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The identification of influential nodes in complex networks is a hot topic among scholars. Classical methods commonly analyze single node information or static structures but seldom emphasize dynamic properties and the interactive influence of nodes. Here, we proposed an improved Dynamic-Sensitive centrality (IDS) method by considering the interactive influence of both the self and neighbor nodes. Based on six real aviation networks and the Susceptible Infected Recovered (SIR) spreading disease model, we simulated the actual spreading process within these networks. Relevant experiments were conducted through Kendall’s correlation coefficient, the imprecision function, and the complementary cumulative distribution function. The experimental results demonstrated that the IDS can more accurately identify the influential node and effectively differentiate the node influence in the network compared with other benchmark methods. Especially in the EU air-2 network, the IDS results in Kendall’s correlation coefficient are improved by 105% compared to the DS centrality. [ABSTRACT FROM AUTHOR]

Influential node detection is crucial for understanding and managing real-world networks, such as social, biological, and information networks, as it helps identify key participants and network dynamics. This paper introduces a novel approach, the Internal-External Overlapping Community Detection method, which aims to uncover overlapping communities within networks to identify influential nodes. We propose a new metric, the Influence Detection Factor, designed to pinpoint nodes that significantly impact network behavior and evolution. By examining state transitions in time-varying networks, our approach provides valuable insights into how these influential nodes drive changes over time, contributing to a deeper understanding of network resilience and adaptation. As a case study, we analyze the spread of COVID-19 in India, where provinces are represented as nodes and number of cases as edge weights. We compare our method against traditional centrality measures, such as degree, closeness, and betweenness centrality, demonstrating that our approach aligns more closely with real-world epidemiological data and offers superior modularity, and higher F1 scores. Our experimental results underscore the efficacy of the proposed method in capturing and forecasting network behavior, making it a powerful tool for dynamic social network analysis and providing actionable insights for public health interventions and epidemic management strategies. [ABSTRACT FROM AUTHOR]

Summary: The 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. [ABSTRACT FROM AUTHOR]

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 coefficient under the SIR infection spreading model show that LASPN improves the performance by 2.7% compared to the best available equivalent method. [ABSTRACT FROM AUTHOR]

Text network has been of research interest in the computational domain. A text network contains textual documents with dynamic content. The textual documents linguistically depend upon the grammar and use of words. We have proposed determining relevant words in dynamic content using dynamic complex network approaches. We considered dynamic news updates for exploring our analysis of text networks. This work proposes a complex network-based approach for determining the change of important words with dynamic content. We have suggested determining relevant words from dynamic content using degree centrality, closeness centrality and clustering coefficient. We have proposed Affiliation Factor for bond strength determination among the important words. We have considered an instance of live updates in the news for our approach. The news updates on a particular live incident are uploaded dynamically in a thread. We have introduced the Instance Factor for perceiving the relevance of the important words throughout the document, apart from considering the co-occurrence frequency. The approach is free of semantic analysis and, consequently, exempt depending on any corpus. The analytical results show that the varying important words can be efficiently determined from the dynamic content of the text network compared to the other approaches. [ABSTRACT FROM AUTHOR]

The popularity of social networks is growing and offering promising opportunities for practical applications, such as computer networks, social systems, physical systems, and biological networks. The use of networks provides valuable insights into the patterns, relationships, and behavior of various entities and their interactions. Healthcare service systems are critical social systems that can be analyzed using network-based methodologies, one of which is influence maximization (IM). It is a network analysis technique that identifies a small subset of suppliers in a network that has the most influence on the overall behavior of the healthcare system. By utilizing network analysis techniques, data scientists can identify the most crucial healthcare providers in a network and better understand the system's operation. The paper uses a network global structure-based centrality (ngsc) approach that combines conventional k-shell and the sum of neighbors' degree methods with information about the network's global structural features to identify significant providers in the provider-interacting network. The experimental outcomes on five real-world networks reveal that the proposed NGSC technique outperforms earlier benchmark methods in identifying significant providers under a spreading dynamic when using the IC model. [ABSTRACT FROM AUTHOR]

