Information Spreading In Networks Research Articles

Variable neighborhood search for weighted total domination problem and its application in social network information spreading

The weighted total domination problem (WTDP) is a practical extension of the well-known total domination problem. The most efficient literature approaches to tackle this problem are based on branch and cut or genetic algorithm. In this work, we propose a different strategy to solve WTDP that relies on the popular variable neighborhood search (VNS) metaheuristic. VNS is equipped with a carefully designed fitness function that allows for evaluation of both feasible and infeasible solutions, which further allows for a thorough search of the promising regions of the solution space. The method also utilizes two effective first-improvement local search procedures. The effectiveness of VNS is demonstrated on a wide range of benchmark sets compared to all three competing methods from the literature. For small-to-middle-sized instances (up to 100 nodes), VNS can obtain solutions that match the quality of optimal solutions in almost all cases (134 out of 135). For middle-to-large-sized instances, VNS can outperform all comparison algorithms in terms of solution quality, which is verified by statistical hypothesis tests. Another key aspect of this paper is presenting a potential application of the WTDP for boosting information spreading across social networks. Experiments confirmed that information spreading is accelerated when informed nodes (spreaders) are set to be solutions of the WTDP obtained by VNS. Although our method has been successfully applied to samples of real social networks of up to approximately 81 thousand nodes and 1.34 million edges, further research might enhance the method to be used on various (unsampled) social network datasets.

TEXT MESSAGE FLOW MODEL OF THEMATIC INTERNET RESOURCES INFORMATION SECURITY FORECASTING SYSTEMS

The tasks of analysis and classification of detection of existing mechanisms for the implementation of attacks and threats to information security, which can lead to unauthorized access to confidential information, disruption of the functioning of information systems, are relevant and prioritized at the current stage. The importance of the problems is connected with the following main factors: growth of variety and quantity of means of computer technology and spheres of human activity; a high level of trust in information and search systems for data processing and management; the growth of the number of users of the information space of interaction; accumulation of large volumes of various types of information, intensive exchange of data flow in the network between users, using a wide range of access mechanisms to confidential resources, information processes; industrial espionage and competitive struggle in the sphere of information services of society; insufficient number at the present stage of highly qualified specialists in the field of information security, market relations in the field of software development, maintenance, distribution, production of computing equipment for the implementation of information security. The presented model of the flow of text messages of thematic Internet resources of the information security forecasting system and the information model of the forum database of the thematic Internet resource, which differs from the known ones in its universality, allows to analyze and study the data flow of Internet forums implemented on the basis of popular software platforms for the development of discussion information thematic resources The model of the flow of text messages of thematic Internet forums allows statistical analysis and semantic filtering of messages, taking into account authorship, author rating, forum, time of creation, number of messages, forum topic, allows analysis and research of text messages of thematic Internet resources. Solving the set tasks will allow: to improve the quality of decisions made in the process of identifying and counteracting malicious information; sort information objects of influence for the operator by priority; set the input data settings of the system of detection and countermeasures against the spread of malicious information in networks.

Studies of Chordal Ring Networks via Double Metric Dimensions

Locating the sources of information spreading in networks including tracking down the origins of epidemics, rumors in social networks, and online computer viruses, has a wide range of applications. In many situations, identifying where an epidemic started, or which node in a network served as the source, is crucial. But it can be difficult to determine the root of an outbreak, especially when data are scarce and noisy. The goal is to find the source of the infection by analysing data provided by only a limited number of observers, such as nodes that can indicate when and where they are infected. Our goal is to investigate where the least number of observers should be placed, which is similar to how to figure out the minimal doubly resolving sets in the network. In this paper, we calculate the double metric dimension of chordal ring networks C R n 1,3,5 by describing their minimal doubly resolving sets.

Variable Neighborhood Search for Weighted Total Domination Problem and its Application in Social Network Information Spreading

Variable Neighborhood Search for Weighted Total Domination Problem and its Application in Social Network Information Spreading

Fairness in Influence Maximization through Randomization

The influence maximization paradigm has been used by researchers in various fields in order to study how information spreads in social networks. While previously the attention was mostly on efficiency, more recently fairness issues have been taken into account in this scope. In the present paper, we propose to use randomization as a mean for achieving fairness. While this general idea is not new, it has not been applied in the area of information spread in networks. Similar to previous works like Fish et al. (WWW '19) and Tsang et al. (IJCAI '19), we study the maximin criterion for (group) fairness. By allowing randomized solutions, we introduce two different variants of this problem. While the original deterministic maximin problem has been shown to be inapproximable, interestingly, we show that both probabilistic variants permit approximation algorithms with a constant multiplicative factor of 1-1/e plus an additive arbitrarily small error that is due to the simulation of the information spread. For an experimental study, we provide implementations of our methods and compare the achieved fairness values to existing methods. Non-surprisingly, the ex-ante values, i.e., minimum expected value of an individual (or group) to obtain the information, of the computed probabilistic strategies are significantly larger than the (ex-post) fairness values of previous methods. This confirms that studying fairness via randomization is a worthwhile direction. More surprisingly, we observe that even the ex-post fairness values, i.e., fairness values of sets sampled according to the probabilistic strategies, computed by our routines dominate over the fairness achieved by previous methods on most of the instances tested.

