Information Diffusion Model Research Articles

MSA-Net: A Multi-Scale Information Diffusion Model Awaring User Activity Level

Modeling information diffusion on social networks can be used to guide the prediction and control of information propagation and improve the structure and functionality of social networks. Existing information diffusion prediction methods can predict information diffusion paths and its volume by modeling social network structure and user behavior. However, none of the existing methods take user activity level, which is proved to be critical in modeling the information diffusion process, into account, thus weaken the prediction accuracy. To solve this problem, this paper proposes a Multi-Scale Activity Network (MSA-Net) to capture topological and historical affect features for different scales and to predict the users who will be affected at a specific future timestamp with the help of user activity level. Specifically, we first learn the network representation of three scales or levels: micro-scale, meso-scale, and macro-scale, which refers to the user level, intra-community level, and inter-community level, respectively. Then, we introduce the user activity level for each user by using user degree and average number of tweets per time unit to model the individual differences of users to achieve a more accurate prediction. Extensive experiments based on real-world datasets show that MSA-Net achieves a 6.14% improvement in terms of precision, a 6.74% improvement in terms of recall metrics, a 4.26% improvement in terms of F1-score, a 3.15% improvement in terms of MAP, and a 25.78% improvement in terms of NRMSE over the best existing baseline. The code and data are available at https://github.com/tsinghua-fib-lab/MSA-Net.

Blowing Seeds across Gardens: Visualizing Implicit Propagation of Cross-Platform Social Media Posts.

Propagation analysis refers to studying how information spreads on social media, a pivotal endeavor for understanding social sentiment and public opinions. Numerous studies contribute to visualizing information spread, but few have considered the implicit and complex diffusion patterns among multiple platforms. To bridge the gap, we summarize cross-platform diffusion patterns with experts and identify significant factors that dissect the mechanisms of cross-platform information spread. Based on that, we propose an information diffusion model that estimates the likelihood of a topic/post spreading among different social media platforms. Moreover, we propose a novel visual metaphor that encapsulates cross-platform propagation in a manner analogous to the spread of seeds across gardens. Specifically, we visualize platforms, posts, implicit cross-platform routes, and salient instances as elements of a virtual ecosystem - gardens, flowers, winds, and seeds, respectively. We further develop a visual analytic system, namely BloomWind, that enables users to quickly identify the cross-platform diffusion patterns and investigate the relevant social media posts. Ultimately, we demonstrate the usage of BloomWind through two case studies and validate its effectiveness using expert interviews.

Analyzing the channels of information dissemination: Investigating abrupt transitions in resource investment.

Investment in resources is essential for facilitating information dissemination in real-world contexts, and comprehending the influence of resource allocation on information dissemination is, thus, crucial for the efficacy of collaborative networks. Nonetheless, current studies on information dissemination frequently fail to clarify the complex interplay between information distribution and resources in network contexts. In this work, we establish a resource-based information dissemination model to identify the complex interplay by examining the propagation threshold and equilibriums. We assess the model's efficacy by juxtaposing the mean-field method with Monte Carlo simulations across three author collaboration networks. In addition, we define the function of resources in information dissemination and evaluate the model's applicability using propagating threshold, time evolution, and parametric analyses. Our findings indicate that an increase in available resources accelerates and expands the distribution of information. Notably, we identify abrupt transition phenomena concerning available resources and demonstrate that the information self-learning rate and the information review rate hasten this transition, while information decline and re-diffusion rates decelerate it.

An Information Dissemination Model Based on the Rumor and Antirumor and Cognitive Game

An Information Dissemination Model Based on the Rumor and Antirumor and Cognitive Game

Dynamic analysis and optimal control of competitive information dissemination model

Information dissemination is vital to human production and life. Identifying and analyzing the modes and mechanisms of information dissemination under different conditions is conducive to playing the role of positive information and reducing the impact of negative information. Considering that multiple pieces of information coexist and conflict with each other, in this paper, a competitive information dissemination model is established and the proposed system is analyzed. The next generation matrix method is used to determine the basic reproduction number R0. The equilibrium conditions of the system are presented, and the Routh–Hurwitz stability criterion and the second method of Liapunov are used for performing stability analysis. Based on Pontryagin’s extreme value principle, the influence rate and the isolation rate of the boot mechanism are chosen as control parameters. In addition, the optimal control parameter expression of the system and optimal control system are presented. The numerical simulation shows various states and changes in information dissemination. The results presented in this work show that the competitive information suppresses other competitive information during the process of dissemination. The results presented in this paper can serve as a reference for relevant departments to manage information dissemination.