Identifying the seed nodes in networks is an important task for understanding the dynamics of information diffusion. It has many applications, such as energy usage/consumption, rumor control, viral marketing, and opinion monitoring. When compared to other nodes, seed nodes have the potential to spread information in the majority of networks. To identify seed nodes, researchers gave centrality measures based on network structures. Centrality measures based on local structure are degree, semi-local, Pagerank centralities, etc. Centrality measures based on global structure are betweenness, closeness, eigenvector, etc. Very few centrality measures exist based on the network's local and global structure. We define mixed centrality measures based on the local and global structure of the network. We propose a measure based on degree, the shortest path between vertices, and any global centrality. We generalized the definition of our mixed centrality, where we can use any measure defined on a network's global structure. By using this mixed centrality, we identify the seed nodes of various real-world networks. We also show that this mixed centrality gives good results compared with existing basic centrality measures. We also tune the different real-world parameters to study the effect of their maximum influence. [ABSTRACT FROM AUTHOR]

The identification of a central node in a network is one of the important tasks of social networks. Nowadays, the central node helps grow online businesses, spread news, advertisements, etc. Existing methods for centrality measurement capture the direct reachability of the node. In social networks, parameters such as relationships among the nodes are generally uncertain. This uncertainty can be tracked using either probability theory or fuzzy theory. In this article, the fuzzy theory, particularly the neutrosophic fuzzy theory, is used because, in this concept, more information, such as true values, falsity and indeterminacy, is incorporated. Thus, the representation of social networks using neutrosophic graphs gives more information compared to fuzzy graphs. This study introduces a new form of centrality measurement using a neutrosophic graph. This measurement considers the different merits of individuals in a network. Individual merits (self-weight) have been included in the proposed method. A small network of university faculty members has been considered to illustrate the problem and to demonstrate the potential fields of application of this new method of centrality measurement. [ABSTRACT FROM AUTHOR]

Identifying influential nodes is a key research topic in complex networks, and there have been many studies based on complex networks to explore the influence of nodes. Graph neural networks (GNNs) have emerged as a prominent deep learning architecture, capable of efficiently aggregating node information and discerning node influence. However, existing graph neural networks often ignore the strength of the relationships between nodes when aggregating information about neighboring nodes. In complex networks, neighboring nodes often do not have the same influence on the target node, so the existing graph neural network methods are not effective. In addition, the diversity of complex networks also makes it difficult to adapt node features with a single attribute to different types of networks. To address the above problems, the paper constructs node input features using information entropy combined with the node degree value and the average degree of the neighbor, and proposes a simple and effective graph neural network model. The model obtains the strength of the relationships between nodes by considering the degree of neighborhood overlap, and uses this as the basis for message passing, thereby effectively aggregating information about nodes and their neighborhoods. Experiments are conducted on 12 real networks, using the SIR model to verify the effectiveness of the model with the benchmark method. The experimental results show that the model can identify the influence of nodes in complex networks more effectively. [ABSTRACT FROM AUTHOR]

Influential nodes identification technology is one of the important topics which has been widely applied to logistics node location, social information dissemination, transportation network carrying, biological virus dissemination, power network anti-destruction, etc. At present, a large number of influential nodes identification methods have been studied, but the algorithms that are simple to execute, have high accuracy and can be better applied to real networks are still the focus of research. Therefore, due to the advantages of simple to execute in voting mechanism, a novel algorithm based on adaptive adjustment of voting ability (AAVA) to identify the influential nodes is presented by considering the local attributes of node and the voting contribution of its neighbor nodes, to solve the problem of low accuracy and discrimination of the existing algorithms. This proposed algorithm uses the similarity between the voting node and the voted node to dynamically adjust its voting ability without setting any parameters, so that a node can contribute different voting abilities to different neighbor nodes. To verify the performance of AAVA algorithm, the running results of 13 algorithms are analyzed and compared on 10 different networks with the SIR model as a reference. The experimental results show that the influential nodes identified by AAVA have high consistency with SIR model in Top-10 nodes and Kendall correlation, and have better infection effect of the network. Therefore, it is proved that AAV algorithm has high accuracy and effectiveness, and can be applied to real complex networks of different types and sizes.

