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Nowadays, organizations use viral marketing strategies to promote their products through social networks. A graph represents the social networks. It is expensive to directly send the product promotional information to all the users in the network. In this context, as reported by Kempe et al. (in: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003) introduced the Influence Maximization (IM) problem, which identifies k most influential nodes (spreader nodes) such that the maximum number of people in the network adopt the promotional message. In this work, we propose a maximization version of PAP called the Interest Maximization problem. Different people have different levels of interest in a particular product. This is modeled by assigning an interest value to each node in the network. Then, the problem is to select k initial spreaders such that the sum of the interest values of the people (nodes) who become aware of the message is maximized. We study the Interest Maximization problem under two popular diffusion models: the Linear Threshold Model (LTM) and the Independent Cascade Model (ICM). We show that the Interest Maximization problem is NP-Hard under LTM. We give linear programming formulation for the problem under LTM. We propose four heuristic algorithms for the Interest Maximization problem: Level Based Greedy Heuristic (LBGH), Maximum Degree First Heuristic (MDFH), Profit Based Greedy Heuristic (PBGH), and Maximum Profit Based Greedy Heuristic (MPBGH). Extensive experimentation has been carried out on many real-world benchmark data sets for both diffusion models. The results show that among the proposed heuristics, MPBGH performs better in maximizing the interest value. [ABSTRACT FROM AUTHOR]

Everyone has a different level of interest in a trending topic posted on social media, which may affect user's behaviour. In order to find out the way it affects the process of information transmission, we construct an interest intensity-based susceptible-forwarding-immune $\left({I - SFI} \right)$ I − SFI propagation dynamic model and two parameters birth rate $ A$ A and death rate $ \mu $ μ are introduced to represent the users who newly join the group of disseminated information and the users who leave this population. And we give different birth rates to people with various levels of interest, which helps us to determine the interest intensity of potential users to a certain extent. We use the forwarding data of the real topic on Chinese Sina-microblog for data fitting, which can accurately parameterize the model and quantify the impact of interest intensity. And sensitivity analyses also give some strategies for increasing the impact of information dissemination process from the perspective of interest intensity. [ABSTRACT FROM AUTHOR]

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The network topology structure based influence maximization algorithms are greatly influenced by the network structure, which leads to unstable performance of social networks of different scales and different topology structures. In view of this problem, a improved Transformer model based social network influence maximization algorithm was proposed. Firstly, the high influential nodes of the society network were selected based on the k-shell decomposition method. Seconcly, the topology structure information and connection framework information of the candidate nodes were discovered by use of the random walk strategy. Finally, the Transformer model was improved, in order to support scalable node feature sequences, and the improved Transformer model was taken advantage to predict the seed nodes of the social network. Validation experiments were carried on six real social networks of different scales. The results show that the proposed algorithm realizes a good influence maximization performance on social networks of different scales and topology structures, and the time efficiency of the seed node recognition has been increased significantly.

The editorial in "Frontiers in Physics" discusses the use of compartmental models in understanding social interactions, information spread, and behavioral dynamics beyond traditional epidemiological contexts. The article highlights the historical context and theoretical framework of compartmental models, their applications in various fields, and recent advancements in incorporating network topology. It also features articles on forecasting infections using graph neural networks, competitive information propagation in online social networks, dynamic network representation for rumor propagation, and the impact of assortative mixing on epidemic spread. The research emphasizes the importance of network structure in shaping epidemic dynamics and social behavior, inviting further exploration into interconnected systems for a deeper understanding of public health and information dissemination. [Extracted from the article]

In the era of big data, the propagation of malicious software poses a significant threat to corporate data security. To safeguard data assets from the encroachment of malware, it is essential to conduct a dynamic analysis of various information propagation behaviors within software. This paper introduces a dynamic analysis detection method for malicious behavior based on feature extraction (MBDFE), designed to effectively identify and thwart the spread of malicious software. The method is divided into three stages: First, variable-length N-gram algorithms are utilized to extract subsequences of varying lengths from the sample APl call sequences as continuous dynamic features. Second, feature selection techniques based on information gain are employed to identify suitable classification features. Lastly, recurrent neural networks (RNN) are applied for the classification training and prediction of diverse software behaviors. Experimental results and analysis demonstrate that this approach can accurately detect and promptly interrupt the information dissemination of malicious software when such behavior occurs, thereby enhancing the precision and timeliness of malware detection. [ABSTRACT FROM AUTHOR]