Relieving Overexposure in Information Diffusion Through a Budget Multi-stage Allocation

When information dissemination campaigns on Online Social Networking platforms are too aggressive, this can easily cause information overexposure. Overexposure can break through the psychological and physiological limits that the audience can tolerate, making it difficult for the audience to obtain reasonable cognitive concepts. For example, the overexposure of information referring to an object in a promotional campaign can lead to inflated expectations in individuals. Building on this, we introduce two indicators for individuals’ expectations and actual utility of the object and design a multi-stage triggered mechanism for seed individuals to explore the relieving overexposure problem in information diffusion. We build a multi-stage information diffusion model and characterize the evolution of individual expectations. We verify the hardness result of the relieving overexposure problem by budget multi-stage allocation, and the non-submodularity and non-monotonicity of the objective function. Addressing the non-monotonic and non-submodular set function, we provide a direct influence-oriented algorithm with a greedy approach. Extensive experiments are performed on four real networks to explore how model parameters and network properties affect the effects of multi-stage triggered strategies for seed individuals. Using the experiments, we found that the seed individual multi-stage incremental triggered strategy of dissemination campaign of information referring to an object shows better performance, and the lower the actual utility of the specific object, the more accurate the promotion strategy needs to be developed.

The Development of Audience Participatory Information Dissemination in the New Media Environment

With the rapid development of modern information technology and the continuous improvement of China's overall capabilities, the information era has truly arrived. In this context, various development mechanisms continue to promote the prosperity of the new media industry in China. Currently, new media dissemination has become the primary method of information dissemination in today's social market and has formed a well-established dissemination system. In the era of new media dissemination, the core feature of information dissemination mainly involves publishing the same type of information through multiple channels, including individuals who represent short videos and can become news information publishers. This approach significantly enhances the speed of information transmission and expands its coverage. This method of dissemination is significantly different from traditional information dissemination models, allowing the audience to better participate in the dissemination process. Therefore, this paper provides a detailed analysis of the diversification of information dissemination and the personalized characteristics of platforms in the new media environment, followed by an in-depth analysis of the impact of audience participation in information dissemination. The aim is to better promote the development of new media dissemination and audience participatory information dissemination.

IDEEA: information diffusion model for integrating gene expression and EEG data in identifying Alzheimer’s disease markers

Abstract Understanding the genetic components of Alzheimer’s disease (AD) via transcriptome analysis often necessitates the use of invasive methods. This work focuses on overcoming the difficulties associated with the invasive process of collecting brain tissue samples in order to measure and investigate the transcriptome behavior of AD. Our approach called IDEEA (Information Diffusion model for integrating gene Expression and EEG data in identifying Alzheimer’s disease markers) involves systematically linking two different but complementary modalities: transcriptomics and EEG data. We preprocess these two data types by calculating the spectral and transcriptional sample distances, over 11 brain regions encompassing 6 distinct frequency bands. Subsequently, we employ a genetic algorithm approach to integrate the distinct features of the preprocessed data. Our experimental results show that
IDEEA converges rapidly to local optima gene subsets, in fewer than 250 iterations. Our algorithm identifies novel genes along with genes that have previously been linked to AD. It is also capable of detecting genes with transcription patterns specific to individual EEG bands as well as those with common patterns among bands. In particular, the alpha2 (10-13 Hz) frequency band yielded 8 AD associated genes out of the top 100 most frequently selected genes by our algorithm, with a p-value of
0.05. Our method not only identifies AD-related genes but also genes that interact with AD genes in terms of transcription regulation. We evaluated various aspects of our approach, including the genetic algorithm performance, band-pair association and gene interaction topology. Our approach reveals AD-relevant genes with transcription patterns inferred from EEG alone, across various frequency bands, avoiding the risky brain tissue collection process. This is a significant advancement toward the early identification of AD using non-invasive EEG recordings.

