Identification Of Influential Nodes Research Articles

A graph convolutional network model based on regular equivalence for identifying influential nodes in complex networks

The identification of influential nodes in complex networks is an interesting problem. Traditional approaches, such as metric- and machine learning-based approaches, often fail to adequately consider the network structure or node features, limiting their applications in certain scenarios. Deep learning-based methods, particularly those using graph neural networks (GNNs), have yielded remarkable results in this field. However, there is a lack of utilization of the regular equivalence-based similarity between nodes to feature construction, which is critical to deep learning-based methods. Nevertheless, regular equivalence-based similarity is extremely suitable to the quantification of the similarity between influential nodes, as these nodes usually have similar structural properties but do not require common neighbors. Building upon graph convolutional network (GCN, a specific type of GNN) and the regular equivalence-based similarity, this study proposes a novel method for identifying influential nodes in complex networks. Particularly, the regular equivalence-based similarities between nodes are considered to construct node features; a GCN is enhanced to improve the identification of influential nodes. Experiments on several datasets indicate that the proposed method is superior to state-of-the-art methods, demonstrating the advantages of the proposed feature construction based on regular equivalence-based similarity over the identification of influential nodes.

Identification of Influential Nodes in Social Network: Big Data - Hadoop

Software development and associated data is the most critical factor these days. Currently, people are living in an internet world where data and related artifacts are major sets of information these days. The data is correlated with real-world data. The analysis of large datasets was done as part of the experimental analysis. The dataset for online social media like Facebook and Twitter was taken for the identification of influential nodes. The analysis of the dataset provides an overview and observation of the dataset for Facebook or Twitter. Here, in the current activity, an overview of cloud computing and big data technologies are discussed along with effective methods and approaches to resolve the problem statement. Particularly, big data technologies such as Hadoop provided by Apache for processing and analysis of Gigabyte(GB) or petabyte(PB) scale datasets are discussed for processing data in distributed and parallel data fashion. Here, the processing of large datasets is done by big data technology by implementing Apache Hadoop in online social media.

Identifying influential nodes on directed networks

Identifying influential nodes on directed networks is a challenging and widely studied task that keeps drawing extensive attention from both academia and industry. The simultaneous consideration of both local and global structural information has demonstrated its effectiveness in identifying influential nodes on un-directed networks. Nevertheless, how to better utilize these two types of information on directed networks remains a challenge. In this paper, we address the influential nodes identification problem for directed networks where a node can directly affect its in-neighbors, like the social network of Twitter. We present a general iterative framework that integrates both local structural information and global influence. The global influence exerted by the target node is determined as the cumulative sum of the global influences originating from its in-neighbors, achieved through an iterative procedure. Meanwhile, the in-degree of the target node is leveraged to capture local structural information, which is consistently reinforced throughout the iterative process to prevent the attenuation of its significance over successive iterations. Our algorithm demonstrates significant performance improvement, averaging 21.61% in Kendall's τ and 23.43% in precision@0.05 over the 8 benchmarks across 15 real networks. Moreover, it outperforms the benchmarks on artificial networks, and can effectively identify fast influencers.

Dismantling complex networks with graph contrastive learning and multi-hop aggregation

Network dismantling is a process of identifying influential nodes that can decompose a network into disconnected sub-networks. This provides a novel approach to understanding and analyzing complex networks abstracted from the real world. State-of-the-art solutions for this task exploit graph encoders to capture the structural features of the network, which are then sent to the multi-layer perceptron for predicting the node importance. This process, however, fails to exploit the interactions among the graph representations learned from different views and neglects the neighboring information when evaluating node importance. In this work, we address these issues with a graph contrastive learning framework with multi-hop aggregation, resulting in the identification of influential nodes. Firstly, we construct role graphs to provide a holistic view of the original graphs. Secondly, graph representations are obtained in the individual views, and enhanced expressiveness is achieved through contrastive learning. Finally, based on the representations, the multi-hop neighbor information of the nodes is aggregated to rank the node importance, and thus aid in the identification of important nodes. We evaluate our proposal on real and synthetic networks, and the results show that our method outperforms the baseline with fewer nodes required to disassemble a network.

