Influence Maximization In Social Networks Research Articles

A cost-effective seed selection model for multi-constraint influence maximization in social networks

The Influence Maximization (IM) problem aims to maximize the diffusion of information or adoption of products among users within a social network by identifying and activating a set of initial users. It is not unrealistic to have a higher activation cost for more influential users in real-life applications. However, existing works on IM focus solely on finding the most influential users as the seed set, without considering either the activation costs of individual nodes and the total budget or the size of the seed set. This oversight may lead to infeasible solutions, particularly from financial and managerial perspectives, respectively. To address these issues, we propose a more realistic and generalized formulation called Multi-Constraint Influence Maximization (MCIM) to achieve a cost-effective solution under both budgetary and cardinality constraints. The MCIM model allows for variable-length solutions, necessitating the exclusion of seed nodes from the influence spread estimation. Consequently, unlike existing IM formulations, the spread function under the MCIM model is no longer monotonic but submodular. As it has also been proven to be an NP-hard problem, we propose a Simple Additive Weighting (SAW)-assisted Differential Evolution (DE) algorithm for solving large-size real-world IM problems. Experimental results on four datasets demonstrate the effectiveness of the proposed formulation and algorithm in finding optimal and cost-effective solutions.

Fuzzy Influence Maximization in Social Networks

Influence maximization is a fundamental problem in social network analysis. This problem refers to the identification of a set of influential users as initial spreaders to maximize the spread of a message in a network. When such a message is spread, some users may be influenced by it. A common assumption of existing work is that the impact of a message is essentially binary: A user is either influenced (activated) or not influenced (non-activated). However, how strongly a user is influenced by a message may play an important role in this user’s attempt to influence subsequent users and spread the message further; existing methods may fail to model accurately the spreading process and identify influential users. In this article, we propose a novel approach to model a social network as a fuzzy graph where a fuzzy variable is used to represent the extent to which a user is influenced by a message (user’s activation level). By extending a diffusion model to simulate the spreading process in such a fuzzy graph, we conceptually formulate the fuzzy influence maximization problem for which three methods are proposed to identify influential users. Experimental results demonstrate the accuracy of the proposed methods in determining influential users in social networks.

Fairness-Aware Competitive Bidding Influence Maximization in Social Networks

Competitive influence maximization (CIM) has been studied for years due to its wide application in many domains. Most current studies primarily focus on the microlevel optimization by designing policies for one competitor to defeat its opponents. Furthermore, current studies ignore the fact that many influential nodes have their own starting prices, which may lead to inefficient budget allocation. In this article, we propose a novel competitive bidding influence maximization (CBIM) problem, where the competitors allocate budgets to bid for the seeds attributed to the platform during multiple bidding rounds. To solve the CBIM problem, we propose a fairness-aware multiagent CBIM (FMCBIM) framework. In this framework, we present a multiagent bidding particle environment (MBE) to model the competitors’ interactions and design a starting price adjustment mechanism to model the dynamic bidding environment. Moreover, we put forward a novel multiagent CBIM (MCBIM) algorithm to optimize competitors’ bidding policies. Extensive experiments on five datasets show that our work has good efficiency and effectiveness.

Neural attentive influence maximization model in social networks via reverse influence sampling on historical behavior sequences

Influence maximization in social networks aims to identify users with the highest influence in the network and leverage them as initial spreaders to maximize the revenue of influence. Recently, an increasing number of studies have drawn attention to the study of influence maximization in multi-entity social networks, achieving some degrees of success. However, most of them still have two major shortcomings. First, most of the previous work oversimplifies the correlation between the target entity and other entities, and overlooks the influence of user’s interest preference on information propagation. Second, the majority of existing methods face a trade-off between operational efficiency and solution accuracy, making it difficult to be effectively applied to large social networks. To fill this gap, this paper proposes a Deep neural network model based on Historical Behavior Sequences, named DHBS. DHBS consists of two core components: a Deep neural network model Incorporating Multi-head Self-attention, named DIMS; and a Reverse Influence Sampling algorithm based on Historical Behavior Sequence, named RIS-HBS. Specifically, DIMS captures the dynamic interest changes of users through its attention mechanism, enabling more accurate modeling of entity correlation in multi-entity social networks. RIS-HBS efficiently identifies a set of high-influential users for the target entity by incorporating the concept of reverse influence sampling. Experimental results on four publicly available network datasets demonstrate that DHBS outperforms baseline methods in terms of prediction accuracy, expected spread, and scalability, and DIMS plays a key role on RIS-HBS in the DHBS model.