The evaluation of node importance is a critical research topic in network science, widely applied in social networks, transport systems, and computer networks. Prior works addressing this topic either consider a single metric or assign weights for multiple metrics or select features by handcraft, which exist one-sidedness and subjectivity issues. In this paper, to tackle these problems, we propose a new approach named CGNN to identify influential nodes based on deep learning methods, including Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs). CGNN obtains the feature matrices by the contraction algorithm and gets the labels by the Susceptible-Infected-Recovered (SIR) model, which will be leveraged for learning the hidden representations of nodes without utilizing any network metrics as features. We adopt three evaluation criteria to verify CGNN concerning effectiveness and distinguishability, including Kendall's τ correlation coefficient, monotonicity index (MI), and ranking distribution function (RDF). Nine baselines are employed to compare with CGNN on thirty synthetic networks and twelve real-world networks from different domains. Simulation results demonstrate that CGNN manifests better performance than the baselines, in which the values of τ are large and significantly increase, the values of MI approach to 1, and the points in the RDF curves distribute more uniformly. These results may provide reference significance for controlling epidemic spreading and enhancing network robustness. [ABSTRACT FROM AUTHOR]

Identifying important actors (or nodes) in a two-mode network is a crucial challenge in mining, analyzing, and interpreting real-world networks. While traditional bipartite centrality indices are often used to recognize key nodes that influence the network information flow, inaccurate results are frequently obtained in intricate situations such as massive networks with complex local structures or a lack of complete knowledge about the network topology and certain properties. In this paper, we introduce Bi-face (BF), a new bipartite centrality measurement for identifying important nodes in two-mode networks. Using the powerful mathematical formalism of Formal Concept Analysis, the BF measure exploits the faces of concept intents to detect nodes that have influential bicliques connectivity and are not located in irrelevant bridges. Unlike off-the shelf centrality indices, it quantifies how a node has a cohesive substructure influence on its neighbour nodes via bicliques while not being in network core-peripheral ones through its absence from non-influential bridges. In terms of identifying accurate node centrality, our experiments on a variety of real-world and synthetic networks show that BF outperforms several state-of-the art bipartite centrality measures, producing the most accurate Kendall coefficient. It provides unique node identification based on network topology. The findings also demonstrate that the presence of terminal nodes, influential bridges, and overlapping key bicliques impacts both the performance and behaviour of BF as well as its relationship with other traditional centrality measures. On the datasets tested, the computation of BF is at least twenty-three times faster than betweenness, eleven times faster than percolation, nine times faster than eigenvector, and ten times faster than closeness.

The discovery of community structures in social networks has gained significant attention since it is a fundamental problem in understanding the networks' topology and functions. However, most social network data are collected from partially observable networks with both missing nodes and edges. In this article, we address a new problem of detecting overlapping community structures in the context of such an incomplete network, where communities in the network are allowed to overlap since nodes belong to multiple communities at once. To solve this problem, we introduce KroMFac, a new framework that conducts community detection via regularized nonnegative matrix factorization (NMF) based on the Kronecker graph model. Specifically, from an inferred Kronecker generative parameter matrix, we first estimate the missing part of the network. As our major contribution to the proposed framework, to improve community detection accuracy, we then characterize and select influential nodes (which tend to have high degrees) by ranking, and add them to the existing graph. Finally, we uncover the community structures by solving the regularized NMF-aided optimization problem in terms of maximizing the likelihood of the underlying graph. Furthermore, adopting normalized mutual information (NMI), we empirically show superiority of our KroMFac approach over two baseline schemes by using both synthetic and real-world networks. [ABSTRACT FROM AUTHOR]

Summary: Biological systems are composed of highly complex networks, and decoding the functional significance of individual network components is critical for understanding healthy and diseased states. Several algorithms have been designed to identify the most influential regulatory points within a network. However, current methods do not address all the topological dimensions of a network or correct for inherent positional biases, which limits their applicability. To overcome this computational deficit, we undertook a statistical assessment of 200 real-world and simulated networks to decipher associations between centrality measures and developed an algorithm termed Integrated Value of Influence (IVI), which integrates the most important and commonly used network centrality measures in an unbiased way. When compared against 12 other contemporary influential node identification methods on ten different networks, the IVI algorithm outperformed all other assessed methods. Using this versatile method, network researchers can now identify the most influential network nodes. The Bigger Picture: Decoding the information buried within the interconnection of components could have several benefits for the smart control of a complex system. One of the major challenges in this regard is the identification of the most influential individuals that have the potential to cause the highest impact on the entire network. This knowledge could provide the ability to increase network efficiency and reduce costs. In this article, we present a novel algorithm termed the Integrated Value of Influence (IVI) that combines the most important topological characteristics of the network to identify the key individuals within it. The IVI is a versatile method that could benefit several fields such as sociology, economics, transportation, biology, and medicine. In biomedical research, for instance, identification of the true influential nodes within a disease-associated network could lead to the discovery of novel biomarkers and/or drug targets, a process that could have a considerable impact on society.