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.

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.

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

New approaches for the identification of influential and critical nodes in an electric grid

New approaches for the identification of influential and critical nodes in an electric grid

A new edge weight-based measure for k-shell influential node identification in complex networks

A new edge weight-based measure for k-shell influential node identification in complex networks

Gray Correlation Entropy-Based Influential Nodes Identification and Destruction Resistance of Rail-Water Intermodal Coal Transportation Network

Evaluating the importance of nodes in coal transportation networks and identifying influential nodes is a crucial study in the field of network science, vital for ensuring the stable operation of such complex networks. However, most existing studies focus on the performance analysis of single-medium networks, lacking research on combined transportation, which is not applicable to China’s coal transportation model. To address this issue, this paper first establishes a static topological structure of China’s coal-iron-water combined transportation network based on complex network theory, constructing a node importance evaluation index system through four centrality indicators. Subsequently, an enhanced TOPSIS method (GRE-TOPSIS) is proposed based on the Grey Relational Entropy Weight (GRE) to identify key nodes in the complex network from local and positional information dimensions. Compared to previous studies, this research emphasizes composite networks, breaking through the limitations of single-medium network research, and combines gray relational analysis with entropy weighting, enhancing the objectivity of the TOPSIS method. In the simulation section of this paper, we establish the model of China’s coal-iron-water combined transportation network and use the algorithm to comprehensively rank and identify key nodes in 84 nodes, verifying its performance. Network efficiency and three other parameters are used as measures of network performance. Through simulated deliberate and random attacks on the network, the changing trends in network performance are analyzed. The results show that in random attacks, the performance drops to around 50% after damaging nearly 40 ordinary nodes. In contrast, targeting close to 16 key nodes in deliberate attacks achieves a similar effect. Once key nodes are well protected, the network exhibits a certain resistance to damage.

Optimizing an mHealth Program to Promote Type 2 Diabetes Prevention in High-Risk Individuals: Cross-Sectional Questionnaire Study.

We evaluated the outcomes of a pilot SMS text messaging-based public health campaign that identified social networking nodes and variations of response rates to develop a list of variables that could be used to analyze and develop an outreach strategy that would maximize the impact of future public health campaigns planned for Kuwait. Computational analysis of connections has been used to analyze the spread of infectious diseases, dissemination of new thoughts and ideas, efficiency of logistics networks, and even public health care campaigns. Percolation theory network analysis provides a mathematical alternative to more established heuristic approaches that have been used to optimize network development. We report on a pilot study designed to identify and treat subjects at high risk of developing type 2 diabetes mellitus in Kuwait. The aim of this study was to identify ways to optimize efficient deployment of resources and improve response rates in a public health campaign by using variables identified in this secondary analysis of our previously published data (Alqabandi et al, 2020). This analysis identified key variables that could be used in a computational analysis to plan for future public health campaigns. SMS text message screening posts were sent inviting recipients to answer 6 questions to determine their risk of developing type 2 diabetes mellitus. If subjects agreed to participate, a link to the Centers for Disease Control and Prevention prediabetes screening test was automatically transmitted to their mobile devices. The phone numbers used in this campaign were recorded and compared to the responses received through SMS text messaging and social media forwarding. A total of 180,000 SMS text messages through 5 different campaigns were sent to 6% of the adult population in Kuwait. A total of 260 individuals agreed to participate, of which 153 (58.8%) completed the screening. Remarkably, 367 additional surveys were received from individuals who were not invited by the original circulated SMS text messages. These individuals were invited through forwarded surveys from the original recipients after authentication with the study center. The original SMS text messages were found to successfully identify influencers in existing social networks to improve the efficacy of the public health campaign. SMS text messaging-based health care screening campaigns were found to have limited effectiveness alone; however, the increased reach through shared second-party forwarding suggests the potential of exponentially expanding the reach of the study and identifying a higher percentage of eligible candidates through the use of percolation theory. Future research should be directed toward designing SMS text messaging campaigns that support a combination of SMS text message invitations and social networks along with identification of influential nodes and key variables, which are likely unique to the environment and cultural background of the population, using percolation theory modeling and chatbots.

