Dynamics Of Information Diffusion Research Articles

Navigating the Web: A Study on Network Topology and Information Flow

The internet, as a complex network of interconnected nodes, presents a dynamic landscape for the dissemination and flow of information. This study delves into the intricate interplay between network topology and information propagation, aiming to elucidate the mechanisms underlying navigation through the web. By analyzing various network models and employing techniques from graph theory, we investigate how the structural characteristics of the web influence the spread of information. Furthermore, we explore the role of key factors such as node centrality, clustering, and connectivity patterns in shaping information diffusion dynamics. Through empirical analysis and computational simulations, our findings shed light on the mechanisms governing the flow of information across the web, offering insights into strategies for effective navigation and content dissemination in the digital age.

Dynamics of information diffusion and disease transmission in time-varying multiplex networks with asymmetric activity levels

While the interaction between information and disease in static networks has been extensively investigated, many studies have ignored the characteristics of network evolution. In this study, we construct a new two-layer coupling model to explore the interactions between information and disease. The upper layer describes the diffusion of disease-related information, and the lower layer represents the disease transmission. We then use power-law distributions to examine the influence of asymmetric activity levels on dynamic propagation, revealing a mapping relationship characterizing the interconnected propagation of information and diseases among partial nodes within the network. Subsequently, we derive the disease outbreak threshold by using the microscopic Markov-chain approach (MMCA). Finally, we perform extensive Monte Carlo (MC) numerical simulations to verify the accuracy of our theoretical results. Our findings indicate that the activity levels of individuals in the disease transmission layer have a more significant influence on disease transmission compared with the individual activity levels in the information diffusion layer. Moreover, reducing the damping factor can delay disease outbreaks and suppress disease transmission, while improving individual quarantine measures can contribute positively to disease control. This study provides valuable insights into policymakers for developing outbreak prevention and control strategies.

Broadcast information diffusion processes on social media networks: exogenous events lead to more integrated public discourse

Abstract Understanding information diffusion is vital to explaining the good, bad, and ugly impacts of social media. Two types of processes govern information diffusion: broadcasting and viral spread. Viral spreading is when a message is diffused by peer-to-peer social connections, whereas broadcasting is characterized by influences that can come from outside of the peer-to-peer social network. How these processes shape public discourse is not well understood. Using a simulation study and real-world Twitter data (10,155 users, 18,000,929 tweets) gathered during 2020, we show that broadcast spreading is associated with more integrated discourse networks compared to viral spreading. Moreover, discourse oscillates between extended periods of segregation and punctuated periods of integration. These results defy simple interpretations of good or bad, and instead suggest that information diffusion dynamics on social media have the capacity to disrupt or amplify both prosocial and antisocial content.

Intervention against information diffusion in static and temporal coupling networks

Information diffusion in complex networks has become quite an active research topic. As an important part of this field, intervention against information diffusion processes is attracting ever-increasing attention from network and control engineers. In particular, it is urgent to design intervention schemes for the coevolutionary dynamics between information diffusion processes and coupled networks. For this purpose, we comprehensively study the problem of information diffusion intervention over static and temporal coupling networks. First, individual interactions are described by a modified activity-driven network (ADN) model. Then, we establish a novel node-based susceptible–infected–recovered–susceptible (SIRS) model to characterize the information diffusion dynamics. On these bases, three synergetic intervention strategies are formulated. Second, we derive the critical threshold of the controlled-SIRS system via stability analysis. Accordingly, we exploit a spectral optimization scheme to minimize the outbreak risk or the required budget. Third, we develop an optimal control scheme of dynamically allocating resources to minimize both system loss and intervention expense, in which the optimal intervention inputs are obtained through optimal control theory and a forward–backward sweep algorithm. Finally, extensive simulation results validate the accuracy of theoretical derivation and the performance of our proposed intervention schemes.

Convolutional Learning on Multigraphs

Graph convolutional learning has led to many exciting discoveries in diverse areas. However, in some applications, traditional graphs are insufficient to capture the structure and intricacies of the data. In such scenarios, multigraphs arise naturally as discrete structures in which complex dynamics can be embedded. In this paper, we develop convolutional information processing on multigraphs and introduce convolutional multigraph neural networks (MGNNs). To capture the complex dynamics of information diffusion within and across each of the multigraph's classes of edges, we formalize a convolutional signal processing model, defining the notions of signals, filtering, and frequency representations on multigraphs. Leveraging this model, we develop a multigraph learning architecture, including a sampling procedure to reduce computational complexity. The introduced architecture is applied towards optimal wireless resource allocation and a hate speech localization task, offering improved performance over traditional graph neural networks.