In recent years, with the emergence of various brand marketing platforms and the rapid development of social networks, brand marketing information has exploded. If the propagation of brand marketing information in social networks is moderate, it can be effectively communicated within a certain period of time. However, excessive dissemination often has a counterproductive effect, resulting in a backlash. What are the backlash mechanism and the propagation dynamics characteristics during the dissemination of brand marketing information? Based on this, this paper proposes a model named I-SIRI, which conducts a thorough study on the backlash mechanism and its regularities and characteristics in the dissemination of brand marketing information. Taking the dissemination of Xiaomi SU7 brand marketing information on a social platform as an example, the reliability of the model is verified. The results found that content homogeneity, consumer participation, and information authenticity have significant impacts on the backlash mechanism of brand marketing information dissemination in social networks. The threshold for the backlash state of brand marketing information dissemination is closely related to the number of transmissions. This research not only helps us better understand the role of the backlash mechanism in the dissemination of brand marketing information, but also provides valuable references for brands in predicting and controlling the dissemination of marketing information, facilitating brands to better formulate and implement marketing strategies. [ABSTRACT FROM AUTHOR]

Social networks, functioning as core platforms for modern information dissemination, manifest distinctive user clustering behaviors and state transition mechanisms, thereby presenting new challenges to traditional information propagation models. Based on hypergraph theory, this paper augments the traditional SEIR model by introducing a novel hypernetwork information dissemination SSEIR model specifically designed for online social networks. This model accurately represents complex, multi-user, high-order interactions. It transforms the traditional single susceptible state ( S ) into active ( S a ) and inactive ( S i ) states. Additionally, it enhances traditional information dissemination mechanisms through reaction process strategies (RP strategies) and formulates refined differential dynamical equations, effectively simulating the dissemination and diffusion processes in online social networks. Employing mean field theory, this paper conducts a comprehensive theoretical derivation of the dissemination mechanisms within the SSEIR model. The effectiveness of the model in various network structures was verified through simulation experiments, and its practicality was further validated by its application on real network datasets. The results show that the SSEIR model excels in data fitting and illustrating the internal mechanisms of information dissemination within hypernetwork structures, further clarifying the dynamic evolutionary patterns of information dissemination in online social hypernetworks. This study not only enriches the theoretical framework of information dissemination but also provides a scientific theoretical foundation for practical applications such as news dissemination, public opinion management, and rumor monitoring in online social networks. [ABSTRACT FROM AUTHOR]

The information propagation on the social network has been an important research topic, with a focus on the significant influence of individual behaviors. The marginal utility behavior can affect the process of information propagation. But most previous research ignore it. Besides, the process can also be influenced by limited-contact capacity, which increase the complexity of networks. In this paper, the marginal utility behavior model on the single-layer network with limited-contact capacity is proposed first. Then the edge-based compartmental (EBC) method is used to explore the novel information propagation mechanism. Through experiments, it was found that when individuals show an increasing marginal utility behavior, with the propagation probability increasing, the final spreading scope shows a discontinuous increase pattern by weakening behavior. However, the final spreading scope shows no outbreak by strengthening behavior. In contrast, when individuals show a diminishing marginal utility behavior, with the propagation probability increasing, the final spreading scope shows a continuous increase pattern by strengthening. Nevertheless, the final spreading scope shows a discontinuous increasing by weakening. What's more, the limited-contact capacity and the degree distribution heterogeneity can also change the information propagation pattern. Besides, the experimental results are in agreement with the theoretical results. [ABSTRACT FROM AUTHOR]

Aiming at the dissemination of malicious information on online social networks, this paper proposes a new positive–negative information propagation model that incorporates the conformist psychological factors of communicators during the dissemination process. Firstly, the local and global stability of information-free equilibrium is analyzed and three types of information-spread equilibria are obtained. The persistence of information spreading is expounded in detail. Secondly, two different control strategies are designed to suppress the spread of negative information on online social networks. One is the real-time optimal control with minimal control cost that enables the negative information to be effectively controlled in the expected time by executing two collaborative intervention methods, and the other is the event-triggered impulsive control, in which the control instant is determined by an event-triggered function. Finally, the above theoretical results are verified by some numerical simulations and a practical example. [ABSTRACT FROM AUTHOR]

