Influence Maximization Research Articles

Influence maximization under imbalanced heterogeneous networks via lightweight reinforcement learning with prior knowledge

Influence maximization under imbalanced heterogeneous networks via lightweight reinforcement learning with prior knowledge

Identifying Key Nodes for the Influence Spread Using a Machine Learning Approach

The identification of key nodes in complex networks is an important topic in many network science areas. It is vital to a variety of real-world applications, including viral marketing, epidemic spreading and influence maximization. In recent years, machine learning algorithms have proven to outperform the conventional, centrality-based methods in accuracy and consistency, but this approach still requires further refinement. What information about the influencers can be extracted from the network? How can we precisely obtain the labels required for training? Can these models generalize well? In this paper, we answer these questions by presenting an enhanced machine learning-based framework for the influence spread problem. We focus on identifying key nodes for the Independent Cascade model, which is a popular reference method. Our main contribution is an improved process of obtaining the labels required for training by introducing “Smart Bins” and proving their advantage over known methods. Next, we show that our methodology allows ML models to not only predict the influence of a given node, but to also determine other characteristics of the spreading process—which is another novelty to the relevant literature. Finally, we extensively test our framework and its ability to generalize beyond complex networks of different types and sizes, gaining important insight into the properties of these methods.

DGN: influence maximization based on deep reinforcement learning

DGN: influence maximization based on deep reinforcement learning

Identifying influential nodes in social networks from the perspective of attack-defense game.

Influence spread analysis, a critical component of social network studies, focuses on the patterns and effects of information dissemination among interconnected entities. The core of influence spread analysis is to identify influential nodes that involve two distinct aspects: influence maximization (IM) and influence blocking maximization (IBM). However, when IM and IBM occur simultaneously, identifying influential nodes becomes an intricate decision-making challenge. This study addresses identifying influential nodes in social networks through an attack-defense game perspective, where an attacker maximizes influence and a defender minimizes it. We first develop a two-player static zero-sum game model considering resource constraints. Based on the equilibrium strategy of this game, we redefine the concept of influential nodes from various viewpoints. Extensive experiments on synthetic and real-world networks show that, in most cases, the defender preferentially defends critical nodes, while the attacker adopts the decentralized strategy. Only when resources are unevenly matched do both players tend to adopt centralized strategies. This study expands the connotation of influential nodes and provides a novel paradigm for the social network analysis with significant potential applications.

Budget-Aware Local Influence Iterative Algorithm for Efficient Influence Maximization in Social Networks

The budgeted influence maximization (BIM) problem aims to identify a set of seed nodes that adhere to predefined budget constraints within a specified network structure and cost model. However, it is difficult for the existing algorithms to achieve a balance between timeliness and effectiveness. To address this challenge, our study initially proposes a refined cost model through empirical scrutiny of Weibo's quote data. Subsequently, we introduce a proxy-based algorithm, i.e., the budget-aware local influence iterative (BLII) algorithm tailored for the BIM problem, aimed at expediently identifying seed nodes. The algorithm approximates the global influence by leveraging the user’s one-hop influence and circumvents influence overlap among seed nodes via iterative influence updates. Comparative experiments involving eight algorithms across four real networks demonstrate the effectiveness, efficiency, and robustness of the BLII algorithm. In terms of influence spread, the proposed algorithm outperforms other proxy-based algorithms by 20%–255% and reaches the state-of-the-art simulation-based approach by 96%. In addition, the running time of the BLII algorithm is reasonable. Generally, the proposed cost model and BLII algorithm provide novel insights and potent tools for studying BIM problems.

A General Concave Fairness Framework for Influence Maximization Based on Poverty Reward