Capturing Dynamics of Information Diffusion in SNS: A Survey of Methodology and Techniques

Studying information diffusion in SNS (Social Networks Service) has remarkable significance in both academia and industry. Theoretically, it boosts the development of other subjects such as statistics, sociology, and data mining. Practically, diffusion modeling provides fundamental support for many downstream applications (e.g., public opinion monitoring, rumor source identification, and viral marketing). Tremendous efforts have been devoted to this area to understand and quantify information diffusion dynamics. This survey investigates and summarizes the emerging distinguished works in diffusion modeling. We first put forward a unified information diffusion concept in terms of three components: information, user decision, and social vectors, followed by a detailed introduction of the methodologies for diffusion modeling. And then, a new taxonomy adopting hybrid philosophy (i.e., granularity and techniques) is proposed, and we made a series of comparative studies on elementary diffusion models under our taxonomy from the aspects of assumptions, methods, and pros and cons. We further summarized representative diffusion modeling in special scenarios and significant downstream tasks based on these elementary models. Finally, open issues in this field following the methodology of diffusion modeling are discussed.

The role of degree correlation in shaping filter bubbles in social networks

Filter bubbles shelter people from unconcerned but important information, which is a critical problem in modern online social networks. Although a quantitative model of filter bubbles is still missing, the identification and impact of filter bubbles are widely debated both at a scientific and political level. To shed light on this gap, we introduce a theoretical directed network model of filter bubbles with degree correlations and mathematically analyze information diffusion dynamics on the model. We find that the internal structure of filter bubbles can be modeled by the directed scale-free network with both negative (a node tend to possess high in-degree and low out-degree, or vice versa) and assortative (two nodes with similar degrees tend to be connected) degree correlation. Traditionally, filter bubbles are usually associated with the community structure and emphasize the sparseness of external connections to isolate the spreading of diverse information. However, the negative-assortative degree correlation shows that the filter bubble can spontaneously resist the spreading of non-preferred information (i.e., information with relatively lower transmissibility). Moreover, we study the competition epidemic of two information on the negative-assortative networks, and find that both of the information can coexist only if all nodes prefer the same information.

Anomalous role of information diffusion in epidemic spreading

The authors model the coevolution dynamics of information diffusion, resource allocation and epidemic spreading, and show that the information diffusion may worsen the epidemic spreading subject to allocations of limited resources.

COVID-19 pandemic and information diffusion analysis on Twitter.

The COVID‐19 pandemic has impacted all aspects of our lives, including the information spread on social media. Prior literature has found that information diffusion dynamics on social networks mirror that of a virus, but applying the epidemic Susceptible‐Infected‐Removed model (SIR) model to examine how information spread is not sufficient to claim that information spreads like a virus. In this study, we explore whether there are similarities in the simulated SIR model (SIRsim), observed SIR model based on actual COVID‐19 cases (SIRemp), and observed information cascades on Twitter about the virus (INFOcas) by using network analysis and diffusion modeling. We propose three primary research questions: (a) What are the diffusion patterns of COVID‐19 virus spread, based on SIRsim and SIRemp? (b) What are the diffusion patterns of information cascades on Twitter (INFOcas), with respect to retweets, quote tweets, and replies? and (c) What are the major differences in diffusion patterns between SIRsim, SIRemp, and INFOcas? Our study makes a contribution to the information sciences community by showing how epidemic modeling of virus and information diffusion analysis of online social media are distinct but interrelated concepts.

On the probabilistic modeling of fake news (hoax) persistency in online social networks and the role of debunking and filtering

Understanding the dynamics of information diffusion, including spreading of fake news, hoaxes, and generally, misinformation/disinformation, has become crucial in post‐truth societies. The paper focuses on the probability that a hoax originated at a given time will continue to spread indefinitely in online social networks; a minimalistic model based on the theory of branching processes is devised, which only considers the basic possible reactions that users can have after reading a post whose content is a hoax, that is, to share it further, to ignore it, or to try to debunk it. The analysis of the resulting dynamics shows that ignoring is indeed not sufficient to stop the spreading, not even if most people do so. More active counter‐measures are needed; in particular, the proposed model formally describes the ways in which retractions and debunking posts, cultural/educational initiatives, and content moderation policies (including filtering) by Internet companies, can impact on the persistency probability of hoaxes and generally fake news.