Network temporality can promote and suppress information spreading.

Temporality is an essential characteristic of many real-world networks and dramatically affects the spreading dynamics on networks. In this paper, we propose an information spreading model on temporal networks with heterogeneous populations. Individuals are divided into activists and bigots to describe the willingness to accept the information. Through a developed discrete Markov chain approach and extensive numerical simulations, we discuss the phase diagram of the model and the effects of network temporality. From the phase diagram, we find that the outbreak phase transition is continuous when bigots are relatively rare, and a hysteresis loop emerges when there are a sufficient number of bigots. The network temporality does not qualitatively alter the phase diagram. However, we find that the network temporality affects the spreading outbreak size by either promoting or suppressing, which relies on the heterogeneities of population and of degree distribution. Specifically, in networks with homogeneous and weak heterogeneous degree distribution, the network temporality suppresses (promotes) the information spreading for small (large) values of information transmission probability. In networks with strong heterogeneous degree distribution, the network temporality always promotes the information spreading when activists dominate the population, or there are relatively fewer activists. Finally, we also find the optimal network evolution scale, under which the network information spreading is maximized.

A two-layer SIR information propagation model with heterogeneity based on coupled network

The spreading of information in network is different from epidemics in the population; meanwhile, the node is heterogeneous, and the structure is going in the direction of double or even multi-layer. It is of great practical significance to study the anti-risk capability of coupled network. Based on the subjective heterogeneity and memory effect heterogeneity, a two-layer SIR information propagation model is constructed and an important node selection method for the coupled network based on technique for order preference by similarity to an ideal solution (TOPSIS) is proposed. The effectiveness of the constructed model and the proposed method is verified by simulation experiment which selects the important nodes as the immune nodes of TOPSIS immunization strategy and adopts random immunization strategy, partial nodes immune layer strategy and TOPSIS immunization strategy on BA_BA, WS_WS and BA_WS coupled network. The experimental results show that subjective heterogeneity can hinder the dissemination of information, while the memory effect heterogeneity can facilitate the dissemination of information. In addition, different immune strategies have different effects on different coupled networks, for example, the TOPSIS immune strategy has the best effect in BA_BA network.

Contagion of Information on Two-Layered Weighted Complex Network

In recent years, information spreading mechanism on the social networks has received extensive attention. Previous studies usually simplify the social relationships as binary, though in real social networks, the different intimacy between nodes in different layers will affect the information spreading. The enhancement of non-redundant information memory also plays an important role in information spreading. In this paper, we propose a weighted two-layered social network information spreading model based on threshold model. In order to qualitatively understand the impact of weight distribution heterogeneity on information spreading, an edge-weight based compartmental theory is proposed. We find that under an arbitrary adoption threshold, reducing weight distribution heterogeneity can facilitate information spreading and promote the global adoption, yet unable to change the dependence pattern of the final adoption size on the unit transmission probability. We also find that when the initial seed fraction is less than a critical value, information cannot be transmitted explosively in the network. Increasing the fraction of initial seeds or the heterogeneity of degree distribution will alter the dependence pattern of the final adoption size on the unit transmission probability from being discontinuous to being continuous.

Better Bounds for Coalescing-Branching Random Walks

Coalescing-branching random walks, or cobra walks for short, are a natural variant of random walks on graphs that can model the spread of disease through contacts or the spread of information in networks. In a k -cobra walk, at each timestep, a subset of the vertices are active; each active vertex chooses k random neighbors (sampled independently and uniformly with replacement) that become active at the next step, and these are the only active vertices at the next step. A natural quantity to study for cobra walks is the cover time, which corresponds to the expected time when all nodes have become infected or received the disseminated information. In this article, we extend previous results for cobra walks in multiple ways. We show that the cover time for the 2-cobra walk on [0, n ] d is O ( n ) (where the order notation hides constant factors that depend on d ); previous work had shown the cover time was O ( n ⋅polylog( n )). We show that the cover time for a 2-cobra walk on an n -vertex d -regular graph with conductance φ G is O ( d 4 φ −2 G log 2 n ), significantly generalizing a previous result that held only for expander graphs with sufficiently high expansion. And, finally, we show that the cover time for a 2-cobra walk on a graph with n vertices and m edges is always O ( mn 3/4 log n ; this is the first result showing that the bound of Θ( n 3 ) for the worst-case cover time for random walks can be beaten using 2-cobra walks.

