Influence Spread Research Articles

Siedentop: A Memoir of the Man from Pomerene Hall

ABSTRACT This paper is a memoir of/for Daryl. A memoir is an historical account or biography written from personal knowledge. It provides my personal account of Daryl, the man, the physical educator, the intellectual, the teacher, and the scholar. In this account I trace Daryl’s work from when I first met him in 1981 and the major conceptual ideas that drove his research and scholarship. I discuss his commitment to behaviour analysis, his integrity and his politics. I try to convey the ways in which he thought about research and the world more generally. I also give an insight into Daryl the advisor and, in particular, what he meant for his international PhD students and how, through them, his influence spread across many countries of the world.

Influence Maximization under Fairness Budget Distribution in Online Social Networks

In social influence analysis, viral marketing, and other fields, the influence maximization problem is a fundamental one with critical applications and has attracted many researchers in the last decades. This problem asks to find a k-size seed set with the largest expected influence spread size. Our paper studies the problem of fairness budget distribution in influence maximization, aiming to find a seed set of size k fairly disseminated in target communities. Each community has certain lower and upper bounded budgets, and the number of each community’s elements is selected into a seed set holding these bounds. Nevertheless, resolving this problem encounters two main challenges: strongly influential seed sets might not adhere to the fairness constraint, and it is an NP-hard problem. To address these shortcomings, we propose three algorithms (FBIM1, FBIM2, and FBIM3). These algorithms combine an improved greedy strategy for selecting seeds to ensure maximum coverage with the fairness constraints by generating sampling through a Reverse Influence Sampling framework. Our algorithms provide a (1/2−ϵ)-approximation of the optimal solution, and require OkTlog(8+2ϵ)nln2δ+ln(kn)ϵ2, OkTlognϵ2k, and OTϵlogkϵlognϵ2k complexity, respectively. We conducted experiments on real social networks. The result shows that our proposed algorithms are highly scalable while satisfying theoretical assurances, and that the coverage ratios with respect to the target communities are larger than those of the state-of-the-art alternatives; there are even cases in which our algorithms reaches 100% coverage with respect to target communities. In addition, our algorithms are feasible and effective even in cases involving big data; in particular, the results of the algorithms guarantee fairness constraints.

Minimizing the Spread of Negative Influence in SNIR Model by Contact Blocking

This paper presents a method to minimize the spread of negative influence on social networks by contact blocking. First, based on the infection-spreading process of COVID-19, the traditional susceptible, infectious, and recovered (SIR) propagation model is extended to the susceptible, non-symptomatic, infectious, and recovered (SNIR) model. Based on this model, we present a method to estimate the number of individuals infected by a virus at any given time. By calculating the reduction in the number of infected individuals after blocking contacts, the method selects the set of contacts to be blocked that can maximally reduce the affected range. The selection of contacts to be blocked is repeated until the number of isolated contacts that need to be blocked is reached or all infection sources are blocked. The experimental results on three real datasets and three synthetic datasets show that the algorithm obtains contact blockings that can achieve a larger reduction in the range of infection than other similar algorithms. This shows that the presented SNIR propagation model can more precisely reflect the diffusion and infection process of viruses in social networks, and can efficiently block virus infections.

A complex network-based approach for security and governance in the smart green city

Interest in smart green cities is growing, and IoT is an underlying driver of this trend. The integration of IoT in smart cities has altered the manner of working and living of society. IoT technologies do, however, come with a number of drawbacks, including vulnerability of highly secure devices, energy consumption, governance, etc. in smart cities. Governance in smart city is more citizen-center and the decisions of government are focused on citizens’ demands. Government can identify influential spreaders from this huge voluminous population and take requisite preventive steps such as delaying the spread of rumor, identifying the information source, or blocking the source node. Hence, security of highly vulnerable IoT device and governance are the two main issues of the IoT environment of smart green city. This paper proposes a framework for identifying influential spreaders who play a key role in the governance of smart city as well as determining vulnerable IoT devices that needs to be protected from malware attacks. A framework named as Modified Dynamic Weighted Sum Method, is proposed for determining influential devices and spreaders in smart green cities. The connectivity between devices is considered as a network, then, distinct centrality measures are considered as the multi-attribute of the network, so as to take advantage of each centrality measure. Experiments are conducted on Santender testbed and it was found that static devices play an important role in IoT environment. Hence, they need to be protected from malware attacks. From future perspective, opportunities and challenges in smart city applications are discussed.

