Influential Nodes Research Articles

A new semi-local centrality for identifying influential nodes based on local average shortest path with extended neighborhood

Quantifying the importance of nodes in complex networks is known as the problem of identifying influential nodes and is considered a critical aspect in interacting with these networks. This problem has many applications such as controlling rumors, sickness spreading, and viral marketing, where its importance has been understood by the research society in the last decade. This paper proposes a new semi-local centrality to identify influential nodes in complex networks based on the theory of Local Average Shortest Path with extended Neighborhood concept (LASPN). LASPN focuses on a distributed technique to extract the subgraph associated with each node and apply the average shortest path theory to it. We use the extended neighborhood concept to find the nearest neighbors of each node with low complexity, where this can lead to high efficiency in dealing with large-scale networks. In addition to applying relative changes in the average shortest path, the proposed metric considers the importance of the node itself as well as its nearest neighbors in ranking the nodes. Evaluation of the proposed centrality metric has been done through numerical simulations on several real-world networks. The results based on Kendall's τ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ au$$\\end{document} coefficient under the SIR infection spreading model show that LASPN improves the performance by 2.7% compared to the best available equivalent method.

Integration of Social Network Analysis in the Evaluation of Advertising Communication Effectiveness and Legal Compliance

A fuzzy genetic algorithm (FGA) is a hybrid optimization technique that combines the principles of fuzzy logic with genetic algorithms (GAs). Both fuzzy logic and genetic algorithms are powerful tools for optimization and decision-making in complex, uncertain environments. A Fuzzy Genetic Algorithm (FGA) can be a valuable approach for Social Network Analysis (SNA), where the complexity of social interactions often involves uncertainty and imprecision. This paper proposes an innovative approach to evaluate advertising communication effectiveness and legal compliance by integrating Social Network Analysis (SNA) with Sugeno Fuzzy Genetic Network Analysis (SF-GNA). By combining SNA's insights into communication dynamics and SF-GNA's ability to model complex relationships, the proposed framework offers a comprehensive assessment of advertising campaigns. The integration of SNA enables the identification of influential nodes and communication pathways within social networks, shedding light on the dissemination and reception of advertising messages. Concurrently, SF-GNA facilitates the analysis of legal compliance by assessing the fuzzy relationships between advertising content and regulatory requirements. Social Network Analysis identified key influencers within the network, with nodes A, B, and C demonstrating high centrality scores of 0.85, 0.79, and 0.76, respectively. SF-GNA evaluated the effectiveness of advertising messages, with a membership value of 0.92 indicating strong alignment with consumer preferences and engagement. SF-GNA analyzed the compliance of advertising content with regulatory standards, yielding a compliance score of 0.88, indicating adherence to legal requirements. With Specific regulatory aspects, such as truthfulness and data privacy, were evaluated with membership values of 0.90 and 0.85, respectively, indicating robust compliance.

Finding robust and influential nodes on directed networks using a memetic algorithm

The influence maximization problem is a research hotspot on social networks, which involves selecting a set of nodes as seeds to maximize the influence spread. The robust influence maximization problem is a further extension of the influence maximization problem considering the external factors pertaining to the propagation process. The current research mainly includes how to select the most influential nodes through algorithms, or how to improve the robustness of the network structure. However, these researches lack the application of the propagation models in directed networks, and also ignore the networked robustness on the information transmission. Therefore, this paper focuses on analyzing the characteristics of directed networks in the information propagation process, and further solve the robust influence maximization problem considering the directionality. First, a robust influence evaluation factor RSD is designed to assess the robust influence ability of each node on directed networks. Under the guidance of this evaluation factor, a problem-directed Memetic algorithm is developed to solve the robust influence maximization problem, named MA-RIMD. The characteristics of directed networks are considered involving with the genetic operators. The empirical analysis on several synthetic and practical networks indicates that MA-RIMD can select robust and influential seeds from directed networks, outperforming baseline approaches. Based on experiments conducted on three synthetic networks and two real-world networks, our proposed algorithm exhibits a performance improvement up to 15% over baseline approaches.