Influence Maximization in Social Networks Using an Improved Multi-Objective Particle Swarm Optimization

Abstract The influence maximization (IM) problem has received great attention in the field of social network analysis, and its analysis results can provide reliable basis for decision makers when promoting products or political viewpoints. IM problem aims to select a set of seed users from social networks and maximize the number of users expected to be influenced. Most previous studies on the IM problem focused only on the single-objective problem of maximizing the influence spread of the seed set, ignoring the cost of the seed set, which causes decision makers to be unable to develop effective management strategies. In this work, the IM problem is formulated as a multi-objective IM problem that considers the cost of the seed set. An improved multi-objective particle swarm optimization (IMOPSO) algorithm is proposed to solve this problem. In the IMOPSO algorithm, the initialization strategy of Levy flight based on degree value is used to improve the quality of the initial solution, and the local search strategy based on greedy mechanism is designed to improve the Pareto Frontier distribution and promote algorithm convergence. Experimental results on six real social networks demonstrate that the proposed IMOPSO algorithm is effective, reducing runtime while providing competitive solutions.

Influence maximization in social networks using community-diversified seed selection

Abstract The community-based influence maximization in social networks refers to the strategy of amplifying the influence by leveraging the underlying connectivity patterns of the online communities. This promotes the widespread dissemination of user-generated content across socially connected entities. In our work, we have combined the simplicity of degree centrality and the bridging strength of betweenness for maximizing the influence performance. We have used community detection for partitioning the whole network into smaller subnetworks for intelligent distribution of seed nodes among these communities for improving the rate of information spreading. The proposed ranking, named as Community Diversified Seed Selection, is compared with degree centrality and betweenness centrality-based ranking in terms of rate of spreading, absolute execution time and algorithmic time complexity. The comparative study was performed on LFRμ= 0.01, LFRμ= 0.02 and LFRμ= 0.03 benchmark networks, and validated using Facebook real-world social network. Our proposed algorithm has better and faster spreading with reasonable time complexity, therefore, making it a suitable choice for larger networks.

Influence Maximization in social networks using discretized Harris’ Hawks Optimization algorithm

Influence Maximization (IM) is the task of determining k optimal influential nodes in a social network to maximize the influence spread using a propagation model. IM is a prominent problem for viral marketing and helps significantly in social media advertising. Previous Greedy and Reverse Influence Sampling-based IM approaches are ineffective in real-world social networks due to their significant computational cost and execution time. Further, even heuristic approaches applied to IM generally yield minimal performance gain relative to the decreased time complexity. This presents a challenge in developing cost-effective algorithms with low execution time that can handle diverse social networks. In this paper, we propose the discretization of the nature-inspired Harris’ Hawks Optimization meta-heuristic algorithm using community structures for optimal selection of seed nodes for influence spread. In addition to Harris’ Hawks’ intelligence, we employ a neighbor scout strategy algorithm to avoid blindness and enhance the searching ability of the hawks. Further, we use a candidate nodes-based random population initialization approach, and these candidate nodes aid in accelerating the convergence process for the entire populace, reducing the total computational cost. We evaluate the efficacy of our proposed DHHO approach on eight social networks using the Independent Cascade model for information diffusion. We observe that DHHO is comparable or better than competing meta-heuristic approaches for Influence Maximization across five metrics, and performs noticeably better than competing heuristic approaches.

Balanced influence maximization in social networks based on deep reinforcement learning

Balanced influence maximization aims to balance the influence maximization of multiple different entities in social networks and avoid the emergence of filter bubbles and echo chambers. Recently, an increasing number of studies have drawn attention to the study of balanced influence maximization in social networks and achieves success to some extent. However, most of them still have two major shortcomings. First, the previous works mainly focus on spreading the influence of multiple target entities to more users, ignoring the potential influence of the correlation between the target entities and other entities on information propagation in real social networks. Second, the existing methods require a large amount of diffusion sampling for influence estimation, making it difficult to apply to large social networks. To this end, we propose a Balanced Influence Maximization framework based on Deep Reinforcement Learning named BIM-DRL, which consists of two core components: an entity correlation evaluation module and a balanced seed node selection module. Specifically, in the entity correlation evaluation module, an entity correlation evaluation model based on the users’ historical behavior sequences is proposed, which can accurately evaluate the impact of entity correlation on information propagation. In the balanced seed node selection module, a balanced influence maximization model based on deep reinforcement learning is designed to train the parameters in the objective function, and then a set of seed nodes that maximize the balanced influence is found. Extensive experiments on six real-life network datasets demonstrate the superiority of the BIM-DRL over state-of-the-art methods on the metrics of balanced influence spread and balanced propagation accuracy.