Noting that the gap between individual communication abilities is amplified in the network era, we classify spreaders into super-spreaders and ordinary spreaders by the Pareto principle. In addition, when facing negative information, individuals with higher knowledge levels will recognize it. Based on these, in this study, an improved Ignorants-Hesitant-Super-spreaders-Ordinary spreaders-Wise-Removers (IEASWR) information propagation model was developed. Meanwhile, in view of the gradual saturation, density dependence and nonlinear relationship of the propagation process, the Holling-type II functional response function is introduced to information propagation modeling for the first time making it more realistic. First, the basic reproduction number ℛ 0 is deduced. Then, the global asymptotic stability of the information-free/prevailing equilibria is proved by constructing the Lyapunov function. Furthermore, using the Pontryagin maximum principle, a real-time optimization guidance measure is proposed to achieve the correct guidance of negative information within the expected time. Finally, the validity of the theory is verified through numerical simulation. [ABSTRACT FROM AUTHOR]

The interplay between mass injection, heat release, and boundary layer development plays a key role in dictating the dynamics and stability of confined supersonic flows. The relative impacts of these factors and the timescales over which they influence the upstream and downstream flow can provide critical insights into how different operating modes develop. As such, this work presents a series of simulations of an experimental axisymmetric direct connect flowpath. The mass flow rates and chemical compositions of the injection stages are varied, and subsequent information propagation and mode transitions are analyzed using spatiotemporal correlations of cross-sectional averaged quantities. Increasing the injection flow rate decreases the time lags and durations of positive correlations between pressure and heat release at various points along the flowpath. Meanwhile, in dual-mode cases with lower injection flow rates, these correlations develop after longer time delays and persist for a longer times, illustrating how information propagates more gradually in these scenarios. Over the full flowpath, positive correlations persist for comparatively long times between (1) the upstream isolator pressure and the pressure elsewhere, and (2) the pressure in the downstream diverging combustor section and the upstream pressure. As such, the influence of the pressure in the intermediate constant-area combustor section decays more rapidly. Conditional statistics suggest that flow blockage and pressurization from the injected mass reduce the local ignition delay, thereby facilitating increased pressurization via heat release in a positive feedback loop.

Within the context of contemporary society, the propagation of information is often subject to the influence of inter-individual connectivity, and individuals may exhibit divergent receptive attitudes towards identical information, a phenomenon denoted as the Hesitant-Common (HECO) trait. In light of this, the present study initially constructs a propagation network model devoid of correlation configurations to investigate the HECO characteristics within weighted social networks. Subsequently, the study employs a theoretical framework for edge partitioning, predicated on edge weights and HECO traits, to quantitatively analyze the mechanisms of individual information dissemination. Theoretical analyses and simulation outcomes consistently demonstrate that an augmentation in the proportion of common individuals facilitates both the diffusion and adoption of information. Concurrently, a phase transition crossover is observed, wherein the growth pattern of the ultimate adoption range, denoted as R(∞), transitions from a first-order discontinuous phase transition to a second-order continuous phase transition as the proportion of common individuals increases. An escalation in the weight distribution exponent is found to enhance information propagation. Furthermore, a reduction in the heterogeneity of degree distribution is conducive to the spread of information. Conversely, an increase in degree distribution heterogeneity and a diminution in the collective decision-making capacity can both exert inhibitory effects on the propagation of information. [ABSTRACT FROM AUTHOR]

In the spreading dynamics of previous fashion trends, adoption researchers have neglected to consider that some individuals may behave differently from popular tendencies, which is called opposite-trend adoption behavior. To explore the dissemination mechanisms of the behavior, we first establish the adoption- against-trend model. Additionally, an edge division theory based on the adoption of opposite trends was proposed to quantitatively analyze this unique dissemination mechanism. This study presents three different degrees of opposite trends, each highlighting unique spreading scenarios. In the case of a strong opposite trend, no spreading occurs. In the case of a weak opposite trend, limited contact will accelerate information spreading, but it will not alter the mode of spreading. Nevertheless, in the case of a moderately opposite trend, the degree of the opposite trend alters the mode of spreading. Meanwhile, a cross- phase transition occurs. The findings of this paper can be applied to various areas, including social media and commercial trades. [ABSTRACT FROM AUTHOR]

In real network systems, there are numerous information propagation phenomena. However, the impact of homogeneity on information propagation is still not well understood. In this paper, we introduce a dynamic information propagation model named H-SIR. We also incorporate the propagation mechanism of content homogeneity and define the dynamic propagation rate based on users' interest locking and fatigue level. Through simulation experiments, we demonstrate how homogeneous information spreads and evolves on the network. Furthermore, we compare the model with real data of COVID-19 information on the Sina-Weibo social platform and the traditional SIR model to jointly validate our model. Our results show that in social networks, if the themes of homogeneous information are diverse, even with an increasing amount of homogeneous information, it will hardly affect the propagation of this type of information. However, if users are recommended excessive homogeneous information, they will not focus entirely but instead display selective contact behavior. In the case of information fatigue, a type of information with a low degree of homogeneity will survive longer than a type of information with a high degree of homogeneity. Finally, we propose corresponding strategies to alleviate the phenomenon of information homogenization in social networks. [ABSTRACT FROM AUTHOR]

