time and space. The continuation of research based on the proposed principles, while improving the methodology and involving cities of different classes, will contribute to the adaptation of foreign experience in designing of images of ambitious Russian cities in the face of new global challenges.

Fairness of Information Flow in Social Networks

Social networks form a major parts of people’s lives, and individuals often make important life decisions based on information that spreads through these networks. For this reason, it is important to know whether individuals from different protected groups have equal access to information flowing through a network. In this article, we define the Information Unfairness (IUF) metric, which quantifies inequality in access to information across protected groups. We then introduce MinIUF , an algorithm for reducing inequalities in information flow by adding edges to the network. Finally, we provide an in-depth analysis of information flow with respect to an attribute of interest, such as gender, across different types of networks to evaluate whether the structure of these networks allows groups to equally access information flowing in the network. Moreover, we investigate the causes of unfairness in such networks and how it can be improved.

Social networks and health information sharing in COVID-19 pandemic

Abstract Background Understanding health information flow in social networks is important for designing effective health communications strategies and to achieve health literacy. Limited information is known about variation in social networks and health information sharing in the COVID-19 pandemic by demographic factors. Young people are of particular interest given their heavy exposure to digital media sources, which include considerable health misinformation. Methods Hawai'i (n = 324) residents between 18-35 completed a Spring 2021 online survey including questions on health information flow in social networks: (1) how many they talked to and (2) listened to about health. Two Poisson regression models were fit with gender, education, and race/ethnicity predicting social network size. Results Respondents were 67.6% female. Respondents discussed their own health with 2-3 people (M = 2.18, SD = 2.95) and listened to roughly the same number. Respondents who talked with a greater number of individuals about their own health were significantly more likely to have larger networks for listening to others (r (317) = .614; p< .001). In the model for discussing their own health, as education increased so did social network size. For the model predicting discussing others’ health, gender was significant (p = 0.003); women listened to 30.6% more individuals than men. Most (73%) respondents had conducted a recent digital health search for either themselves or someone else, including for parents, grandparents, and children. Facebook (63%) and Instagram (58%) were the most popular online sources for COVID-19 health information. Conclusions Understanding social networks and digital health sources in young people are important for designing effective health communications to reach all communities, especially those experiencing health inequities, given the amount of health misinformation circulating and the need to build trust in public health communication. Key messages • Social networks provide access to critical health information including information obtained from digital sources. • Gender and education were important predictors of social network size in COVID-19 health communications.

Unrest in Nukus: Inside View

In this analytical article, the authors study the public discussion of constitutional reforms in Uzbekistan. The reasons and necessity of amendments to the legislation are analyzed. The motives underlying the support of the President’s ideas by the political parties of the country, including such parties as, among them, the Liberal Democratic, People’s Democratic, as well as the Milli Tiklanish and Adolat parties, are investigated. Particular attention is paid to the open and pragmatic work of the constitutional commission established in the Parliament of the Republic of Uzbekistan. The authors explore the substance and the content of the information and psychological influence, including calls for separatist actions from the outside, spread among the population of Karakalpakstan. The causes, conditions and behavior of some youth representatives participating in illegal meetings are analyzed. Observing the information flows in social networks and the Internet, the authors compiled an analytical chronology of the events that took place in Nukus from June 26 till July 3.

The “digital pathogen” concept: epidemiological approach to problem of disorders associated with information technologies

The article is devoted to conceptualization of current scientific experience of studying disorders associated with information technologies. Over the past decades, prevalence of information technologies in world modern society occurs with such intensity that detailed study of influence of this factor on health is significantly lagged behind. In the last decade, a number of pathological conditions associated with application of modern information technologies, in particular psychological and psychiatric profile, was described. The new concepts of Internet addiction, gambling, problematic use of technologies, etc., describing individual response of patients, are introduced. At that, lack of fundamental understanding of both etiology and pathogenesis of described conditions due to multiform specifics, high comorbidity and active evolution of digital society. The article attempts to fill this gap and to approach issue on population scale, namely to evaluate from perspective of epidemiological method characteristics of occurrence, spread and termination of pathogenic effects. The analysis of existing studies suggests that causation of emerging disorders is related not only to addictive behavior and prolonged exposure to information flow, but also to different pathogenicity of its particular components, causing various clinical forms of health disorders in individual with different levels of susceptibility. The pattern of new unique pathogen with specific propagation in human population is described - indirectly through digital environment - and capable of replication within specific ecological niche, i.e. digital pathogen tropic to bio-substrates of higher nervous activity. At that, the duration of contact with digital environment is considered as necessary but insufficient cause of development of pathological process that corresponds to empirical data. The most important property of digital pathogen propagation is its ability to replicate in anthropogenic abiotic digital environment, which is conditioned by its nature, mechanisms of accumulation and distribution. It is also noteworthy that basic principles of dissemination of information flows in social networks largely repeat principles of propagation of infectious pathogens in nature and are characterized by unique mechanism and ways of its transmission. It is concluded that in modern conditions there is a need to study problem of pathology associated with digital environment at population level from position of epidemiological method. The important aspects are joint efforts in studying and classifying new pathogen at interdisciplinary level and developing preventive measures.