Group Equality in Adaptive Submodular Maximization

In this paper, we study the classic submodular maximization problem subject to a group equality constraint under both nonadaptive and adaptive settings. It is shown that the utility function of many machine learning applications, including data summarization, influence maximization in social networks, and personalized recommendation, satisfies the property of submodularity. Hence, maximizing a submodular function subject to various constraints can be found at the heart of many of those applications. On a high level, submodular maximization aims to select a group of most representative items (e.g., data points). However, the design of most existing algorithms does not incorporate the fairness constraint, leading to underrepresentation or overrepresentation of some particular groups. This motivates us to study the submodular maximization problem with group equality, in which we aim to select a group of items to maximize a (possibly nonmonotone) submodular utility function subject to a group equality constraint. To this end, we develop the first constant-factor approximation algorithm for this problem. The design of our algorithm is robust enough to be extended to solving the submodular maximization problem under a more complicated adaptive setting. Moreover, we further extend our study to incorporating a global cardinality constraint and other fairness notations. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Supplemental Material: The online supplement is available at https://doi.org/10.1287/ijoc.2022.0384 .

A Search Space Reduction-Based Progressive Evolutionary Algorithm for Influence Maximization in Social Networks

Influence maximization (IM) problem, which selects a subset of nodes from a social network to maximize the influence spread, appeals to numerous scholars. Since the IM problem is NP-hard, it is still an arduous task to achieve good results in terms of influence spread and running time at the same time. This article proposes a novel search space reduction strategy-based progressive evolutionary algorithm (SSR-PEA) for solving IM problems effectively and efficiently. In SSR-PEA, a novel search space reduction strategy (SSR) in the light of the power-law distribution of the social network is designed to reduce the computational overheads, which eliminates a great deal of less influential nodes in a sensible way. After that, we propose a progressive evolutionary framework based on SSR, where the <inline-formula> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula>-element individual is optimized on the basis of the (<inline-formula> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula>-1)-element individual to speed up the optimal solution search process. Experimental results on ten real-world networks demonstrate that the proposed algorithm SSR-PEA can achieve 98<inline-formula> <tex-math notation="LaTeX">$\%$</tex-math> </inline-formula> of the influence spread achieved by cost-effective lazy forward (CELF) on average, and its running time is two or even three orders of magnitude shorter. Thus, SSR-PEA strikes a better balance between effectiveness and efficiency.

Influence maximization in social networks based on discrete harris hawks optimization algorithm

Influence maximization in social networks based on discrete harris hawks optimization algorithm

Exploiting community and structural hole spanner for influence maximization in social networks

AbstractViral marketing is a frequently used social marketing strategy which aims to promote products on various social media. In order to devise an efficient viral marketing strategy, influence maximization problem is widely studied in social networks. Influence maximization problem tries to find a set of influential users who could influence social users maximally. However, existed influence maximization algorithms could not satisfy the needs of social marketers well. In social networks, community structure is an important feature where social users are closely connected together in groups, and structural hole spanners are those users who bridge different communities. In order to balance the effectiveness and efficiency of viral marketing in social networks, in this paper, we present to exploit the community structure and structural hole spanners in social networks for solving the influence maximization problem. Different to traditional algorithms, we devise a strategy to approximate social influences of social users to find seeds efficiently. In particular, for an internal user inside a community in social networks, we utilize his social influence to members inside this community to approximate his social influence in the whole social network. For a structural hole spanner who bridges multiple communities, we utilize his social influence to members in these communities to approximate his social influence in the whole network. Based on the approximate social influence, information diffusion model and greedy framework, we propose a Community and Structural Hole Spanner based Greedy (CSHS‐G) algorithm which devises several influence lists to store social influences of users and fast computation scheme to find seeds. Besides, to further improve the efficiency, we propose a Community and Structural Hole Spanner based Heuristic (CSHS‐H) algorithm which approximates social influences of structural hole spanners by utilizing their 2‐hops influences. We conduct a comprehensive performance evaluation on the crawled real‐world data set. Experimental results show that, compared to all of the baseline algorithms, our proposed algorithms not only have high efficiency, but also could guarantee larger influence spread.