Information Spreading Forensics via Sequential Dependent Snapshots

Mining the characteristics of information spreading in networks is crucial in communication studies, network security management, epidemic investigations, etc. Previous works are restrictive because they mainly focused on the information source detection using either a single observation, or multiple but independent observations of the underlying network while assuming a homogeneous information spreading rate. We conduct a theoretical and experimental study on information spreading, and propose a new and novel estimation framework to estimate 1) information spreading rates, 2) start time of the information source, and 3) the location of information source by utilizing multiple sequential and dependent snapshots where information can spread at heterogeneous rates. Our framework generalizes the current state-of-the-art rumor centrality [1] and the union rumor centrality [2]. Furthermore, we allow heterogeneous information spreading rates at different branches of a network. Our framework provides conditional maximum likelihood estimators for the above three metrics and is more accurate than rumor centrality and Jordan center in both synthetic networks and real-world networks. Applying our framework to the Twitter's retweet networks, we can accurately determine who made the initial tweet and at what time the tweet was sent. Furthermore, we also validate that the rates of information spreading are indeed heterogeneous among different parts of a retweet network.

Generalization of Information Spreading Forensics via Sequential Dependent Snapshots

Learning the characteristics of information spreading in networks is crucial in communication studies, social network sentiment analysis and epidemic investigation. Previous work on information spreading has been focused on the information source detection using either a single observation, or multiple but "independent" observations of the underlying network while assuming information spreads at a "uniform spreading rate" . In this paper, we conduct the first theoretical and experimental study on information spreading forensics, and propose a framework for estimating information spreading rates, information source start time and location of information source by utilizing "multiple sequential and dependent snapshots" where information can spread at different rates. We prove that our estimation framework generalizes the rumor centrality [1], and we allow heterogeneous information spreading rates on different branches in d -regular tree networks. We further show that our framework can provide highly accurate estimates for the information spreading rates on different branches, the source start time, and more accurate estimate for the location of information source than rumor centrality and Jordan center in both synthetic networks and real-world networks (i.e., Twitter).

Towards large-scale social networks with online diffusion provenance detection

In this paper we study a new problem of online discovering diffusion provenances in large networks. Existing work on network diffusion provenance identification focuses on offline learning where data collected from network detectors are static and a snapshot of the network is available before learning. However, an offline learning model does not meet the need for early warning, real-time awareness, or a real-time response to malicious information spreading in networks. To this end, we propose an online regression model for real-time diffusion provenance identification. Specifically, we first use offline collected network cascades to infer the edge transmission weights, and then use an online l1 non-convex regression model as the identification model. The proposed methods are empirically evaluated on both synthetic and real-world networks. Experimental results demonstrate the effectiveness of the proposed model.

The study of the dynamic model on KAD network information spreading

In recent years, the pure distributed file sharing peer-to-peer network—Kad network has been paid close attention by scholars. However, the study of the controlling problem of information spreading on the Kad network is rare. We found that the traditional infectious model MSEIR is similar with the specific information propagation of the Kad network by studying. But the MSEIR model could not completely and accurately simulate the information dissemination process of the Kad network. Therefore, the model of Kad epidemic dynamic model (KEDM) which has improved the transmission status of the existing MSEIR is proposed. Considering the characteristics of the KEDM model in different time segment, we redefine the relationship between the status transitions. We derived the basic reproduction number of KEDM using the degree function reference model. Data simulation results show that the KEDM can more accurately describe the specific information propagation of the Kad network than traditional MSEIR model. The basic reproduction number can accurately reflect the equilibrium threshold and stability of the specific information propagation in Kad network.

Coordination sequences and information spreading in small-world networks

We study the spread of information in small-world networks generated from different d-dimensional regular lattices, with d=1, 2, and 3. With this purpose, we analyze by numerical simulations the behavior of the coordination sequence, e.g., the average number of sites C(n) that can be reached from a given node of the network in n steps along its bonds. For sufficiently large networks, we find an asymptotic behavior C(n) approximately rho(n), with a constant rho that depends on the network dimension d and on the rewiring probability p (which measures the disorder strength of a given network). A simple model of information spreading in these networks is studied, assuming that only a fraction q of the network sites are active. The number of active nodes reached in n steps has an asymptotic form lambda(n), lambda being a constant that depends on p and q, as well as on the dimension d of the underlying lattice. The information spreading presents two different regimes depending on the value of lambda: For lambda>1 the information propagates along the whole system, and for lambda<1 the spreading is damped and the information remains confined in a limited region of the network. We discuss the connection of these results with site percolation in small-world networks.