Maximum a Posteriori Estimation for Information Source Detection

Information source detection is to identify nodes initiating the diffusion process in a network, which has a wide range of applications including epidemic outbreak prevention, Internet virus source identification, and rumor source tracing in social networks. Although it has attracted ever-increasing attention from research community in recent years, existing solutions still suffer from high time complexity and inadequate effectiveness, due to high dynamics of information diffusion and observing just a snapshot of the whole process. To this end, we present a comprehensive study for single information source detection in weighted graphs . Specifically, we first propose a maximum a posteriori (MAP) estimator to detect the information source with other methods as the prior, which ensures our method can be integrated with others naturally. Different from many related works, we exploit both infected nodes and their uninfected neighbors to calculate the effective propagation probability , and then derive the exact formation of likelihood for general weighted graphs. To further improve the efficiency, we design two approximate MAP estimators, namely brute force search approximation (BFSA) and greedy search bound approximation (GSBA), from the perspective of likelihood approximation. BFSA tries to traverse the permitted permutations to directly compute the likelihood, but GSBA exploits a strategy of greedy search to find a surrogate upper bound of the likelihood, and thus avoids the enumeration of permitted permutations. Therefore, detecting with partial nodes and likelihood approximation reduces the computational complexity drastically for large graphs. Extensive experiments on several data sets also clearly demonstrate the effectiveness of our methods on detecting the single information source with different settings in weighted graphs.

Modeling Social Contagion and Information Diffusion in Complex Socio-Technical Systems

The rise of technology and social media has altered the human cognition, and we must rethink our approach toward information dissemination systems when dealing with topics such as social campaigns, advertising, false news outbreak, and more. In this article, we start by providing an overview of classical information spread dynamics using various macroscopic models, including the famous Maki–Thompson model. Building on these, we propose and design context-aware modeling frameworks capable of capturing specific scenarios in online social media information spread. We propose four context-aware macroscopic models capable of capturing the dynamics of information diffusion for a specific context. We also present stochastic versions of these models. Case studies using real Twitter data, along with an algorithm to construct ignorant–spreader–recovered (ISR) groups are presented to validate the proposed models.

A review on graphical evolutionary game for information diffusion on social networks

Purpose With the recent development of science and technology, research on information diffusion has become increasingly important. Design/methodology/approach To analyze the process of information diffusion, researchers have proposed a framework with graphical evolutionary game theory (EGT) according to the theory of biological evolution. Findings Through this method, one can study and even predict information diffusion. Originality/value This paper summarizes three existing works using graphical EGT to discuss how to obtain the static state and the dynamics of information diffusion in social network.

Timing Matters: Influence Maximization in Social Networks Through Scheduled Seeding

One highly studied topic in the field of social networks is the search for influential nodes that, when seeded (i.e., activated intentionally), may further activate a large portion of the network through a viral contagion process. Indeed, various mathematical models were proposed in the literature to characterize the dynamics of such diffusion processes, and different solutions were suggested for maximizing influence under such models. However, most of these solutions focused on selecting a set of nodes to be seeded at the initial phase of the diffusion process. This paper suggests a scheduled seeding approach that aims at finding not only the best set of nodes to be seeded but also the right timing to perform these seedings. More specifically, we identify three different properties of existing contagion models that can be utilized by a scheduled approach to improve the total number of activated nodes: 1) stochastic dynamics; 2) diminishing social effect; and 3) state-dependent seeding. By analyzing each of these properties separately, we demonstrate the advantages of the scheduled seeding approach over the traditional initial seeding approach, both by theoretical and empirical evaluation. Our analysis presents an improvement of 10%–70% in the final number of infected nodes when using the scheduled seeding approach. Our findings have the potential to open up a new area of research, focusing on finding the right timing for seeding actions, thereby helping both in improving our understanding of information diffusion dynamics and in devising better strategies for influence maximization.

Active influence spreading in social networks

Identifying the most influential spreaders is an important issue for the study of the dynamics of information diffusion in complex networks. In this paper we analyze the following spreading model. Initially, a few nodes know a piece of information and are active spreaders of it. At subsequent rounds, spreaders communicate the information to their neighbors. Upon receiving the information, a node becomes aware of it but does not necessarily become a spreader; it starts spreading only if it gets the information from a sufficiently large number of its neighbors. A set of initial spreaders that guarantees that all the nodes become aware of the information is called a perfect seed set. We study the problem of choosing a perfect seed set of minimum size. We provide hardness results and show that the problem becomes tractable on trees. In case of general graphs, we provide an efficient algorithm and validate its effectiveness (in terms of the solution size) on real-life networks. We also study perfect seed sets in dense graphs and derive a bound on the size of a dominating set in Ore graphs.