Community-based influence maximization in social networks under a competitive linear threshold model considering positive and negative user views

Social networks are a collection of people or groups and interactions between them, which can include friendship activities or business relationships, and they play an important role in spreading information and maximizing influence on users. The effect of social networks in our daily life is undeniable and it shows new methods of communication and is used as a medium to spread news, opinions, thoughts and any other kind of information. The main goal in influence maximization is to find a subset of key users that maximize influence expansion under a particular diffusion model. A number of studies over the past several years have tried to solve this problem by considering a nonracist environment where there is only one player without competitors, whereas in the real world, there is always more than one player competing with other players. To influence most nodes, this problem is called competitive influence maximization (CIM). Therefore, we try to solve the problem of maximizing the competitive influence by proposing a new diffusion model, considering two positive and negative views towards advertising, which is an extension of the linear threshold (LT) model with the ability to decide on the expansion of input influence and leads to an improvement in selecting seeds with better quality and also by extracting the community structure from the input graph, it is used to calculate the expansion of each node locally within its own community, and by preventing the repetition of penetration calculations for each node, it significantly reduces the execution time. The purpose of the proposed algorithm is to find the minimum number of seed nodes that can achieve more expansion compared to the expansion of nodes selected by other competitors in an acceptable execution time. Real-world experiments and synthetic datasets show that the proposed approach can obtain the minimum node required to defeat the competitor more realistically and in less time. Competitor leads to budget cuts by reducing the number of seeds required to defeat. The obtained results show that if [Formula: see text] seed nodes are needed to defeat the competitor, the execution time of the proposed algorithm will be approximately [Formula: see text].

A Hurst‐based diffusion model using time series characteristics for influence maximization in social networks

AbstractOnline social networks have grown exponentially in the recent years while finding applications in real life like marketing, recommendation systems, and social awareness campaigns. An important research area in this field is Influence Maximization, which pertains to finding methods for maximizing the spread of information (influence) across an OSN. Existing works in IM widely use a pre‐defined edge propagation probability for node activation. Hurst exponent (H), which depicts the self‐similarity in the time series depicting a user's past interaction behaviour, has also been used as activation criteria. In this work, we propose a Time Series Characteristic based Hurst‐based Diffusion Model (TSC‐HDM), which calculates H based on the stationary or non‐stationary characteristic of the time series. TSC‐HDM selects a handful of seed nodes and activates a seed node's inactive successor only if H &gt; 0.5. The proposed model has been tested on four real‐world OSN datasets. The results have been compared against four other IM models – Independent Cascade, Weighted Cascade, Trivalency, and Hurst‐based Influence Maximization. TSC‐HDM is found to have achieved as much as 590% higher expected influence spread as compared to the other models. Moreover, TSC‐HDM has attained 344% better average influence spread than other state‐of‐the‐art models namely LIR, A‐Greedy, LPIMA, Genetic Algorithm with Dynamic Probabilities, NeighborsRemove, DegreeDecrease, IGIM, IRR, and PHG.

Crisis Assessment Oriented Influence Maximization in Social Networks

Influence maximization (IM) aims to find a subset of k nodes that can maximize the final active node set under an information diffusion model. With the development and popularity of social networks, the IM problem plays an essential role in various applications, such as public opinion analysis, viral marketing, and rumor early warning. However, most of the existing IM solutions have not accessed the risk of negative information from nodes in the future. In reality, a company may face economic loss when negative information breaks out of its spokesman. Therefore, a crisis assessment oriented and topic-based IM problem (TIM-CA) is proposed, which is utilized to model the IM problem by considering the crisis assessment (CA) and topics of users. To solve this problem, we propose a maximum influence arborescence model-based algorithm for TIM-CA, namely, MIA-TIM-CA. The proposed algorithm consists of crisis degree calculation, topic relevance calculation, and influence spread evaluation. More importantly, as for crisis degree calculation, it considers self-, topology-, and topic-based crisis degrees for each node. At the influence spread evaluation stage, MIA-TIM-CA proposes two functions to evaluate the node's importance and node influence spread. Extensive experiments on two real-world social networks demonstrate that our MIA-TIM-CA outperforms all comparison algorithms on influence spread, crisis score, and running time.

A new stochastic diffusion model for influence maximization in social networks

Most current studies on information diffusion in online social networks focus on the deterministic aspects of social networks. However, the behavioral parameters of online social networks are uncertain, unpredictable, and time-varying. Thus, deterministic graphs for modeling information diffusion in online social networks are too restrictive to solve most real network problems, such as influence maximization. Recently, stochastic graphs have been proposed as a graph model for social network applications where the weights associated with links in the stochastic graph are random variables. In this paper, we first propose a diffusion model based on a stochastic graph, in which influence probabilities associated with its links are unknown random variables. Then we develop an approach using the set of learning automata residing in the proposed diffusion model to estimate the influence probabilities by sampling from the links of the stochastic graph. Numerical simulations conducted on real and artificial stochastic networks demonstrate the effectiveness of the proposed stochastic diffusion model for influence maximization.