A sequential-path tree-based centrality for identifying influential spreaders in temporal networks

The problem of identifying influential spreaders in temporal networks has attracted extensive attention in recent years. Existing studies have proposed various centrality measures for quantifying the influence of nodes based on the structures of temporal networks, such as temporal degree centrality and temporal closeness centrality. However, most existing methods only take into account a single feature of nodes, while ignoring other temporal features (e.g., the propagation time, or the path length from an infected node to a destination node). In this paper, we propose a new centrality measure, namely as sequential-path tree-based centrality (SPT-C) which takes into account three different temporal features based on a new representation structure of temporal networks (i.e., sequential-path tree). The three temporal features include propagation time which measures the time that an infection propagates from an infected node to another, hop count which denotes the number of intermediate nodes that an infection propagates from an infected node to another, and reachable paths which represent the number of different time-respecting paths from an infected node to another. The evaluation experiments on 12 real-world temporal networks show that the effectiveness of our SPT-C in identifying influential spreaders is superior to other baseline measures.

Influence maximization in real-world closed social networks

In the last few years, many closed social networks such as WhatsAPP and WeChat have emerged to cater for people's growing demand of privacy and independence. In a closed social network, the posted content is not available to all users or senders can set limits on who can see the posted content. Under such a constraint, we study the problem of influence maximization in a closed social network. It aims to recommend users (not just the seed users) a limited number ofexistingfriends who will help propagate the information, such that the seed users' influence spread can be maximized. We first prove that this problem is NP-hard. Then, we propose a highly effective yet efficient method to augment the diffusion network, which initially consists of seed users only. The augmentation is done by iteratively and intelligently selecting and inserting a limited number of edges from the original network. Through extensive experiments on real-world social networks including deployment into a real-world application, we demonstrate the effectiveness and efficiency of our proposed method.

Influence Maximization With Visual Analytics.

In social networks, individuals' decisions are strongly influenced by recommendations from their friends, acquaintances, and favorite renowned personalities. The popularity of online social networking platforms makes them the prime venues to advertise products and promote opinions. The Influence Maximization (IM) problem entails selecting a seed set of users that maximizes the influence spread, i.e., the expected number of users positively influenced by a stochastic diffusion process triggered by the seeds. Engineering and analyzing IM algorithms remains a difficult and demanding task due to the NP-hardness of the problem and the stochastic nature of the diffusion processes. Despite several heuristics being introduced, they often fail in providing enough information on how the network topology affects the diffusion process, precious insights that could help researchers improve their seed set selection. In this paper, we present VAIM, a visual analytics system that supports users in analyzing, evaluating, and comparing information diffusion processes determined by different IM algorithms. Furthermore, VAIM provides useful insights that the analyst can use to modify the seed set of an IM algorithm, so to improve its influence spread. We assess our system by: (i) a qualitative evaluation based on a guided experiment with two domain experts on two different data sets; (ii) a quantitative estimation of the value of the proposed visualization through the ICE-T methodology by Wall et al. (IEEE TVCG - 2018). The twofold assessment indicates that VAIM effectively supports our target users in the visual analysis of the performance of IM algorithms.

FIP: A fast overlapping community-based influence maximization algorithm using probability coefficient of global diffusion in social networks

Influence maximization is the process of identifying a small set of influential nodes from a complex network to maximize the number of activation nodes. Due to the critical issues such as accuracy, stability, and time complexity in selecting the seed set, many studies and algorithms has been proposed in recent decade. However, most of the influence maximization algorithms run into major challenges such as the lack of optimal seed nodes selection, unsuitable influence spread, and high time complexity. In this paper intends to solve the mentioned challenges, by decreasing the search space to reduce the time complexity. Furthermore, It selects the seed nodes with more optimal influence spread concerning the characteristics of a community structure, diffusion capability of overlapped and hub nodes within and between communities, and the probability coefficient of global diffusion. The proposed algorithm, called the FIP algorithm, primarily detects the overlapping communities, weighs the communities, and analyzes the emotional relationships of the community’s nodes. Moreover, the search space for choosing the seed nodes is limited by removing insignificant communities. Then, the candidate nodes are generated using the effect of the probability of global diffusion. Finally, the role of important nodes and the diffusion impact of overlapping nodes in the communities are measured to select the final seed nodes. Experimental results in real-world and synthetic networks indicate that the proposed FIP algorithm has significantly outperformed other algorithms in terms of efficiency and runtime.