In the era of big data, the propagation of malicious software poses a significant threat to corporate data security. To safeguard data assets from the encroachment of malware, it is essential to conduct a dynamic analysis of various information propagation behaviors within software. This paper introduces a dynamic analysis detection method for malicious behavior based on feature extraction (MBDFE), designed to effectively identify and thwart the spread of malicious software. The method is divided into three stages: First, variable-length N-gram algorithms are utilized to extract subsequences of varying lengths from the sample APl call sequences as continuous dynamic features. Second, feature selection techniques based on information gain are employed to identify suitable classification features. Lastly, recurrent neural networks (RNN) are applied for the classification training and prediction of diverse software behaviors. Experimental results and analysis demonstrate that this approach can accurately detect and promptly interrupt the information dissemination of malicious software when such behavior occurs, thereby enhancing the precision and timeliness of malware detection.

Tree-like structures, characterized by hierarchical relationships and power-law distributions, are prevalent in a multitude of real-world networks, ranging from social networks to citation networks and protein–protein interaction networks. Recently, there has been significant interest in utilizing hyperbolic space to model these structures, owing to its capability to represent them with diminished distortions compared to flat Euclidean space. However, real-world networks often display a blend of flat, tree-like, and circular substructures, resulting in heterophily. To address this diversity of substructures, this study aims to investigate the reconstruction of graph neural networks on the symmetric manifold, which offers a comprehensive geometric space for more effective modeling of tree-like heterophily. To achieve this objective, we propose a graph convolutional neural network operating on the symmetric positive-definite matrix manifold, leveraging Riemannian metrics to facilitate the scheme of information propagation. Extensive experiments conducted on semi-supervised node classification tasks validate the superiority of the proposed approach, demonstrating that it outperforms comparative models based on Euclidean and hyperbolic geometries. [ABSTRACT FROM AUTHOR]

The problem of how to attract a robust following on social media is one of the most pressing for influencers. We study a common practice on popular microblogging platforms such as Twitter, of influencers' expanding their followings by running lotteries and giveaways. We are interested in how the lottery size and the seeding decisions influence the information propagation and the final reward for such a campaign. We construct an information-diffusion model based on a random graph, and show that the market demand curve of the lottery reward via the promotion of the social network is "S"-shaped. This property lays a foundation for finding the optimal lottery size. Second, we observe that (i) dynamic seeding could re-stimulate the spread of information and (ii) with a fixed budget, seeding at two fixed occasions is always better than seeding once at the beginning. This observation motivates us to study the joint optimization of lottery size and adaptive seeding. We model the adaptive seeding problem as a Markov Decision Process. We find the monotonicity of the value functions and trends in the optimal actions, and we show that with adaptive seeding, the reward curve is approximately "S"-shaped with respect to the lottery size. [ABSTRACT FROM AUTHOR]

As a popular graph learning technique, graph neural networks (GNN) show great advantages in the field of personalized recommendation. Existing GNN-based recommendation methods organized user-item interactions (e.g., click, purchase, review, etc.) as the bipartite graph and captured the higher-order collaborative signal with the aid of the GNN to achieve personalized recommendation. However, there exists two limitations in existing studies. First, core features activating user-item interactions were not be identified, which causes that user-item interactions fail to be accurately exploited at the feature level. Second, existing GNNs ignored the mutual association among neighbors in information propagation, which results in structural signal in the bipartite graph not being sufficiently captured. Towards this end, we developed the core features activated graph dual-attention network, namely C-GDN, for personalized recommendation. Specifically, C-GDN firstly identifies core user and item features activating user-item interactions and employs these core features to initialize the bipartite graph, which effectively optimizes the utilizing of user-item interactions at the feature level. Furthermore, C-GDN designs a novel graph dual-attention network to conduct information propagation, which captures more sufficient structural signal in the bipartite graph by considering information from neighbors as well as their mutual association. Extensive experiments on three benchmark datasets shows that C-GDN outperforms state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