Influence maximization (IM) aims to find a group of influential nodes as initial spreaders to maximize the influence spread over a network. Yet, traditional IM algorithms have not been designed with fairness in mind, resulting in discrimination against some groups, like LGBTQ communities and racial minorities, etc. This issue has spurred research on Fair Influence Maximization (FIM). However, existing FIM studies come with some drawbacks. Firstly, most proposed notions of fairness for FIM cannot adjust the trade-off between fairness level and influence spread. Secondly, though a few specific notions of fairness allow such balancing, they are limited to a few specific concave functions, which may not be suitable for various real-world scenarios. Furthermore, none of them have studied the deep relations between the features of concave functions and the level of fairness. Thirdly, existing fairness metrics are limited to their corresponding concepts of fairness. Comparing the level of fairness across different algorithms using existing metrics can be challenging. To tackle the above problems, this paper first proposes a novel fairness notion named Poverty Reward (PR), which achieves fairness by rewarding the enrichment of groups with low utility. Based on PR, we further propose an algorithmic framework called Concave Fairness Framework (CFF) that allows any concave function that satisfies specific requirements. We also systematically clarify how fairness is improved by applying concave functions and provide an in-depth quantitative analysis of how to select appropriate concave functions for different utility distributions. Moreover, we propose the Reward of Fairness (RoF) metric that evaluates the disparity between groups. Based on RoF, an evaluation system is built to uniformly compare FIM algorithms from different fairness notions. Experiments in real-world datasets have demonstrated the validity of the CFF, as well as the proposed fairness notion.

An adaptive differential evolution algorithm driven by multiple probabilistic mutation strategies for influence maximization in social networks

Influence maximization is a critical research topic of social network analysis, particularly with the increasing involvement of individuals in the global networked society. The purpose of the problem is to identify k influential nodes from the social network and activate them initially to maximize the expected number of influenced nodes at the end of the spreading process. Although some meta-heuristics based on swarm intelligence or biological evolution have been proposed to tackle this intractable problem, further investigation is required to refine the exploration and exploitation operations based on the iterative information from the evolutionary process. In this paper, an adaptive differential evolution algorithm driven by multiple probabilistic mutation strategies is proposed for the influence maximization problem. In order to enhance the evolutionary capability of the later stages of the discrete differential evolution, the mutation in the framework, consisting of three policies, namely comprehensive learning particle swarm mutation strategy, differential mutation strategy, and perturbation strategy, is implied based on different probabilistic models. An adaptive local search strategy is presented to improve the local optimum results based on a potential alternative library consisting of structural hole nodes, which guides the differential evolution to find a more optimal solution. Experimental results on six real-world social networks demonstrate the competitive performance of the proposed algorithm in terms of both efficacy and efficiency compared to state-of-the-art algorithms.

Maximizing Influence in Social Networks Using Combined Local Features and Deep Learning-Based Node Embedding.

The influence maximization problem has several issues, including low infection rates and high time complexity. Many proposed methods are not suitable for large-scale networks due to their time complexity or free parameter usage. To address these challenges, this article proposes a local heuristic called Embedding Technique for Influence Maximization (ETIM) that uses shell decomposition, graph embedding, and reduction, as well as combined local structural features. The algorithm selects candidate nodes based on their connections among network shells and topological features, reducing the search space and computational overhead. It uses a deep learning-based node embedding technique to create a multidimensional vector of candidate nodes and calculates the dependency on spreading for each node based on local topological features. Finally, influential nodes are identified using the results of the previous phases and newly defined local features. The proposed algorithm is evaluated using the independent cascade model, showing its competitiveness and ability to achieve the best performance in terms of solution quality. Compared with the collective influence global algorithm, ETIM is significantly faster and improves the infection rate by an average of 12%.

DiFuseR: a distributed sketch-based influence maximization algorithm for GPUs

DiFuseR: a distributed sketch-based influence maximization algorithm for GPUs

IMUNE: A novel evolutionary algorithm for influence maximization in UAV networks

In a network, influence maximization addresses identifying an optimal set of nodes to initiate influence propagation, thereby maximizing the influence spread. Current approaches for influence maximization encounter limitations in accuracy and efficiency. Furthermore, most existing methods are aimed at the IC (Independent Cascade) diffusion model, and few solutions concern dynamic networks. In this study, we focus on dynamic networks consisting of UAV (Unmanned Aerial Vehicle) clusters that perform coverage tasks and introduce IMUNE, an evolutionary algorithm for influence maximization in UAV networks. We first generate dynamic networks that simulate UAV coverage tasks and give the representation of dynamic networks. Novel fitness functions in the evolutionary algorithm are designed to estimate the influence ability of a set of seed nodes in a dynamic process. On this basis, an integrated fitness function is proposed to fit both the IC and SI (Susceptible–Infected) models. IMUNE can find seed nodes for maximizing influence spread in dynamic UAV networks with different diffusion models through the improvements in fitness functions and search strategies. Experimental results on UAV network datasets show the effectiveness and efficiency of the IMUNE algorithm in solving influence maximization problems.