A parameterizable influence spread-based centrality measure for influential users detection in social networks

In social network analysis, centrality refers to the relevance of actors or nodes within a social network represented as a graph. Traditional centrality measures are based on topological aspects of the network or the information flow circulating through it. Since 2010, new centrality measures have been proposed based on the influence spread capacity of the actors. One of the purposes of centrality measures is to obtain well-differentiated rankings among the actors. In order to achieve the above, recent experiments suggest that the actors must rely on some neighbors to increase their initial influence spread capacity. This article presents the General Influence Spread Rank (GISR), a generalized and parameterizable centrality measure based on two well-known influence spread models: the Independent Cascade model and the Linear Threshold Model. This measure allows adjusting the depth levels, probability, and directionality of the initial activation of neighbors, to study how these parameters intervene in the actors’ centrality. The experiments validate the importance of the depth level of neighborhoods and reveal that other connectivity aspects are also relevant to the centrality problem. Further, the depth level seems to be the most relevant variable in the influence spread for denser networks. In contrast, for sparser networks, the probability of selecting neighbors seems even more significant than the depth level.

Art in the Dream World - Surrealism

Surrealism advocates breaking through the concept of logic and reality, completely abandoning the logical and impressionistic image of reality and combining instinct and reality, the subconscious and dreams, to reveal the deepest world of the human psyche. A social thought and literary movement that emerged in France after the First World War and whose influence spread to all European countries. It involved all fields of literature, art, theatre and music. It absorbed anti-traditional and auto-creative ideas from Dadaism, but overcame the weaknesses of Dadaism's negation of everything and had a more affirmative belief and programmer, and spread most widely as a fine art movement between the two World Wars. Surrealism was an artistic trend that involved not only literature, but also music, painting and other artistic fields.

Local-Forest Method for Superspreaders Identification in Online Social Networks.

Identifying the most influential spreaders in online social networks plays a prominent role in affecting information dissemination and public opinions. Researchers propose many effective identification methods, such as k-shell. However, these methods are usually validated by simulating propagation models, such as epidemic-like models, which rarely consider the Push-Republish mechanism with attenuation characteristic, the unique and widely-existing spreading mechanism in online social media. To address this issue, we first adopt the Push-Republish (PR) model as the underlying spreading process to check the performance of identification methods. Then, we find that the performance of classical identification methods significantly decreases in the PR model compared to epidemic-like models, especially when identifying the top 10% of superspreaders. Furthermore, inspired by the local tree-like structure caused by the PR model, we propose a new identification method, namely the Local-Forest (LF) method, and conduct extensive experiments in four real large networks to evaluate it. Results highlight that the Local-Forest method has the best performance in accurately identifying superspreaders compared with the classical methods.

TSIFIM: A three-stage iterative framework for influence maximization in complex networks

The problem of influence maximization is a classic issue that has been well-studied in the field of network science, but most of existing researches are compromising among computational complexity or result accuracy. In this work, a three-stage iterative framework for influence maximization (TSIFIM) is presented to find a set of seed spreaders in complex networks. In TSIFIM, the initial candidate seeds are first selected by considering the global communicability of each node and its importance in their local network. Then, in addition to the candidate seeds, other remained nodes are assigned to the specific communities based on the proposed local resource allocation similarity index, and the core node in each community which satisfies the local influence threshold condition are selected as the supplementary candidate seeds. Furthermore, we employ an adaptive search strategy to find the optimal solution among these candidates. The proposed algorithm is compared with eight popular influence maximization algorithms on nine real-world networks to verify the performance. Experimental results show that TSIFIM has better performance in terms of influence spreading, sensitivity analysis, seed dispersion and statistical test.