Communication dynamics of congestion warning information considering the attitudes of travelers

Traffic congestion is a serious problem faced by many cities worldwide today. Congestion warning information is one of the important influencing factors of urban road congestion; To this end, based on the dynamics of infectious diseases, a congestion warning information dissemination model considering the attitudes of travelers and the network structure was constructed. The existence and stability of the equilibrium points of non congestion warning information and congestion warning information in the model were analyzed, and the optimal control strategy of the model was proposed. Numerical simulation was conducted to verify the results of theoretical analysis, simulate and analyze the impact of changes in various parameters in the model on the dissemination of congestion warning information, and perform sensitivity analysis on several parameters. The results indicate that travelers are more inclined towards “fast” modes of transportation and have a stronger willingness to share congestion warning information. The dissemination range of warning information is wider, which can play a positive role in reducing traffic congestion pressure.

Is disinformation more likely to spread? A fuzzy-set qualitative comparative analysis of emerging infectious diseases on China’s short video platform

ObjectivesThis study aimed to develop a model for the dissemination of information on emerging infectious diseases (EIDs) by identifying the specific features of mpox (monkeypox)-related short video content that encourages...

Hypergraph-Based Influence Maximization in Online Social Networks

Influence maximization in online social networks is used to select a set of influential seed nodes to maximize the influence spread under a given diffusion model. However, most existing proposals have huge computational costs and only consider the dyadic influence relationship between two nodes, ignoring the higher-order influence relationships among multiple nodes. It limits the applicability and accuracy of existing influence diffusion models in real complex online social networks. To this end, in this paper, we present a novel information diffusion model by introducing hypergraph theory to determine the most influential nodes by jointly considering adjacent influence and higher-order influence relationships to improve diffusion efficiency. We mathematically formulate the influence maximization problem under higher-order influence relationships in online social networks. We further propose a hypergraph sampling greedy algorithm (HSGA) to effectively select the most influential seed nodes. In the HSGA, a random walk-based influence diffusion method and a Monte Carlo-based influence approximation method are devised to achieve fast approximation and calculation of node influences. We conduct simulation experiments on six real datasets for performance evaluations. Simulation results demonstrate the effectiveness and efficiency of the HSGA, and the HSGA has a lower computational cost and higher seed selection accuracy than comparison mechanisms.

An agent-based model of cross-platform information diffusion and moderation

Social media platforms are highly interconnected because many users maintain a presence across multiple platforms. Consequently, efforts to limit the spread of misinformation taken by individual platforms can have complex consequences on misinformation diffusion across the social media ecosystem. This is further complicated by the diverse social structures, platform standards, and moderation mechanisms provided on each platform. We study this issue by extending our previous model of Reddit interactions and community-specific moderation measures. By adding a followership-based model of Twitter interactions and facilitating cross-platform user participation, we simulate information diffusion across heterogeneous social media platforms. While incorporating platform-specific moderation mechanisms, we simulate interactions at the user level and specify user-specific attributes. This allows practitioners to conduct experiments with various types of actors and different combinations of moderation. We show how the model can simulate the impacts of such features on discussions facilitated by Reddit and Twitter and the cross-platform spread of misinformation. To validate this model, we use a combination of empirical datasets from three U.S. political events and prior findings from user surveys and studies.

Expanding public health initiatives through community pharmacies and student pharmacists: A programmatic case study