Influence maximization based on discrete particle swarm optimization on multilayer network

Influence maximization (IM) aims to strategically select influential users to maximize information propagation in social networks. Most of the existing studies focus on IM in single-layer networks. However, we have observed that individuals often engage in multiple social platforms to fulfill various social needs. To make better use of this observation, we consider an extended problem of how to maximize influence spread in multilayer networks. The Multilayer Influence Maximization (MLIM) problem is different from the IM problem because information propagation behaves differently in multilayer networks compared to single-layer networks: users influenced on one layer may trigger the propagation of information on another layer. Our work successfully models the information propagation process as a Multilayer Independent Cascade model in multilayer networks. Based on the characteristics of this model, we introduce an approximation function called Multilayer Expected Diffusion Value (MLEDV) for it. However, the NP-hardness of the MLIM problem has posed significant challenges to our work. To tackle the issue, we devise a novel algorithm based on Discrete Particle Swarm Optimization. Our algorithm consists of two stages: 1) the candidate node selection, where we devise a novel centrality metric called Random connectivity Centrality to select candidate nodes, which assesses the importance of nodes from a connectivity perspective. 2)the seed selection, where we employ a discrete particle swarm algorithm to find seed nodes from the candidate nodes. We use MLEDV as a fitness function to measure the spreading power of candidate solutions in our algorithm. Additionally, we introduce a Neighborhood Optimization strategy to increase the convergence of the algorithm. We conduct experiments on four real-world networks and two self-built networks and demonstrate that our algorithms are effective for the MLIM problem.

DCDIMB: Dynamic Community-based Diversified Influence Maximization using Bridge Nodes

Influence maximization (IM) is the fundamental study of social network analysis. The IM problem finds the top k nodes that have maximum influence in the network. Most of the studies in IM focus on maximizing the number of activated nodes in the static social network. But in real life, social networks are dynamic in nature. This work addresses the diversification of activated nodes in the dynamic social network. This work proposes an objective function that maximizes the number of communities by utilizing bridge nodes. We also propose a diffusion model that considers the role of inactive nodes in influencing a node. We prove the submodularity, and monotonicity of the objective function under the proposed diffusion model. This work analyzes the impact of different ratios of bridge nodes in the seed set on real-world and synthetic datasets. Furthermore, we prove the NP-Hardness of the objective function under the proposed diffusion model. The experiments are conducted on various real-world and synthetic datasets with known and unknown community information. The proposed work experimentally shows that the objective function gives the maximum number of communities considering bridge nodes compared with the benchmark algorithms.

Cuckoo Search Optimization-Based Influence Maximization in Dynamic Social Networks

Online social networks are crucial in propagating information and exerting influence through word-of-mouth transmission. Influence maximization (IM) is the fundamental task in social network analysis to find the group of nodes that maximizes the influence in the social network. IM has different applications like viral marketing, campaigning, advertising, and so on. Literature has presented various algorithms based on different approaches to address the IM problem, including nature-inspired algorithms. Most of the work focuses on the static social network. The proposed work first employs nature-inspired Cuckoo Search Optimization to solve the IM problem in dynamic networks. The proposed algorithm applies the fuzzy-logic-based technique to optimize the nests. We also perform statistical tests to show the effectiveness of the proposed algorithm with the benchmark algorithms. The experimental results are performed on five datasets and compare the results with the state-of-the-art algorithms. The results show that the proposed algorithm gives better results than the nature-inspired state-of-the-art algorithms.

Learning graph representations for influence maximization

Learning graph representations for influence maximization

The influence maximization algorithm for integrating attribute graph clustering and heterogeneous graph transformer

In social networks, maximizing influence is an important research direction. However, traditional influence maximization algorithms often overlook the attribute information of nodes and the heterogeneity of networks, leading to inefficiency and inaccuracy in the propagation process. To address this issue, this study first constructs a social network influence maximization propagation model, and then combines auto-encoder and graph convolutional autoencoder to extract social network user attributes. Finally, Transformer is used to learn the feature representation of social network nodes. Moreover, a heterogeneous graph neural network is introduced to combine with Transformer to further learn the feature representation of nodes, so as to design an influence maximization algorithm combining attributed graph clustering and heterogeneous graph Transformer. The results showed that the loss values of the fusion algorithm on different datasets were 0.619 and 0.17, respectively, proving its good fitting performance. The recall rate and F1 of this fusion algorithm were 92.5 % and 0.90, respectively, proving its high clustering accuracy. The influence maximization model based on fusion algorithm achieved active node coverage of 67 % and 48 % on two datasets, respectively, proving its good influence propagation effect. The above results demonstrate that the designed model can effectively spread influence. This helps to better understand and utilize the influence dissemination mechanisms in social networks, thereby promoting the development of social network research.