In reality, the dissemination of information about COVID-19 vaccines typically involves a combination of opinion leaders and self-organizing networks, with each node being exposed to information in varying ways. However, conventional models often assume homogeneity in networks, treating all nodes as equal in terms of propagation probabilities within a fixed timeframe, thereby neglecting the inherent heterogeneity of social networks in information dissemination. To address this limitation, we propose a novel semi-directed network model, referred to as the susceptible-forwarding-immune model, which incorporates the complex structure of actual social networks and classifies nodes based on their mode of contact and the number of users they reach within a specific period. We calibrated and validated our model using real data on COVID-19 vaccine information from the Chinese Sina microblog, and our sensitivity analysis yielded insights into optimal strategies for disseminating such information. [ABSTRACT FROM AUTHOR]

Online social networks have revolutionized communication, providing individuals with platforms to express their personal opinions on diverse topics. Researchers have independently explored information propagation and opinion evolution within complex networks. However, these phenomena exhibit interconnectedness, where information dissemination influences opinion evolution and vice versa. To address challenges in complex network modeling and opinion‐information coupling, internal and external factors are considered in public opinion scenarios by incorporating the crowd effect, enhancement effect, and evolutionary game theory. The susceptible‐latent‐forwarding‐immune‐Jager‐Amblard (SLFI‐JA) model is presented by modifying the SLFI propagation dynamics model and the JA opinion dynamics model, enabling the integration of information propagation and opinion evolution at the microlevel. Through analyzing real‐world social hotspots on Sina Weibo, case studies and comparative analyses are conducted to validate the rationality and effectiveness of the proposed model. Furthermore, the findings identify key factors influencing public opinion dissemination and group opinion evolution, offering valuable insights to relevant departments in public opinion response and management. The study aims to mitigate the harmful effects of negative public opinions, prevent extreme adverse online events, and foster a healthier online environment. [ABSTRACT FROM AUTHOR]

In the spreading dynamics of previous fashion trends, adoption researchers have neglected to consider that some individuals may behave differently from popular tendencies, which is called opposite-trend adoption behavior. To explore the dissemination mechanisms of the behavior, we first establish the adoption-against-trend model. Additionally, an edge division theory based on the adoption of opposite trends was proposed to quantitatively analyze this unique dissemination mechanism. This study presents three different degrees of opposite trends, each highlighting unique spreading scenarios. In the case of a strong opposite trend, no spreading occurs. In the case of a weak opposite trend, limited contact will accelerate information spreading, but it will not alter the mode of spreading. Nevertheless, in the case of a moderately opposite trend, the degree of the opposite trend alters the mode of spreading. Meanwhile, a cross-phase transition occurs. The findings of this paper can be applied to various areas, including social media and commercial trades.

With the rise of social networks, various types of information have emerged in the vision field in a complex manner, making it crucial to analyze the propagation patters of online public opinion to effectively guide information dissemination. To elucidate the dynamics of information dissemination, this paper proposes a directed network information based on self-organized network information dissemination scenario. This model takes into account the influence of networks formed by users spontaneously and distinguishes the dissemination population based on the in- and out-degree of user nodes in the network. To assess the model’s performance, it is evaluated using real retweets from Chinese Sina Weibo, considering the impact of user interactions on information dissemination. Comparing real data with model-fitted data, the proposed model-based evaluation and numerical analysis demonstrate that the forwarding and transfer probabilities align with actual information dissemination. Furthermore, the evaluation sensitivity analyses describe the key factors influencing information dissemination, aiding decision-making in formulating strategies to guide public opinion. To quantify the importance of these factors, assessment metrics are introduced, such as the propagation regeneration number.

A reliability demonstration test (RDT) plays a critical role in safeguarding product reliability and making sure it meets the target requirement. When planning an RDT, the test planning parameters are determined before executing the RDT. There is uncertainty associated with the test result and whether the product will be acceptable and released into the market with additional costs resulting from the warranty service or whether a reliability growth process is needed to further improve the product's reliability. Potentially, such a process could be repeated multiple times depending on how quickly the reliability growth process can improve product reliability. Existing RDT designs primarily consider the cost of RDT itself or over a single demonstration stage before the next possible RDT, and hence fail to fully address the uncertainty of all possible future RDTs and various pathways a product may go through in a multi-stage demonstration process. By focusing on binomial RDT (BRDT) plans based on failure count data, this paper proposes an optimal Bayesian BRDT design framework by explicitly quantifying the multi-stage acceptance uncertainties resulting from current and subsequent BRDTs. It allows the BRDT planning decision to be determined more holistically by anticipating the costs of warranty service and reliability growth along different pathways over multiple stages. A recursive information propagation algorithm is proposed to incorporate the prior belief of product reliability and allow it to evolve and update over multiple stages of BRDT. A case study is presented to illustrate the proposed multi-stage Bayesian BRDT design framework and demonstrate its cost-efficiency compared to existing strategies. A comprehensive sensitivity analysis is also provided to demonstrate the impact of the relative size of different cost components, reliability growth rate, and prior setting on the performance of the proposed method. [ABSTRACT FROM AUTHOR]