Community pharmacies are critical to the public health infrastructure in the United States and provide reliable information for public health concerns. Public health agencies curate educational materials that community pharmacy teams can disseminate. Student pharmacists participate in experiential learning at community pharmacies which could be utilized for dissemination of these resources. The objectives of this project were to (1) design a model for dissemination of public health information at community pharmacies; and (2) evaluate both the dissemination model's reach within communities and student pharmacist learnings from engagement in the model. We engaged student pharmacists in a model to disseminate information at community pharmacies for 2 Centers for Disease Control and Prevention initiatives about Opioid Use Disorder Anti-Stigma and Antibiotic Stewardship Education. The number of pharmacies and student pharmacists who participated from 2021 from 2023 was retrospectively reviewed to demonstrate programmatic reach. A retrospective text mining of student assignments was conducted to evaluate student experiences. Descriptive statistics were used to report quantitative data. An inductive, rapid content analysis was completed for qualitative data. Across 3years, 333 student pharmacists participated. Students reached 121 community pharmacies, 139 practicing pharmacist preceptors, and over 2000 patients with education and resources. Eleven student learning points emerged from the qualitative analysis. These included learnings around opioid use disorder and antibiotic stewardship. Students also acknowledged that there are public health needs present in communities and that community pharmacy teams are well-positioned to address these needs. Engaging student pharmacists to distribute curated information from public health authorities, both to pharmacist preceptors and patients at community pharmacies, is one way to educate future pharmacists, pharmacy teams, and communities on public health priorities. Pharmacies can serve as key venues in communities for dissemination of reliable public health information.

Comprehensive Risk Assessment of Farmer Households in Flood and Earthquake Prone Areas Based on Multidimensional Information Diffusion Model

In recent years, the global practice of regional natural disaster reduction and comprehensive risk prevention has promoted the integration of multiple disciplines, and the theory and technology of comprehensive risk assessment for multiple natural disaster chains urgently need innovation. This article is based on multiple natural disaster chain scenarios and sets secondary indicators for multiple influencing factors based on the first level indicators composed of basic risk factors such as hazard factors, vulnerability of disaster bearing bodies, and disaster prevention and reduction capabilities. By using multidimensional information diffusion technology, multiple hazard factor hazard combination factors, vulnerability combination factors of disaster bearing bodies, and comprehensive disaster prevention and reduction capability combination factors reflecting various natural disaster scenarios are constructed, A household comprehensive risk assessment index system for multi-dimensional information diffusion assessment of natural disaster comprehensive risk was designed by quantifying risk factors such as the intensity of comprehensive disaster causing factors, vulnerability of disaster bearing bodies, and comprehensive disaster prevention and reduction capabilities in disaster scenarios. This paper expands the traditional information diffusion comprehensive risk assessment model, makes up for the lack of information on geographical units, establishes a multi impact factor multi dimension natural disaster risk comprehensive assessment model for multi risk factors of complex disaster scenarios with small samples, obtains the theoretical value of multi-dimensional information Diffusion model comprehensive risk assessment for complex disaster scenarios, breaks through the sample size limit of natural disaster risks, and makes up for the lack of information on small samples of small probability events, Improved the reliability and accuracy of comprehensive risk assessment for multiple natural disaster chains. Then, take the disaster scenario formed by the comprehensive risk of earthquake and flood in a place in the upper reaches of the Fujiang River basin as the analysis sample, calculate the comprehensive risk assessment value of flood and earthquake damage in the study area's micro samples (rural households in villages and towns). By comparison, it is found that the comprehensive risk value assessed by the multi-dimensional information Diffusion model has good reliability and high accuracy, which is significantly better than the traditional Kriging interpolation method and geographical weighted regression method. This article provides a comprehensive risk assessment of natural disasters at the micro household level, which can better reflect the diversity and regionality of risks, help clarify the hidden risks of rural natural disasters, and provide scientific basis for government emergency management decision-making.

Measuring the centrality of nodes in networks based on the interstellar model

Measuring the centrality of nodes in networks based on the interstellar model

Modeling the dissemination of privacy information in online social networks

AbstractDriven by the rapid development of information technology, online social networks (OSNs) have experienced a fast development in recent years, allowing increasingly more people to share and spread information over OSNs. The rapid rise of OSN platforms such as Facebook and Twitter is sufficient evidence of such development. As one type of information, privacy information can also be created and disseminated over an OSN, posing a severe threat to individual privacy. This article attempts to construct a model for disseminating privacy information in OSNs and to analyze the model by simulating the dissemination process of privacy information in OSNs. First, we establish network models that exhibit the main characteristics of OSNs. Second, by considering the factors related to social relationships, especially intimacy between users and the attention of users to the privacy subject, we derive the parameters for privacy information dissemination models in OSNs. Third, based on the theory of information dissemination dynamics, we construct a model for information dissemination that conforms to the properties of privacy information. We also present some experimental results based on the constructed model and analyze the characteristics of privacy information dissemination. Fourth, we study and verify the various properties of the model through a set of experiments. The proposed model provides the opportunity to better understand the dynamics of privacy information dissemination in OSNs and the effect of user behavior on dissemination.