Influence maximization in blockchain social networks: a heterogeneous LDAG approach

Influence maximization in blockchain social networks: a heterogeneous LDAG approach

A co-operative game theoretic approach for the budgeted influence maximization problem

A co-operative game theoretic approach for the budgeted influence maximization problem

Maximizing influence via link prediction in evolving networks

Influence Maximization (IM), targeting the optimal selection of k seed nodes to maximize potential information dissemination in prospectively social networks, garners pivotal interest in diverse realms like viral marketing and political discourse dissemination. Despite receiving substantial scholarly attention, prevailing research predominantly addresses the IM problem within the confines of existing networks, thereby neglecting the dynamic evolutionary character of social networks. An inevitable requisite arises to explore the IM problem in social networks of future contexts, which is imperative for certain application scenarios. In this light, we introduce a novel problem, Influence Maximization in Future Networks (IMFN), aimed at resolving the IM problem within an anticipated future network framework. We establish that the IMFN problem is NP-hard and advocate a prospective solution framework, employing judiciously selected link prediction methods to forecast the future network, and subsequently applying a greedy algorithm to select the k most influential nodes. Moreover, we present SCOL (Sketch-based Cost-effective lazy forward selection algorithm Optimized with Labeling technique), a well-designed algorithm to accelerate the query of our IMFN problem. Extensive experimental results, rooted in five real-world datasets, are provided, affirming the efficacy and efficiency of the proffered solution and algorithms.

Context propagation based influence maximization model for dynamic link prediction

Influence maximization (IM) in dynamic social networks is an optimization problem to analyze the changes in social networks for different periods. However, the existing IM methods ignore the context propagation of interaction behaviors among users. Hence, context-based IM in multiplex networks is proposed here. Initially, multiplex networks along with their contextual data are taken as input. Community detection is performed for the network using the Wilcoxon Hypothesized K-Means (WH-KMA) algorithm. From the detected communities, the homogeneous network is used for extracting network topological features, and the heterogeneous networks are used for influence path analysis based on which the node connections are weighted. Then, the influence-path-based features along with contextual features are extracted. These extracted features are given for the link prediction model using the Parametric Probability Theory-based Long Short-Term Memory (PPT-LSTM) model. Finally, from the network graph, the most influencing nodes are identified using the Linear Scaling based Clique (LS-Clique) detection algorithm. The experimental outcomes reveal that the proposed model achieves an enhanced performance.

A learning-based influence maximization framework for complex networks via K-core hierarchies and reinforcement learning

Influence maximization (IM) is a critical aspect of complex network analysis, as it holds considerable commercial value across various domains such as recommendation systems and viral marketing. Its objective is to identify a set of seed nodes that can initiate the most extensive cascade of influence within the network. However, IM encounters significant challenges due to its NP-hard nature of accurately identifying the optimal seed nodes and estimating the spread of influence. Although recent learning-based IM methods have shown promising results, there are still challenges related to scalability, efficiency, and avoiding influence overlap that need to be addressed. This paper proposes CoreQ, a novel reinforcement learning-based framework that leverages K-core hierarchies to address IM challenges and make advised decisions to learn the optimal policy to select seed nodes. CoreQ significantly enhances IM by reducing computational costs and effectively addressing the influence overlap problem, making it a scalable and efficient solution. CoreQ first utilizes K-core decomposition to analyze the network structure and extract hierarchical features that guide the process of seed node identification. Then, it employs a novel maximum likelihood-based approach to identify a sufficient number of candidate seed nodes from K-core hierarchies. Finally, it leverages Q-learning’s decision-making capabilities to optimize a policy for learning the most effective seed selection strategy. The experimental results demonstrate that CoreQ not only outperforms state-of-the-art IM methods in terms of influence spread under the weighted independent cascade model but also surpasses other competing learning-based IM methods in terms of time efficiency.