With the development of Internet technology, social media has gradually become an important platform where users can express opinions about hot events. Research on the mechanism of public opinion evolution is beneficial to guide the trend of opinions, making users' opinions change in a positive direction or reach a consensus among controversial crowds. To design effective strategies for public opinion management, we propose a dynamic opinion network susceptible-forwarding-immune model considering environmental factors (NET-OE-SFI), which divides the forwarding nodes into two types: support and opposition based on the real data of users. The NET-OE-SFI model introduces environmental factors from infectious diseases into the study of network information transmission, which aims to explore the evolution law of users' opinions affected by the environment. We attempt to combine the complex media environmental factors in social networks with users' opinion information to study the influence of environmental factors on the evolution of public opinion. Data fitting of real information transmission data fully demonstrates the validity of this model. We have also made a variety of sensitivity analysis experiments to study the influence of model parameters, contributing to the design of reasonable and effective strategies for public opinion guidance.

The spread of rumors may have adverse effects and thus need to be controlled at the earliest possible time to reduce the scale of the impact. It is also desirable that the process be low-cost. Therefore, the rumor containment problem should be treated as a multi-objective optimization problem that tries to maximize the number of individuals protected from the rumor while minimizing containment time and cost. However, the existing works have treated it as a single-objective optimization problem under time and cost constraints. Imposing predetermined constraints may not always be feasible and not wise as a slight relaxation in any of the constraints may lead to a more preferable trade-off. In this paper, the major contribution is to offer an adaptive solution based on the choice of a decision-maker through a well-defined preference injection process embedded in the proposed multi-objective PSO-based algorithm. Moreover, in contrast to the existing works, the proposed work considers the spread of positive as well as negative influences within the network depending on the nature of the social relationships, making the solution more realistic. To tackle this challenge, a belief-aware diffusion model has been designed for the simultaneous propagation of truth and rumor. The experiment conducted on real-life data sets demonstrates that the proposed algorithm provides better trade-off solutions close to the decision maker’s preferences compared to notable existing works. [ABSTRACT FROM AUTHOR]

We formulate an SEIR model for information propagation with the effect of a hot search in complex networks. Mathematical analysis is conducted in both a homogeneous network and heterogenous network. The results reveal that the dynamics are completely determined by the basic propagation number if the effect of a hot search is absent. On the other hand, when the effect of a hot search is taken into account, there exists no information-free equilibrium, and the information-propagating equilibrium is stable if the threshold is greater than 1. Numerical simulations were performed to examine the sensitivity of the parameters to the basic propagation number and the propagable nodes. Furthermore, the proposed model has been applied to fit the collected data for two types of information spreading in Sina Weibo, which confirmed the validity of our model and simulated the dynamical behaviors of information propagation.

Identifying the sources of propagation in social networks, such as the misinformation propagation, is one of the key issues recently. Most existing studies assume the underlying propagation model is known, which is difficult to obtain in practice. Recent efforts have been devoted to detect multiple sources in real-world situations, and the social influence of neighbors in the propagation is assumed to be identical. However, this assumption will result in inaccurate results as the infection state of a node is determined by its critical neighbors. In this paper, we fill this gap by capturing social influence of neighbors with structural properties in social networks. For instance, opinions are more likely to spread via closely connected friends within small groups. Here we propose a Motif-based Graph Convolutional Networks for Source Identification (MGCNSI) framework based on the GCN-based source identification approach. Specifically, different network motifs are used to capture different types of structural properties. Then each motif extracts the critical neighbors of a particular type, and a motif-based graph convolutional layer is constructed to aggregate critical neighbors for that motif. To adapt to underlying propagation mechanisms, an attention mechanism for aggregation is designed to automatically assign higher weights to more informative motifs. The empirical results demonstrate that MGCNSI outperforms several benchmark methods on both synthetic and real-world networks. The advantage is most obvious for networks with denser node neighborhoods, where MGCNSI can select critical neighbors from the larger neighbor sets. How the motifs can capture the social influence and the underlying critical paths of propagation is also illustrated.