Identifying influential nodes based on the disassortativity and community structure of complex network

The complex networks exhibit significant heterogeneity in node connections, resulting in a few nodes playing critical roles in various scenarios, including decision-making, disease control, and population immunity. Therefore, accurately identifying these influential nodes that play crucial roles in networks is very important. Many methods have been proposed in different fields to solve this issue. This paper focuses on the different types of disassortativity existing in networks and innovatively introduces the concept of disassortativity of the node, namely, the inconsistency between the degree of a node and the degrees of its neighboring nodes, and proposes a measure of disassortativity of the node (DoN) by a step function. Furthermore, the paper analyzes and indicates that in many real-world network applications, such as online social networks, the influence of nodes within the network is often associated with disassortativity of the node and the community boundary structure of the network. Thus, the influential metric of node based on disassortativity and community structure (mDC) is proposed. Extensive experiments are conducted in synthetic and real networks, and the performance of the DoN and mDC is validated through network robustness experiments and immune experiment of disease infection. Experimental and analytical results demonstrate that compared to other state-of-the-art centrality measures, the proposed methods (DoN and mDC) exhibits superior identification performance and efficiency, particularly in non-disassortative networks and networks with clear community structures. Furthermore, we find that the DoN and mDC exhibit high stability to network noise and inaccuracies of the network data.

Weighted information index mining of key nodes through the perspective of evidential distance

Many strategies have been developed to mine critical nodes in the networks, however, most of them are based solely on the network topology, with no regard for the diversity of information between nodes. Using a weighted fusion method, we propose a method that considers neighboring nodes of second order and their similarity in this paper. Based on the evidence distance, an information distance Id is computed to find out how similar nodes are, and second-order adjacent nodes are used to reflect node information. Information distance is used to determine the weight of a node, and a weighted information index (WII) is used to evaluate how important a node is by combining the weight and information of nodes. The proposed method is tested by identifying influential nodes in twelve real-world networks and comparing their performance with the other seven methods. The Susceptible–Infected (SI) model and two evaluation indices have been applied to twelve real-world networks to evaluate the feasibility of our method. The findings indicate that the WII exhibits the most effective differentiation, thereby demonstrating the efficacy and practicality of our approach.

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.

Method Toward Network Embedding Within Homogeneous Attributed Network Using Influential Node Diffusion-Aware

Network embedding (NE) focuses on discovering low-dimensional embeddings of nodes while retaining their intrinsic features and structure of nodes. It is essential for many practical applications, containing text mining, community detection, and node classification. However, the great majority of existing systems are incapable of combining structural and attribute information. To tackle the above-mentioned problem, considering the information diffusion process, we present a novel model for attribute NE (ANE), namely influential node diffusion-based matrix factorization (INDMF), which contains topology level and attribute level. In detail, we first propose a novel method to extract high-order information via influential node diffusion sequences. Then, we regard the optimization of our proposed structure-based and attribute-based loss functions as a matrix factorization problem. Furthermore, this model can be used to generate final node embedding by aggregating the topology level and attribute level hierarchically. Experiments are conducted on four real-world datasets, which indicates that INDMF beats all competing algorithms in node categorization, community detection, and graph visualization.

Identifying Influential Nodes in Complex Networks From Semi-Local and Global Perspective

Identifying Influential Nodes in Complex Networks From Semi-Local and Global Perspective