Influential process nodes identification strategy for aircraft assembly system based on complex network and improved PageRank

In the manufacturing network, the fluctuation of process operation time can result in the delay of the total completion time, which brings a huge challenge to controlling entire production schedule. For aircraft manufacturing industries, the processes that have a significant impact on the completion time of the assembly line need to be identified and monitored in order to ensure the final assembly efficiency and reliability. However, due to the large number of process nodes and the complexity of resource constraints and relationship constraints, traditional node centrality algorithms cannot identify influential nodes accurately. Therefore, based on complex network theory, this paper studies the influential nodes identification problem in the context of assembly lines. Firstly, The Resource-Process Coupling Network for the assembly line is constructed based on the production background and network resource characteristics. Then, based on PageRank algorithm and the idea of resource iteration, Improved PageRank Algorithm (IPRA) is proposed to identify influential process nodes, which brings the resource and time parameters into the allocation rules. Finally, according to simulation results, a comparative analysis of IPRA and existing algorithms is conducted and concludes that our method can better identify influential nodes in actual complex production networks. Furthermore, this paper identifies influential process nodes of aircraft assembly line based on the case of the commercial aircraft manufactory.

Identification of Influential Nodes in Complex Networks With Degree and Average Neighbor Degree

Identifying influential nodes in complex networks is vital for understanding their structure and dynamic behavior. Although methods based on a single characteristic of nodes have been demonstrated to be effective in specific scenarios, the information they provide in dealing with the global aspect of the network is often limited or incomplete. In this paper, we present a measure that combines the degree and the average neighbor degree to evaluate the influence of nodes. As a supplement, we also propose the corresponding gravity index. Experiments on synthetic and real-world networks show that the method proposed in this paper is superior to previous methods based on the single characteristic of nodes in most scenarios. Our gravity strategy is also highly competitive compared with the current famous gravity method.

CAGM: A communicability-based adaptive gravity model for influential nodes identification in complex networks

Identifying influential nodes is a very hot and challenging issue in the field of complex system and network. A great deal of algorithms have been developed to address the influential nodes identification problem, but most of previous studies are compromising between result accuracy and time cost. In this work, we propose a communicability-based adaptive gravity model (CAGM) for influential nodes identification. The key idea of CAGM algorithm is that the importance of each node is evaluated by comprehensively considering the influence probability and influence intensity information of neighbor nodes located in its influence radius. More specifically, the communicability network matrix is introduced to depict the influence probability between each pair of nodes. By integrating k-shell, degree and distance information, the influence radius of each node can be determined uniquely so as to portray the inherent heterogeneity of nodes in complex networks. To verify the effectiveness and applicability of CAGM, several groups of simulated experiments on twelve real and artificial datasets are concluded. Experimental results show that CAGM performs better than eight popular algorithms in terms of top-10 nodes, discrimination ability, imprecision function and ranking accuracy.

Identification of Influential Nodes in Social Network: Graph Approach

The identification of nodes that are actually influencing other nodes is one of the core and fundamental research problems in the social network data sciences. There are multiple proven techniques available globally that depict the influence of the root node on the remaining nodes in the given network. Although methods and methodology is already proven and multiple experiments have been carried out in this area, the current research work is an extension of the influential node identification problem with the perspective of graph traversal. To prove the methodology and new approach a tree and AUC (area under the curve) - based analysis method has been proposed, TArank in which the information collected from the breadth-first search tree is used to identify influential nodes. From the obtained results and analysis, the effectiveness of TARank in the identification of influential nodes has been validated. The concentration of data at a particular segment of the plot is observed. Additionally, the tree view of the analysis predicts the root and leaf node thus helping in the identification of the most influential nodes in the online social media network. Keywords: Influential Node, PCC, TARank, Degree Centrality, PCC