Although the Internet and social media provide people with a range of opportunities and benefits in a variety of ways, the proliferation of fake news has negatively affected society and individuals. Many efforts have been invested to detect the fake news. However, to learn the representation of fake news by context information, it has brought many challenges for fake news detection due to the feature sparsity and ineffectively capturing the non-consecutive and long-range context. In this paper, we have proposed Intra-graph and Inter-graph Joint Information Propagation Network (abbreviated as IIJIPN) with Third-order Text Graph Tensor for fake news detection. Specifically, data augmentation is firstly utilized to solve the data imbalance and strengthen the small corpus. In the stage of feature extraction, Third-order Text Graph Tensor with sequential, syntactic, and semantic features is proposed to describe contextual information at different language properties. After constructing the text graphs for each text feature, Intra-graph and Inter-graph Joint Information Propagation is used for encoding the text: intra-graph information propagation is performed in each graph to realize homogeneous information interaction, and high-order homogeneous information interaction in each graph can be achieved by stacking propagation layer; inter-graph information propagation is performed among text graphs to realize heterogeneous information interaction by connecting the nodes across the graphs. Finally, news representations are generated by attention mechanism consisting of graph-level attention and node-level attention mechanism, and then news representations are fed into a fake news classifier. The experimental results on four public datasets indicate that our model has outperformed state-of-the-art methods. Our source code is available at https://github.com/cuibenkuan/IIJIPN. [ABSTRACT FROM AUTHOR]

The interaction of information and the evolution of network structure are inseparable. In order to construct social network evolution and information propagation models that better fit real-world scenarios, this paper proposes a social network structure evolution model driven by changes in the strength of relationships between individuals through their information interactions with each other. During the evolution process of the network, information interaction between individuals is also influenced by the network structure. Therefore, we improve traditional propagation models and construct an information propagation model with dynamic propagation rates. The proposed model is used to simulate both the spread of information and the evolution of network structures in real social networks. Finally, simulation results are compared to real-world data, demonstrating the effectiveness and rationality of the proposed model. [ABSTRACT FROM AUTHOR]

Social media platforms are broadly used to exchange information by milliards of people worldwide. Each day people share a lot of their updates and opinions on various types of topics. Moreover, politicians also use it to share their postulates and programs, shops to advertise their products, etc. Social media are so popular nowadays because of critical factors, including quick and accessible Internet communication, always available. These conditions make it easy to spread information from one user to another in close neighborhoods and around the whole social network located on the given platform. Unfortunately, it has recently been increasingly used for malicious purposes, e.g., rumor propagation. In most cases, the process starts from multiple nodes (users). There are numerous papers about detecting the real source with only one initiator. There is a lack of solutions dedicated to problems with multiple sources. Most solutions that meet those criteria need an accurate number of origins to detect them correctly, which is impossible to obtain in real-life usage. This paper analyzes the methods to detect rumor outbreaks in online social networks that can be used as an initial guess for the number of real propagation initiators. [ABSTRACT FROM AUTHOR]

Due to the differences in society stratum, personal profession, and social acceptability, information propagation can be impacted by the contact capabilities of individuals. Importantly, we found that with the changes in individual psychology, their response to a phenomenon will gradually weaken. This phenomenon is called heterogeneous decreased behavior and applied in the fields of economics, sociology, and ecology. In the social network, people show a gradually decreasing degree of interest for information, named individual heterogeneous decreased behavior (IHDB). We structure a two-layer network model to describe individual behavioral contact and propose a threshold function to represent IHDB. Meanwhile, we use partition theory to explain the information propagation mechanism. Through experiments, it is demonstrated that there is a continuous information outbreak in the ultimate adoption size when individuals exhibit a positive IHDB. However, when individuals exhibit a passive IHDB, there is a discontinuous information outbreak in the ultimate adoption size. Eventually, our experiments show that the theoretical analysis coincides with the results of the simulations.

The complex heterogeneous nature of social networks generates colossal user data, hence requiring exhaustive efforts to accelerate the propagation of information. This necessitates the identification of central nodes that are considered substantial for information spread and control. Our research proposes a novel centrality metric, TriBeC to identify the significant nodes in online social networks by utilizing the impact of weighted betweenness extended with network quartiles. The proposed approach introduces a user data-driven centrality measure for the discovery of influential nodes in online social networks. This is based on locating the median with the information flowing upstream and downstream, thereby considering the impact of border nodes lying farthest in the network circumference. Experimental outcomes on Twitter, Facebook, BlogCatalog, Scale-free and Random networks show the outperforming results of topmost 1% TriBeC central nodes over existing counterparts in terms of the percentage of the network being infested with information over time. [ABSTRACT FROM AUTHOR]