IMNE: Maximizing influence through deep learning-based node embedding in social network

IMNE: Maximizing influence through deep learning-based node embedding in social network

Influence maximization for heterogeneous networks based on self-supervised clustered heterogeneous graph transformer

Influence maximization for heterogeneous networks based on self-supervised clustered heterogeneous graph transformer

Model of Information Dissemination in the Context of Reputation Formation of an Auditing Company: Official Sources or “Word of Mouth”?

The reputation of auditing firms is of paramount importance in ensuring trust and confidence in financial markets. This article investigates the information dissemination model and its impact on the reputation formation of auditing companies, focusing on the choice between official sources (formal channels, corporate communications, regulatory disclosures, etc.) and word-of-mouth mechanisms (informal channels, client recommendations, corporate gossip, etc.). The research reveals the complex dynamics between formal and informal information dissemination strategies (official sources provide trust and transparency, while word-of-mouth mechanisms offer detailed information and trust) and their implications for reputation management in the auditing industry. The relevance of this research problem lies in the critical role of auditing firms in supporting transparency and honesty in financial reporting, especially after corporate scandals and regulatory scrutiny. The primary aim of this research is to understand the relative effectiveness of different information dissemination models in shaping the reputation of auditing companies. The choice of research subject is justified by the significant influence of auditing firms on financial markets, corporate governance, and investor trust. Using VOSviewer 1.6.16 software, the article conducts a bibliometric analysis of English-language articles and conference abstracts indexed in the Scopus database from 2007 to 2023 (1177 publications) using the keywords “Reputation” and “Auditing Firm.” The analysis confirms the increasing scholarly interest in this topic and identifies 8 thematic clusters, the largest of which combines corporate, image, and social reputation with stability and consistency. The empirical part of the research involves constructing a polygamous model similar to the SIR model, which describes the behavior of three groups of subjects depending on the presence of information and actions regarding its dissemination (active, passive, neutral). The article models the intensity of changes in the number of group members considering various phenomenological parameters (e.g., intensity of communicative processes between groups, structure of social system connections, mathematical expectation of time required for a subject to transition from one group to another, etc.). The results of this research have practical implications for auditing firms, regulatory bodies, and stakeholders in the financial sector. By understanding the relative strengths and weaknesses of different information dissemination models, auditing firms can adapt their communication strategies to effectively enhance their reputation. This research contributes to the ongoing discourse on reputation management and trust-building in financial markets.

The Irrelevance of Influencers: Information Diffusion with Re-Activation and Immunity Lasts Exponentially Long on Social Network Models

Information diffusion models on networks are at the forefront of AI research. The dynamics of such models typically follow stochastic models from epidemiology, used to model not only infections but various phenomena, including the behavior of computer viruses and viral marketing campaigns. A core question in this setting is how to efficiently detect the most influential vertices in the host graph such that the infection survives the longest. In processes that incorporate re-infection of the vertices, such as the SIS process, theoretical studies identify parameter thresholds where the survival time of the process rapidly transitions from logarithmic to super-polynomial. These results contradict the intuition that the starting configuration is relevant, since the process will always either die out fast or survive almost indefinitely. A shortcoming of these results is that models incorporating short-term immunity (or creative advertisement fatigue) have not been subjected to such a theoretical analysis so far. We reduce this gap in the literature by studying the SIRS process, a more realistic model, which besides re-infection additionally incorporates short-term immunity. On complex network models, we identify parameter regimes for which the process survives exponentially long, and we get a tight threshold for random graphs. Underlying these results is our main technical contribution, showing a threshold behavior for the survival time of the SIRS process on graphs with large expander subgraphs, such as social network models.