Development of a method for calculation of information protection from the clustering coefficient and information flow in social networks

The object of research is the system of information protection of the social network. The article investigates the dynamic models of the information protection system in social networks taking into account the clustering coefficient, and also analyzes the stability of the protection system. In graph theory, the clustering factor is a measure of the degree to which nodes in a graph tend to group together. The available data suggest that in most real networks, and in particular in social networks, nodes tend to form closely related groups with a relatively high density of connections. It is probability is greater than the average probability of a random connection between two nodes. There are two variants of this term: global and local. The global version was created for a general idea of network clustering, while the local one describes the nesting of individual nodes. There is a practical interest in studying the behavior of the system of protection of social networks from the value of the clustering factor. Dynamic systems of information protection in social networks in the mathematical sense of this term are considered. A dynamic system is understood as any object or process for which the concept of state as a set of some quantities at a given moment of time is unambiguously defined and a given law is described that describes the change (evolution) of the initial state over time. This law allows the initial state to predict the future state of a dynamic system. It is called the law of evolution. The study is based on the nonlinearity of the social network protection system. To solve the system of nonlinear equations used: the method of exceptions, the joint solution of the corresponding homogeneous characteristic equation. Since the differential of the protection function has a positive value in some data domains (the requirement of Lyapunov's theorem for this domain is not fulfilled), an additional study of the stability of the protection system within the operating parameters is required. Phase portraits of the data protection system in MatLab/Multisim are determined, which indicate the stability of the protection system in the operating range of parameters even at the maximum value of influences.

Research on the Application of Fluid Dynamics in Internet Rumor Dissemination

The spread of network rumors has always been a research hotspot. How information flows in social networks is a question of interest to us. Based on the theory of fluid mechanics, the dissemination model is constructed, and the dissemination mechanism of network rumor is explored. Through numerical simulation, it is found that the flow of rumor information has a strong correlation with the subject of rumor dissemination and their relationship. The dissemination law of rumor information is revealed, which provides theoretical support for rumor control and governance.

Making a Small World Smaller: Path Optimization in Networks

Reduction of end-to-end network delay is an optimization task with applications in multiple domains. Low delays enable improved information flow in social networks, quick spread of ideas in collaboration networks, low travel times for vehicles on road networks, and increased rate of packets in the case of communication networks. Delay reduction can be achieved by both improving the propagation capabilities of individual nodes and adding additional edges in the network. One of the main challenges in such network design problems is that the effects of local changes are not independent, and as a consequence, there is a combinatorial search-space of possible improvements. Thus, minimizing the cumulative propagation delay requires novel scalable and data-driven approaches. We consider the problem of network delay minimization via node upgrades. We show that the problem is NP-hard and prove strong inapproximability results about it (i.e., APX-hard) even for equal vertex delays. On the positive side, probabilistic approximations for a restricted version of the problem can be obtained. We propose a greedy heuristic to solve the general problem setting which has good quality in practice, but does not scale to very large instances. To enable scalability to real-world networks, we develop approximations for Greedy with probabilistic guarantees for every iteration, tailored to different models of delay distribution and network structures. Our methods scale almost linearly with the graph size and consistently outperform competitors in quality. We evaluate our approaches on several real-world graphs from different genres. We achieve up to two orders of magnitude speed-up compared to alternatives from the literature on moderate size networks, and obtain high-quality results in minutes on large datasets while competitors from the literature require more than four hours.

Noticeable network delay minimization via node upgrades

In several domains, the flow of data is governed by an underlying network. Reduction of delays in end-to-end data flow is an important network optimization task. Reduced delays enable shorter travel times for vehicles in road networks, faster information flow in social networks, and increased rate of packets in communication networks. While techniques for network delay minimization have been proposed, they fail to provide any noticeable reduction in individual data flows. Furthermore, they treat all nodes as equally important, which is often not the case in real-world networks. In this paper, we incorporate these practical aspects and propose a network design problem where the goal is to perform k network upgrades such that it maximizes the number of flows in the network with a noticeable reduction in delay. We show that the problem is NP-hard, APX-hard, and non-submodular. We overcome these computational challenges by designing an importance sampling based algorithm with provable quality guarantees. Through extensive experiments on real and synthetic data sets, we establish that importance sampling imparts up to 1000 times speed-up over the greedy approach, and provides up to 70 times the improvement achieved by the state-of-the-art technique.