In the field of geoinformation science, multiview, image-based 3D city modeling has developed rapidly, and image depth estimation is an important step in it. To address the problems of the poor adaptability of training models of existing neural network methods and the long reconstruction time of traditional geometric methods, we propose a general depth estimation method for fast 3D city modeling that combines prior knowledge and information propagation. First, the original image is downsampled and input into the neural network to predict the initial depth value. Then, depth plane fitting and joint optimization are combined with the superpixel information and the superpixel optimized depth value is upsampled to the original resolution. Finally, the depth information propagation is checked pixel-by-pixel to obtain the final depth estimate. Experiments were conducted using multiple image datasets taken from actual indoor and outdoor scenes. Our method was compared and analyzed with a variety of existing widely used methods. The experimental results show that our method maintains high reconstruction accuracy and a fast reconstruction speed, and it achieves better performance. This paper offers a framework to integrate neural networks and traditional geometric methods, which provide a new approach for obtaining geographic information and fast 3D city modeling. [ABSTRACT FROM AUTHOR]

The same people exhibit various adoption behaviors for the same information on various networks. Previous studies, however, did not examine the variety of adoption behaviors on multi-layer networks or take into consideration this phenomenon. Therefore, we refer to this phenomenon, which lacks systematic analysis and investigation, as behavioral adoption diversity on multi-layered networks. Meanwhile, individual adoption behaviors have LTI (local trend imitation) characteristics that help spread information. In order to study the diverse LTI behaviors on information propagation, a two-layer network model is presented. Following that, we provide two adoption threshold functions to describe diverse LTI behaviors. The crossover phenomena in the phase transition is shown to exist through theoretical derivation and experimental simulation. Specifically, the final spreading scale displays a second-order continuous phase transition when individuals exhibit active LTI behaviors, and, when individuals behave negatively, a first-order discontinuous phase transition can be noticed in the final spreading scale. Additionally, the propagation phenomena might be impacted by the degree distribution heterogeneity. Finally, there is a good agreement between the outcomes of our theoretical analysis and simulation. [ABSTRACT FROM AUTHOR]

A significant distinction between the COVID-19 pandemic and previous pandemics is the significant role of social media platforms in shaping public adherence to non-pharmaceutical interventions and vaccine acceptance. However, with the recurrence of the epidemic, the conflict between epidemic prevention and production recovery has become increasingly prominent on social media. To help design effective communication strategies to guide public opinion, we propose a susceptible-forwarding-immune pseudo-environment (SFI-PE) dynamic model for understanding the environment with direct and indirect propagation behaviors. Then, we introduce a system with external interventions for direct and indirect propagation behaviors, termed the macro-controlled SFI-PE (M-SFI-PE) model. Based on the numerical analyses that were performed using actual data from the Chinese Sina microblogging platform, the data fitting results prove our models' effectiveness. The research grasps the law of the new information propagation paradigm, and our work bridges the gap between reality and theory in information interventions.

Passionate psychology behavior is a common behavior in everyday society but has been rarely studied on complex networks; so, it needs to be explored in more scenarios. In fact, the limited contact feature network will be closer to the real scene. In this paper, we study the influence of sensitive behavior and the heterogeneity of individual contact ability in a single-layer limited-contact network, and propose a single-layer model with limited contact that includes passionate psychology behaviors. Then, a generalized edge partition theory is used to study the information propagation mechanism of the model. Experimental results show that a cross-phase transition occurs. In this model, when individuals display positive passionate psychology behaviors, the final spreading scope will show a second-order continuous increase. When the individual exhibits negative sensitive behavior, the final spreading scope will show a first-order discontinuous increase In addition, heterogeneity in individuals' limited contact capabilities alters the speed of information propagation and the pattern of global adoption. Eventually, the outcomes of the theoretic analysis match those of the simulations. [ABSTRACT FROM AUTHOR]

Accumulating evidences demonstrate that circular RNA (circRNA) plays an important role in human diseases. Identification of circRNA-disease associations can help for the diagnosis of human diseases, while the traditional method based on biological experiments is time-consuming. In order to address the limitation, a series of computational methods have been proposed in recent years. However, few works have summarized these methods or compared the performance of them. In this paper, we divided the existing methods into three categories: information propagation, traditional machine learning and deep learning. Then, the baseline methods in each category are introduced in detail. Further, 5 different datasets are collected, and 14 representative methods of each category are selected and compared in the 5-fold, 10-fold cross-validation and the de novo experiment. In order to further evaluate the effectiveness of these methods, six common cancers are selected to compare the number of correctly identified circRNA-disease associations in the top-10, top-20, top-50, top-100 and top-200. In addition, according to the results, the observation about the robustness and the character of these methods are concluded. Finally, the future directions and challenges are discussed. [ABSTRACT FROM AUTHOR]