Overlapping community‐based particle swarm optimization algorithm for influence maximization in social networks

AbstractInfluence maximization, whose aim is to maximise the expected number of influenced nodes by selecting a seed set of k influential nodes from a social network, has many applications such as goods advertising and rumour suppression. Among the existing influence maximization methods, the community‐based ones can achieve a good balance between effectiveness and efficiency. However, this kind of algorithm usually utilise the network community structures by viewing each node as a non‐overlapping node. In fact, many nodes in social networks are overlapping ones, which play more important role in influence spreading. To this end, an overlapping community‐based particle swarm optimization algorithm named OCPSO for influence maximization in social networks, which can make full use of overlapping nodes, non‐overlapping nodes, and their interactive information is proposed. Specifically, an overlapping community detection algorithm is used to obtain the information of overlapping community structures, based on which three novel evolutionary strategies, such as initialisation, mutation, and local search are designed in OCPSO for better finding influential nodes. Experimental results in terms of influence spread and running time on nine real‐world social networks demonstrate that the proposed OCPSO is competitive and promising comparing to several state‐of‐the‐arts (e.g. CGA, CMA‐IM, CIM, CDH‐SHRINK, CNCG, and CFIN).

Influence maximization in social networks using role-based embedding

&lt;abstract&gt;&lt;p&gt;Influence maximization (IM), a central issue in optimizing information diffusion on social platforms, aims to spread posts or comments more widely, rapidly, and efficiently. Existing studies primarily focus on the positive effects of incorporating heuristic calculations in IM approaches. However, heuristic models fail to consider the potential enhancements that can be achieved through network representation learning techniques. Some recent work is keen to use representation learning to deal with IM issues. However, few in-depth studies have explored the existing challenges in IM representation learning, specifically regarding the role characteristics and role representations. This paper highlights the potential advantages of combining heuristic computing and role embedding to solve IM problems. First, the method introduces role granularity classification to effectively categorize users into three distinct roles: opinion leaders, structural holes and normal nodes. This classification enables a deeper understanding of the dynamics of users within the network. Second, a novel role-based network embedding (RbNE) algorithm is proposed. By leveraging the concept of node roles, RbNE captures the similarity between nodes, allowing for a more accurate representation of the network structure. Finally, a superior IM approach, named RbneIM, is recommended. RbneIM combines heuristic computing and role embedding to establish a fusion-enhanced IM solution, resulting in an improved influence analysis process. Exploratory outcomes on six social network datasets indicate that the proposed approach outperforms state-of-the-art seeding algorithms in terms of maximizing influence. This finding highlights the effectiveness and efficacy of the proposed method in achieving higher levels of influence within social networks. The code is available at &lt;ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://github.com/baiyazi/IM2"&gt;https://github.com/baiyazi/IM2&lt;/ext-link&gt;.&lt;/p&gt;&lt;/abstract&gt;

A polyhedral approach to least cost influence maximization in social networks

The least cost influence maximization problem aims to determine minimum cost of partial (e.g., monetary) incentives initially given to the influential spreaders on a social network, so that these early adopters exert influence toward their neighbors and prompt influence propagation to reach a desired penetration rate by the end of cascading processes. We first conduct polyhedral analysis on a substructure that describes influence propagation assuming influence weights are unequal, linear and additively separable. Two classes of facet-defining inequalities based on a mixed 0–1 knapsack set contained in this substructure are proposed. We characterize another exponential class of valid and facet-defining inequalities utilizing the concept of minimum influencing subset. We show that these inequalities can be separated in polynomial time efficiently. Furthermore, a polynomial-time dynamic programming recursion is presented to solve this problem on a simple cycle graph. For arbitrary graphs, we propose a new exponential class of valid inequalities that dominates the cycle elimination constraints and an efficient separation algorithm for them. A compact convex hull description for a special case is presented. We illustrate the effectiveness of these inequalities via a delayed cut generation algorithm in the computational experiments.

Submodular maximization over data streams with differential privacy noise

In the big data era, data often comes in the form of streams and fast data stream analysis has recently attracted intensive research interest. Submodular optimization naturally appears in many streaming data applications such as social network influence maximization with the property of diminishing returns. However, in a practical setting, streaming data frequently comes with noises that are small but significant enough to impact the optimality of submodular optimization solutions. Following the framework of differential privacy (DP), this paper considers a streaming model with DP noise that is small by construction. Within this noisy streaming model, the paper strives to address the general problem of submodular maximization with a cardinality constraint. The main theoretical result we obtained is a streaming algorithm that is one-pass and has an approximation guarantee of 1(2+(1+1k)2)(1+1k)−δ for any δ>0. Finally, we implement the algorithm and evaluate it against several baseline methods. Numerical results support the practical performance of our algorithm across several real datasets.