Identifying influential nodes in complex networks based on network embedding and local structure entropy

The identification of influential nodes in complex networks remains a crucial research direction, as it paves the way for analyzing and controlling information diffusion. The currently presented network embedding algorithms are capable of representing high-dimensional and sparse networks with low-dimensional and dense vector spaces, which not only keeps the network structure but also has high accuracy. In this work, a novel centrality approach based on network embedding and local structure entropy, called the ELSEC, is proposed for capturing richer information to evaluate the importance of nodes from the view of local and global perspectives. In short, firstly, the local structure entropy is used to measure the self importance of nodes. Secondly, the network is mapped to a vector space to calculate the Manhattan distance between nodes by using the Node2vec network embedding algorithm, and the global importance of nodes is defined by combining the correlation coefficients. To reveal the effectiveness of the ELSEC, we select three types of algorithms for identifying key nodes as contrast approaches, including methods based on node centrality, optimal decycling based algorithms and graph partition based methods, and conduct experiments on ten real networks for correlation, ranking monotonicity, accuracy of high ranking nodes and the size of the giant connected component. Experimental results show that the ELSEC algorithm has excellent ability to identify influential nodes.

Influential Node Detection and Ranking With Fusion of Heterogeneous Social Media Information

Identification of influential nodes has emerged as one of the major challenges, especially after their use in the rapid propagation of information, epidemics, and so on in social media. Most of the previous works in this field deal with the homogeneous interactions that are not pertinent in the determination of the accurate context of the nodes due to their noisy and sparse nature. Hence, heterogeneous interactions need to be explored for the identification of influential nodes in the network. To consider heterogeneous interactions available within the network, a multilayer network (ML) has been designed in this work. Each layer of the network represents a particular type of interaction, e.g., upload, comment, retweet, reply, and mention. A heterogeneous degree ranking (HDR)-based influential nodes’ detection and ranking are proposed for the designed ML. Furthermore, multiple-criteria decision-making (MCDM) methods, such as the analytic hierarchy process (AHP), the technique for order of preference by similarity to ideal solution (TOPSIS), fuzzy AHP, fuzzy TOPSIS, and the analytic network process (ANP), are explored for the proposed ML for identification and ranking of the influential nodes. The susceptible–infected–recovered (SIR) model is used to evaluate the proposed work. In addition to this, statistical analysis is performed using the Pearson correlation, Kendall’s correlation, Spearman’s correlation, and the Friedman test on the ranks generated by different methods, which shows that the results generated by different proposed methods are consistent. Furthermore, the performance of the proposed method is compared with state-of-the-art approaches.

Identification of influential nodes with Shapley Influence Maximization Extremal Optimization algorithm

The Influence Maximization Problem is a challenging computational task with multiple real-world applications. A new approach to this problem based on cooperative game theory and optimization called the Shapley Influence Maximization Extremal Optimization approach is proposed. The influence maximization problem for the independent cascade model is considered as a cooperative game, where players seek to choose seeder nodes to maximize the size of the influence set of their cascade model by maximizing their average marginal contribution to all possible coalitions. Numerical experiments conducted on both synthetic and real-world networks and comparisons with state-of-the-art algorithms show the potential of the proposed approach.

Integrating local and global information to identify influential nodes in complex networks

Centrality analysis is a crucial tool for understanding the role of nodes in a network, but it is unclear how different centrality measures provide much unique information. To improve the identification of influential nodes in a network, we propose a new method called Hybrid-GSM (H-GSM) that combines the K-shell decomposition approach and Degree Centrality. H-GSM characterizes the impact of nodes more precisely than the Global Structure Model (GSM), which cannot distinguish the importance of each node. We evaluate the performance of H-GSM using the SIR model to simulate the propagation process of six real-world networks. Our method outperforms other approaches regarding computational complexity, node discrimination, and accuracy. Our findings demonstrate the proposed H-GSM as an effective method for identifying influential nodes in complex networks.