Graph Theory and Algorithms for Social Network Analysis

Graph theory and algorithms play a crucial role in the analysis of social networks, which are complex systems composed of individuals or entities connected by various types of relationships. This paper provides an overview of graph theory concepts and algorithms commonly used in social network analysis. It discusses how these tools can be applied to understand the structure and dynamics of social networks, identify key actors or communities, and analyze information flow and influence propagation. The paper also highlights some of the challenges and future directions in the field of social network analysis using graph theory. The study of graph theory and algorithms for social network analysis is of significant importance due to its wide-ranging applications in various fields. Understanding the structure and dynamics of social networks can provide valuable insights into human behavior, information diffusion, organizational dynamics, and the spread of diseases or ideas. By applying graph theory concepts such as centrality measures, community detection algorithms, and link prediction techniques, researchers can uncover hidden patterns, identify influential nodes or groups, and predict future interactions in social networks. This knowledge can be used to design more effective strategies for marketing, public health interventions, social media management, and other areas where understanding and influencing human behavior are critical. Versatile with distinctive options. MOORA is a new optimization approach that has been proposed. This objective method signifies a matrix of potential replies, but it also proposes better policies based on the rates employed. Well established. For comparison, the reference point method is another multi-objective optimization technique. After several competitions, this proved to be the best pick among the methods. From the result network models is in first position whereas Graph density is in 5th position

Topic modeling and social network analysis approach to explore diabetes discourse on Twitter in India.

The utilization of social media presents a promising avenue for the prevention and management of diabetes. To effectively cater to the diabetes-related knowledge, support, and intervention needs of the community, it is imperative to attain a deeper understanding of the extent and content of discussions pertaining to this health issue. This study aims to assess and compare various topic modeling techniques to determine the most effective model for identifying the core themes in diabetes-related tweets, the sources responsible for disseminating this information, the reach of these themes, and the influential individuals within the Twitter community in India. Twitter messages from India, dated between 7 November 2022 and 28 February 2023, were collected using the Twitter API. The unsupervised machine learning topic models, namely, Latent Dirichlet Allocation (LDA), non-negative matrix factorization (NMF), BERTopic, and Top2Vec, were compared, and the best-performing model was used to identify common diabetes-related topics. Influential users were identified through social network analysis. The NMF model outperformed the LDA model, whereas BERTopic performed better than Top2Vec. Diabetes-related conversations revolved around eight topics, namely, promotion, management, drug and personal story, consequences, risk factors and research, raising awareness and providing support, diet, and opinion and lifestyle changes. The influential nodes identified were mainly health professionals and healthcare organizations. The study identified important topics of discussion along with health professionals and healthcare organizations involved in sharing diabetes-related information with the public. Collaborations among influential healthcare organizations, health professionals, and the government can foster awareness and prevent noncommunicable diseases.

Allostery in Protein Tyrosine Phosphatases is Enabled by Divergent Dynamics.

Dynamics-driven allostery provides important insights into the working mechanics of proteins, especially enzymes. In this study, we employ this paradigm to answer a basic question: in enzyme superfamilies, where the catalytic mechanism, active sites, and protein fold are conserved, what accounts for the difference in the catalytic prowess of the individual members? We show that when subtle changes in sequence do not translate to changes in structure, they do translate to changes in dynamics. We use sequentially diverse PTP1B, TbPTP1, and YopH as representatives of the conserved protein tyrosine phosphatase (PTP) superfamily. Using amino acid network analysis of group behavior (community analysis) and influential node dominance on networks (eigenvector centrality), we explain the dynamic basis of the catalytic variations seen between the three proteins. Importantly, we explain how a dynamics-based blueprint makes PTP1B amenable to allosteric control and how the same is abstracted in TbPTP1 and YopH.

The Identification of Influential Nodes Based on Neighborhood Information in Asymmetric Networks