An improved clustering based multi-objective evolutionary algorithm for influence maximization under variable-length solutions

Knowledge of influential actors on real networks is essential for designing policies for boosting the spread of positive things (e.g., news and innovation) and preventing the spread of negative things (e.g., disease and misinformation). Finding an optimum set of nodes with maximum influence, known as Influence Maximization (IM), aims to achieve this, which requires consideration of both magnitude of the influence spread and other important factors such as the cost and size of the seed set. However, most previous research has viewed the IM problem as a single objective problem, wherein decision-makers are required to make decisions on other criteria without having a clear view of them. In this study, the IM problem is formulated as a Multi-objective Influence Maximization Problem (MIMP) with three conflicting objectives and real-world constraints. An improved version of θ-Dominance based Evolutionary Algorithm, named as θDEA-II, is proposed to solve the MIMP. To achieve a better balance between divergence and convergence of the proposed θDEA-II, a gap-based selection operator is introduced along with some modifications in the sorting and normalization process. Moreover, because the proposed MIMP deals with variable-length solutions, all of the investigated algorithms, including the baseline algorithms, are constructed with a novel crossover mechanism capable of producing offsprings that are different in size from their parents. Experimental results on six real-world datasets demonstrate that the proposed MIMP formulation as well as the θDEA-II algorithm are effective for finding an optimal solution.

Editor's Note

Editor's Note

Influence Spread in the Heterogeneous Multiplex Linear Threshold Model

The linear threshold model (LTM) has been used to study spread on single-layer networks defined by one interagent sensing modality and agents homogeneous in protocol. We define and analyze the heterogeneous multiplex LTM to study spread on multilayer networks with each layer representing a different sensing modality and agents heterogeneous in protocol. Protocols are designed to distinguish signals from different layers: an agent becomes active if a sufficient number of its neighbors in each of any <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$a$</tex-math></inline-formula> of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> layers is active. We focus on Protocol OR, when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$a=1$</tex-math></inline-formula> , and Protocol AND, when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$a=m$</tex-math></inline-formula> , which model agents that are most and least readily activated, respectively. We develop theory and algorithms to compute the size of the spread at steady state for any set of initially active agents and to analyze the role of distinguished sensing modalities, network structure, and heterogeneity. We show how heterogeneity manages the tension in spreading dynamics between sensitivity to inputs and robustness to disturbances.

AIGCrank: A new adaptive algorithm for identifying a set of influential spreaders in complex networks based on gravity centrality

The influence maximization problem in complex networks asks to identify a given size of seed spreaders set to maximize the number of expected influenced nodes at the end of the spreading process. This problem finds many practical applications in numerous areas such as information dissemination, epidemic immunity, and viral marketing. However, most existing influence maximization algorithms are limited by the “rich-club” phenomenon and are thus unable to avoid the influence overlap of seed spreaders. This work proposes a novel adaptive algorithm based on a new gravity centrality and a recursive ranking strategy, named AIGCrank, to identify a set of influential seeds. Specifically, the gravity centrality jointly employs the neighborhood, network location and topological structure information of nodes to evaluate each node’s potential of being selected as a seed. We also present a recursive ranking strategy for identifying seed nodes one-by-one. Experimental results show that our algorithm competes very favorably with the state-of-the-art algorithms in terms of influence propagation and coverage redundancy of the seed set.

Influence prediction model for marketing campaigns on e-commerce platforms

E-commerce platforms are committed to planning various marketing campaigns to make profits, and the impact of marketing campaigns is determined by the number of consumers, as well as the good or bad planning scheme. Therefore, accurate prediction of user behavior and comprehensive analysis of marketing campaign trends can help e-commerce platforms to identify quality marketing campaigns, which are crucial for developing e-commerce platforms. In this paper, we propose a model for predicting the influence trend of marketing campaigns based on the key information between group users and marketing campaigns on e-commerce platforms. To begin, text representation learning is used to unify user representations to address the multi-label nature of platform users, considering that text representation learning can extract deeper correlations between different label information and learn key information in the marketing campaign-user network by mining the correlations between user labels. Furthermore, considering the leading power of key users in the e-commerce platform, the influential spread network is constructed for the characteristics of the group user network under the marketing campaign using the locality-sensitive hashing (LSH) algorithm and combined with the mining of key information in the marketing campaign-user network. Finally, to address the time-series nature of influence development, the influence spread network is fused, and the contextual relationships of the influence spread network are learned using a recurrent neural network (RNN) algorithm to propose a model for predicting the influence trend of marketing campaigns based on key information. Experiments have shown that the model can predict user engagement behavior in the context of a marketing campaign, as well as dynamically depict changes in trends in the influence of the campaign.