Identifying central nodes for information flow in social networks using compressive sensing

This paper addresses the problem of identifying central nodes from the information flow standpoint in a social network. Betweenness centrality is the most prominent measure that shows the node importance from the information flow standpoint in the network. High betweenness centrality nodes play crucial roles in the spread of propaganda, ideologies, or gossips in social networks, the bottlenecks in communication networks, and the connector hubs in biological systems. In this paper, we introduce DICeNod, a new approach to efficiently identify central nodes in social networks without direct measurement of each individual node using compressive sensing, which is a well-known paradigm in sparse signal recovery. DICeNod can perform in a distributed manner by utilizing only local information at each node; thus, it is appropriate for large real-world and unknown networks. We theoretically show that by using only $$O \big ( k \log (\frac{n}{k}) \big )$$ indirect end-to-end measurements in DICeNod, one can recover top-k central nodes in a network with n nodes, even though the measurements have to follow network topological constraints. Furthermore, we show that the computationally efficient $$\ell _1$$ -minimization can provide recovery guarantees to infer such central nodes from the constructed measurement matrix with this number of measurements. Finally, we evaluate accuracy, speedup, and effectiveness of the proposed method by extensive simulations on several synthetic and real-world networks. The experimental results demonstrate that the number of correctly identified central nodes and their estimated rank based on the DICeNod framework and the global betweenness centrality correlate very well. Moreover, the results show that DICeNod is a scalable framework and it can be efficiently used for identifying central nodes in real-world networks.

Complex contagions and the diffusion of popular Twitter hashtags in Nigeria

Social media sites such as Facebook and Twitter provide highly granular time-stamped data about the interactions and communications between people and provide us unprecedented opportunities for empirically testing theory about information flow in social networks. Using publicly available data from Twitter’s free API (Application Program Interface), we track the adoption of popular hashtags in Nigeria during 2014. These hashtags reference online marketing campaigns, major news stories, and events and issues specific to Nigeria, including reactions to the kidnapping of 276 schoolgirls in Northeastern Nigeria by the Islamic extremist group Boko Haram. We find that hashtags related to Nigerian sociopolitical issues, including the #bringbackourgirls hashtag, which was associated with protests against the Nigerian government’s response to the kidnapping, are more likely to be adopted among densely connected users with multiple network neighbors who have also adopted the hashtag, compared to mainstream news hashtags. This association between adoption threshold and local network structure is consistent with theory about the spread of complex contagions, a type of social contagion which requires social reinforcement from multiple adopting neighbors. Theory also predicts the need for a critical mass of adopters before the contagion can become viral. We illustrate this with the #bringbackourgirls hashtag by identifying the point at which the local social movement transforms into a more widespread phenomenon. We also show that these results are robust across both the follow and reply/mention/retweet networks on Twitter. Our analysis involves data mining records of hashtag adoption and of the social connections between adopters.

Searching for superspreaders of information in real-world social media.

A number of predictors have been suggested to detect the most influential spreaders of information in online social media across various domains such as Twitter or Facebook. In particular, degree, PageRank, k-core and other centralities have been adopted to rank the spreading capability of users in information dissemination media. So far, validation of the proposed predictors has been done by simulating the spreading dynamics rather than following real information flow in social networks. Consequently, only model-dependent contradictory results have been achieved so far for the best predictor. Here, we address this issue directly. We search for influential spreaders by following the real spreading dynamics in a wide range of networks. We find that the widely-used degree and PageRank fail in ranking users' influence. We find that the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society. Furthermore, when the complete global network structure is unavailable, we find that the sum of the nearest neighbors' degree is a reliable local proxy for user's influence. Our analysis provides practical instructions for optimal design of strategies for “viral” information dissemination in relevant applications.

Research of Collaborative Filtering Recommendation Algorithm based on Network Structure

This paper combines the classic collaborative filtering algorithm with personalized recommendation algorithm based on network structure. For the data sparsity and malicious behavior problems of traditional collaborative filtering algorithm, the paper introduces a new kind of social network-based collaborative filtering algorithm. In order to improve the accuracy of the personalized recommendation technology, we first define empty state in the state space of multi-dimensional semi-Markov processes and obtain extended multi-dimensional semi-Markov processes which are combined with social network analysis theory, and then we get social network information flow model. The model describes the flow of information between the members of the social network. So, we propose collaborative filtering algorithm based on social network information flow model. The algorithm uses social network information and combines user trust with user interest and find nearest neighbors of the target user and then forms a project recommended to improve the accuracy of recommended. Compared with the traditional collaborative filtering algorithm, the algorithm can effectively alleviate the sparsity and malicious behavior problem, and significantly improve the quality of the recommendation system recommended.