Identifying influential nodes, with pivotal roles in practical domains like epidemic management, social information dissemination optimization, and transportation network security enhancement, is a critical research focus in complex network analysis. Researchers have long strived for rapid and precise identification approaches for these influential nodes that are significantly shaping network structures and functions. The recently developed SPON (sum of proportion of neighbors) method integrates information from the three-hop neighborhood of each node, proving more efficient and accurate in identifying influential nodes than traditional methods. However, SPON overlooks the heterogeneity of neighbor information, derived from the asymmetry properties of natural networks, leading to its lower accuracy in identifying essential nodes. To sustain the efficiency of the SPON method pertaining to the local method, as opposed to global approaches, we propose an improved local approach, called the SSPN (sum of the structural proportion of neighbors), adapted from the SPON method. The SSPN method classifies neighbors based on the h-index values of nodes, emphasizing the diversity of asymmetric neighbor structure information by considering the local clustering coefficient and addressing the accuracy limitations of the SPON method. To test the performance of the SSPN, we conducted simulation experiments on six real networks using the Susceptible–Infected–Removed (SIR) model. Our method demonstrates superior monotonicity, ranking accuracy, and robustness compared to seven benchmarks. These findings are valuable for developing effective methods to discover and safeguard influential nodes within complex networked systems.

HIKS: A K‐shell‐weighted hybrid approach method for detecting influential nodes in complex networks using possible edge weights

SummaryThe influential node in the network is the node that has a higher impact on network functioning compared to the other nodes. The influential node detection in the complex network is crucial for rumor containment, virus spreading, viral marketing, and so forth. The researchers designed several influential node detection methods; still, detecting community and influential node selection with minimal computational complexity by considering the relationship between the nodes is challenging. Hence, an optimal community detection along with the hybrid K‐shell decomposition method is introduced in this research. Initially, the optimal community from the complex network is identified to reduce the computation burden. For this, the Improved Dingo (IDingo) algorithm is introduced by hybridizing the hunting behavior of Dingo and the rough encircling behavior of Harris Hawk. After detecting the optimal community, the influential node identification is devised using the proposed hybrid K‐shell decomposition methods. The potential edge weights are considered while ranking the nodes. The performance of a proposed method is analyzed using six various datasets and accomplished the maximal cluster coefficient of 0.56578, 0.25674, 0.24022, 0.5968, 0.23419, and 0.10196 for Karate, Dolphins, C‐Elegance, Facebook, Gowalla, and Power Grid Dataset.

Relationships between psychopathological symptoms, pandemic-related stress, perceived social support, and COVID-19 infection history: a network analysis in Chinese college students.

Previous coronavirus, 2019 (COVID-19) research has applied network analysis to examine relationships between psychopathological symptoms but rarely extended to potential risk and protective factors or the influence of COVID-19 infection history. This study examined complex inter-relationships between psychopathological symptoms, COVID-19-related stressors, perceived social support, and COVID-19 infection history among Chinese university/college students during the peak of fifth pandemic wave using a network analysis approach. A Least Absolute Shrinkage and Selection Operator-regularized partial correlation network using Gaussian graphical model was constructed in 1,395 Chinese university/college students in Hong Kong who completed a survey between 15 March and 3 April, 2022. Depressive, anxiety, and acute/traumatic stress symptoms were measured by Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, and Impact of Event Scale-6, respectively. COVID-19-related stressors and perceived social support were measured. Network differences by COVID-19 infection history (COVID-network vs. no_COVID-network) and network communities were examined. Our results showed that the most influential nodes were depressed mood, uncontrollable worries, and uncontrollable thoughts about COVID-19. The main bridging symptoms were concentration problems and psychomotor problems. The COVID-network, comprising participants with a history of COVID-19 infection only, was significantly stronger than the no_COVID-network. Perceived social support and stress from conflicts with family/friends formed a unique community with negative cognition and suicidal idea in the COVID-network only. Our findings indicate that specific interventions targeting interpersonal conflicts and concentration problems as well as facilitating stress buffering effects of social support may represent effective strategies to reduce psychological distress in university/college students during COVID-19 and should be considered for future pandemic preparedness.