An improved voterank algorithm to identifying a set of influential spreaders in complex networks

Identifying a set of critical nodes with high propagation in complex networks to achieve maximum influence is an important task in the field of complex network research, especially in the background of the current rapid global spread of COVID-19. In view of this, some scholars believe that nodes with high importance in the network have stronger propagation, and many classical methods are proposed to evaluate node importance. However, this approach makes it difficult to ensure that the selected spreaders are dispersed in the network, which greatly affects the propagation ability. The VoteRank algorithm uses a voting-based method to identify nodes with strong propagation in the network, but there are some deficiencies. Here, we solve this problem by proposing the DILVoteRank algorithm. The VoteRank algorithm cannot properly reflect the importance of nodes in the network topology. Based on this, we redefine the initial voting ability of nodes in the VoteRank algorithm and introduce the degree and importance of the line (DIL) ranking method to calculate the voting score so that the algorithm can better reflect the importance of nodes in the network structure. In addition, the weakening mechanism of the VoteRank algorithm only weakens the information of neighboring nodes of the selected nodes, which does not guarantee that the identified initial spreaders are sufficiently dispersed in the network. On this basis, we consider all the neighbors nodes of the node’s nearest and next nearest neighbors, so that the crucial spreaders identified by our algorithm are more widely distributed in the network with the same initial node ratio. In order to test the algorithm performance, we simulate the DILVoteRank algorithm with six other benchmark algorithms in 12 real-world network datasets based on two propagation dynamics model. The experimental results show that our algorithm identifies spreaders that achieve stronger propagation ability and propagation scale and with more stability compared to other benchmark algorithms.

Optimal Control Analysis of Treatment Strategies of the Dynamics of Cholera

A nine-compartment deterministic cholera model was formulated, and the model describes interactions between human, Vibrio cholerae bacteria, and the enviroment that warrant the interaction. Realities and socioeconomic burden influence spread and control mechanism of the disease. The model investigated some effective ways of hindering cholera outbreak and spread. The existence and uniqueness of solution of the system of equations that the model comprises were ascertained. The basic reproduction number R 0 of the model was obtained using “next-generation matrix” method, and the most sensitive parameters were identified using “normalised forward sensitivity index” method. Three controls, hygiene consciousness denoted by X1, cholera vaccine X2, and cholera awareness programme X3, were chosen. Optimal control theory is applied to ascertain the level of effect of the controls in reducing susceptible, exposed, infected individuals and causative pathogen population. Pontryagin’s maximum principle is used to prove the optimal solution of the model, and the optimal system was derived and numerically solved. Simulations were made with graphs that show the effects of the controls on susceptible, exposed, infected, and Vibrio cholerae population. The findings are that simultaneous application of the three controls can be one of the fast and effective ways of controlling cholera. If two controls are to be selected, hygiene consciousness and vaccine are the best combination.

Public health through the prism of personnel security

The functional safety is a key management task, considering that public organizations have to work in the rapidly changing environment, in the conditions of growing competition in the medical sphere, and spread of influence of information technology on the development of a public organizations.The situations when administration is more interested in the impact of natural and technological aspects on the safety of an organization happens more often, while the main threats are coming from people though, and in many cases from employees. The aim is to substantiate the scientific and theoretical provisions on the essence of personnel safety of a public organizations and to establish future measures to strengthen it. We used methods of analysis and synthesis, comparative and monographic methods. The results and conclusions of our work are the management of personnel safety of a health care institution is an integral part of the personnel management system of a public organizations, aimed at identifying, neutralizing and preventing risks and threats that are associated with the use of personnel and can potentially lead to negative consequences for a public organizations. It also requires the protection of employees, the creating of favorable conditions for work, meeting needs and achieving development goals.