Managing node disappearance based on information flow in social networks

Social networks are dynamic structures in which entities and links appear and disappear for different reasons. Starting from the observation that each entity plays a more or less important role in transmitting the information inside a network, the objective of this article is to propose a method which exploits the role played by a given node to both estimate the impact of its disappearance on the information flow, and conduct network changes to restore the information flow with a similar quality as before the node disappearance. To this end, we propose a network restructuring approach that categorizes nodes into critical and non-critical classes based on their role, and hence, manages their disappearance appropriately by adding new links in a parsimonious way and selecting a substitute for a deleted critical node. As opposed to a previously defined solution, our approach adds links that are just enough to maintain the quality of the information flow within the network as before a node deletion. A prototype is designed and implemented using an open source social network analysis library (NetworkX). Its validation is conducted using network data sets with various sizes. The empirical study shows a low network update, a quite constant quality of the information flow and reasonable execution times after a node deletion.

Vertex routing models

A class of models describing the flow of information within networks via routing processes is proposed and investigated, concentrating on the effects of memory traces on the global properties. The long-term flow of information is governed by cyclic attractors, allowing to define a measure for the information centrality of a vertex given by the number of attractors passing through this vertex. We find the number of vertices having a nonzero information centrality to be extensive/subextensive for models with/without a memory trace in the thermodynamic limit. We evaluate the distribution of the number of cycles, of the cycle length and of the maximal basins of attraction, finding a complete scaling collapse in the thermodynamic limit for the latter. Possible implications of our results for the information flow in social networks are discussed.

Group betweenness and co-betweenness: Inter-related notions of coalition centrality

Vertex betweenness centrality is a metric that seeks to quantify a sense of the importance of a vertex in a network in terms of its ‘control’ on the flow of information along geodesic paths throughout the network. Two natural ways to extend vertex betweenness centrality to sets of vertices are (i) in terms of geodesic paths that pass through at least one of the vertices in the set, and (ii) in terms of geodesic paths that pass through all vertices in the set. The former was introduced by Everett and Borgatti [Everett, M., Borgatti, S., 1999. The centrality of groups and classes. Journal of Mathematical Sociology 23 (3), 181–201], and called group betweenness centrality. The latter, which we call co-betweenness centrality here, has not been considered formally in the literature until now, to the best of our knowledge. In this paper, we show that these two notions of centrality are in fact intimately related and, furthermore, that this relationship may be exploited to obtain deeper insight into both. In particular, we provide an expansion for group betweenness in terms of increasingly higher orders of co-betweenness, in a manner analogous to the Taylor series expansion of a mathematical function in calculus. We then demonstrate the utility of this expansion by using it to construct analytic lower and upper bounds for group betweenness that involve only simple combinations of (i) the betweenness of individual vertices in the group, and (ii) the co-betweenness of pairs of these vertices. Accordingly, we argue that the latter quantity, i.e., pairwise co-betweenness, is itself a fundamental quantity of some independent interest, and we present a computationally efficient algorithm for its calculation, which extends the algorithm of Brandes [Brandes, U., 2001. A faster algorithm for betweenness centrality. Journal of Mathematical Sociology 25, 163] in a natural manner. Applications are provided throughout, using a handful of different communication networks, which serve to illustrate the way in which our mathematical contributions allow for insight to be gained into the interaction of network structure, coalitions, and information flow in social networks.

A probabilistic switch model of information flow in social networks: application of virtual circuits to organisational design

Network building and exchange of information by people within networks is crucial to the innovation process. Contrary to older models, in social networks the flow of information is noncontinuous and nonlinear. There are critical barriers to information flow that operate in a probabilistic manner. New models and new analytic tools are needed for these systems. This paper introduces the concept of virtual circuits and draws on recent concepts of network modelling and design to introduce a probabilistic switch theory that can be described using matrices. It can be used to model multistep information flow between people within organisational networks, to provide formal definitions of efficient and balanced networks and to describe distortion of information as it passes along human communication channels. The concept of multi-dimensional information space arises naturally from the use of matrices. The theory and the use of serial diagonal matrices have applications to organisational design and to the modelling of other systems. It is hypothesised that opinion leaders or creative individuals are more likely to emerge at information-rich nodes in networks. A mathematical definition of such nodes is developed and it does not invariably correspond with centrality as defined by early work on networks.

Information Dynamics in the Networked World

We review three studies of information flow in social networks that help reveal their underlying social structure, how information spreads among them and why small world experiments work.