A Graph Sampling-Based Model for Influence Maximization in Large-Scale Social Networks

Social networks have attracted a great deal of attention and have, in fact, changed the way we produce, consume, and diffuse information. This change gave rise to the notion of social influence and today we talk about influential nodes. The process of detecting influential nodes in social networks aims to find entities that propagate information to a large portion of the network users. This process is often known as the influence maximization (IM) problem. Due to the explosive growth of social networks’ data, their structure is more complex and we talk about “big graph data.” Moreover, modern networks are dynamic and their topology or/and information is likely to change over time. Detecting influential nodes in such networks is a challenging task. Several methods have been developed in this context. However, they concentrate on static networks and there is little work on large-scale social networks. We propose in this article a new model for IM called MapReduce-based dynamic selection of influential nodes (MR-DSINs) that has the ability to cope with the huge size of real social networks. In fact, our approach is based on a graph sampling step in order to reduce the network’s size. Given that reduced version, MR-DSIN is able to select dynamically influential nodes. Our proposal has the advantage of considering the dynamics of information that can be modeled by users’ social actions (e.g.“, share”, “comment”, “retweet”). Experimental results on real-world social networks and computer-generated artificial graphs demonstrate that MR-DSIN is efficient for identifying influential nodes, compared with three known proposals. We prove that our model is able to detect in the reduced graph an influence as important as in the original one.

Social-ecological perspective on the suicidal behaviour factors of early adolescents in China: a network analysis

BackgroundIn early adolescence, youth are highly prone to suicidal behaviours. Identifying modifiable risk factors during this critical phase is a priority to inform effective suicide prevention strategies.AimsTo explore the risk...

An insight into topological, machine and Deep Learning-based approaches for influential node identification in social media networks: a systematic review

An insight into topological, machine and Deep Learning-based approaches for influential node identification in social media networks: a systematic review

Multi-factor information matrix: A directed weighted method to identify influential nodes in social networks

Identifying super-propagators on social networks takes on critical significance in guiding and controlling public opinions on the internet, information retrieval, viral marketing and so on. The influence of nodes is enhanced under the dual role of network structure and dynamic interactions, while existing research tends to attach great importance on the network structure. This means the interaction behaviors between nodes are ignored, which indicates that the performance of algorithms still needs to be improved. Moreover, only a minority of work has been conducted on directed weighted networks. To address these problems, from the perspectives of the network topology and dynamic interaction, a multi-factor information matrix centrality algorithm is developed in this study in accordance with the characteristics of information propagation process and three degrees of influence rule, with the node influence, neighbor influence, and mutual influence exerted by information feedback considered simultaneously. Based on this, the subjective and objective weighted methods are combined to enhance the accuracy of evaluation results, then the multi-factor matrix can be obtained. Four comparative experiments are employed to verify the effectiveness of proposed method with ten networks and nine existing centralities applied. The results of the experiments confirm the validity and superiority of the proposed method in terms of differentiation, connectivity and propagation performance. Moreover, its low time complexity makes the algorithm can be used in large-scale social networks. In general, methodological and multidimensional support is provided for the accurate identification of super-propagators in online social networks.

Identifying influential spreaders in complex networks based on density entropy and community structure

In recent years, exploring the relationship between community structure and node centrality in complex networks has gained significant attention from researchers, given its fundamental theoretical significance and practical implications. To address the impact of network communities on target nodes and effectively identify highly influential nodes with strong propagation capabilities, this paper proposes a novel influential spreaders identification algorithm based on density entropy and community structure (DECS). The proposed method initially integrates a community detection algorithm to obtain the community partition results of the networks. It then comprehensively considers the internal and external density entropies and degree centrality of the target node to evaluate its influence. Experimental validation is conducted on eight networks of varying sizes through susceptible–infected–recovered (SIR) propagation experiments and network static attack experiments. The experimental results demonstrate that the proposed method outperforms five other node centrality methods under the same comparative conditions, particularly in terms of information spreading capability, thereby enhancing the accurate identification of critical nodes in networks.

Quantifying influential nodes in complex networks using optimization and particle dynamics: a comparative study

Quantifying influential nodes in complex networks using optimization and particle dynamics: a comparative study