Sentiment Based Information Diffusion in Online Social Networks

This article describes how social media has emerged as a main vehicle of information diffusion among people. They often share their experience, feelings and knowledge through these channels. Some pieces of information quickly reach a large number of people, while others not. The authors analyzed this variation by collecting tweets on 2016 U.S. presidential election. This article gives a comprehensive understanding of how sentiment encoded in the textual contents can affects the information diffusion, along with the effect of content features, i.e., URLs, hashtags, and contextual features, i.e., number of followers, followees, tweets generated by the user so far, account age, tweet age. In order to explore the relationship between sentiment content and information diffusion, the authors first checked the features' significance as an indicator of diffusibility by using random forests. Finally, support vectors and k-Neighbors regression models are used to capture the complete dynamics of information diffusion. Experiments and results clearly reveal that sentiment prominently helps in making a better prediction of information diffusion.

Modelling and simulation of misconduct information diffusion in web forum

Using the method of analogy, this paper built the social information field, information field force model and the information diffusion dynamics model to study the dynamic mechanism and the law of the movement regarding how misconduct information moves among nodes in the web forum. It also constructed the web forum misconduct information diffusion complex network simulation model to study the diffusion intensity of misconduct information and its influencing factors. The conclusion is that, under the force of the field, the information flows from the high potential node to the low potential node, during which resistance is generated inside and outside the diffusion channel. In the complex network of the web forum, the diffusion intensity of misconduct information displays an increasing trend as the possibility of reconnection among broken nodes becomes higher. The main factor that determines the diffusion intensity of the misconduct information is the average shortest path. It also increases when the interaction frequency turns higher.

Information spreading in complex networks with participation of independent spreaders

Information diffusion dynamics in complex networks is often modeled as a contagion process among neighbors which is analogous to epidemic diffusion. The attention of previous literature is mainly focused on epidemic diffusion within one network, which, however neglects the possible interactions between nodes beyond the underlying network. The disease can be transmitted to other nodes by other means without following the links in the focal network. Here we account for this phenomenon by introducing the independent spreaders in a susceptible–infectious–recovered contagion process. We derive the critical epidemic thresholds on Erdős–Rényi and scale-free networks as a function of infectious rate, recovery rate and the activeness of independent spreaders. We also present simulation results on ER and SF networks, as well as on a real-world email network. The result shows that the extent to which a disease can infect might be more far-reaching, than we can explain in terms of link contagion only. Besides, these results also help to explain how activeness of independent spreaders can affect the diffusion process, which can be used to explore many other dynamical processes.

Evidence of complex contagion of information in social media: An experiment using Twitter bots.

It has recently become possible to study the dynamics of information diffusion in techno-social systems at scale, due to the emergence of online platforms, such as Twitter, with millions of users. One question that systematically recurs is whether information spreads according to simple or complex dynamics: does each exposure to a piece of information have an independent probability of a user adopting it (simple contagion), or does this probability depend instead on the number of sources of exposure, increasing above some threshold (complex contagion)? Most studies to date are observational and, therefore, unable to disentangle the effects of confounding factors such as social reinforcement, homophily, limited attention, or network community structure. Here we describe a novel controlled experiment that we performed on Twitter using ‘social bots’ deployed to carry out coordinated attempts at spreading information. We propose two Bayesian statistical models describing simple and complex contagion dynamics, and test the competing hypotheses. We provide experimental evidence that the complex contagion model describes the observed information diffusion behavior more accurately than simple contagion. Future applications of our results include more effective defenses against malicious propaganda campaigns on social media, improved marketing and advertisement strategies, and design of effective network intervention techniques.

Information Diffusion Model and Hadoop Implementation Research on Online Social Network

Social networks are currently gotten increasing focus with the growth of online social network services, such as Facebook, Sina Weibo. One major challenge for social network research is how to build information diffusion model to describe the complex information diffusion model. For this purpose, information diffusion models are researched. To show the importance of network topology, how to identify the most important spreaders in the network is a key scientific problem. Different kinds of network centrality measure methods are analyzed. The role of a potential edge in social network is analyzed. The relationship between network topology structure and information diffusion dynamics model is analyzed. Pagerank algorithm for SinaWeibo users are implemented in Hadoop cloud computing platform. Experiment result shows that Pagerank algorithm can be implemented in Hadoop cloud platform to recognize the important spreaders in social network. Big data in social network can be analyzed in the platform.