Social Networks is an essential phenomenon in all aspects through various perspectives. These networks contain a large number of users better termed as nodes and the connections between the users termed as edges. For efficient information processing and retrieving, accessing the influential node is essential for improving the diffusion process. To identify the influential node inside a heterogeneous community, incorporating probability metrics with regression classifier is put forth stated by proposed method Support Vector Bayesian Machine (SVBM). Node metrics such as degree centrality, closeness centrality is measured for eliminating the nodes primarily. A standardized index based on the centrality values computed for enhancing into SVBM. After the standardized index, similarity dissimilarity index values evaluated by combining the Euclidean, Hamming, Pearson coefficient for valued relations and Jaccard for binary relations which results in a single index value considered as the power degree value(p). The value p determines the node's boundedness, which indicates the range of influence within the community. The outlier nodes in the bounded region get eliminated, and the nodes remaining taken for the final phase of SVBM, probability regression line predicts the node inhibiting the most influential nature. Experimental evaluation of the proposed system with the existing Support Vector Machine (SVM) technique resulted in 0.95 and 0.41 respectively for Area Under Curve (AUC) denoting that the true positive influential node classification process from the other existing nodes was higher than SVM. In comparison with the existing SVM, the proposed methodology SVBM attained a node detection, which influenced a higher diffusion rate within the networks. [ABSTRACT FROM AUTHOR]

Link Prediction, which aims to infer the missing or future connections between two nodes, is a key step in many complex network analysis areas such as social friend recommendation and protein function prediction. A majority of existing efforts are devoted to define the influence of neighbor nodes. However, even though recent studies show that node attributes have an added value to network structure for accurate link prediction, it still remains ignoring the real node influence. To address this problem, in this paper we investigate influential node identification technique to formulate a node ranking-based link prediction metric. The general idea of our approach is to exploit the ranking score as the contribution of a common neighbor. Such fundamental mechanism preserve both local structure and global information. Experimental results on real-world networks with two scenario demonstrate that our proposed metrics achieves better performance than existing state-of-the-art local and global similarity methods. • We first present a general link prediction framework based on Influential Nodes Identification (INI) technique. • The framework are generalized as it is suitable to different types of networks and can be plugged in any INI metric. • The ranking score of common neighbor has been introduced to calculate similarity between nodes. • Substantial experiments are presented to validate the effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

Identifying the most influential nodes is one of the most promising domains in understanding and controlling propagation processes in complex network. According to the percolation theory, there is a epidemic threshold difference between the residual network and the original network after removing a node. We think that the threshold difference can represent the node’s global influence, which the absence of the node can promote or suppress the epidemic outbreak. By considering threshold differences and the local property (degree centrality), we propose a comprehensive influence method (CI) to identify the influential nodes. Comparing with the susceptible-infected-recovered model, the experimental results for nine empirical networks show that the CI method which can be applied to most networks with the different structures is more accurate than the K -shell, degree, closeness, and eigenvector centralities. [ABSTRACT FROM AUTHOR]

One of the most important problems in complex network is the identification of the influential nodes. For this purpose, the use of differences and similarities of structure to enrich the centrality method in complex networks is proposed. The centrality method called ECDS centrality used is the eigen-centrality which is based on the Jaccard similarities between the two random nodes. This can be described by an eigenvalues problem. Here, we use a tunable parameter α to adjust the influence of the differences and similarities. Comparing with the results of the Susceptible Infected Recovered (SIR) model for four real networks, the ECDS centrality could identify influential nodes more accurately than the tradition centralities such as the k -shell, degree and closeness centralities. Especially, in the Erdös network, the Kendall’s tau could be reached to 0.93 when the spreading rate is 0.12. In the US airline network, the Kendall’s tau could be reached to 0.95 when the spreading rate is 0.06. [ABSTRACT FROM AUTHOR]

In networks, dynamic phenomena such as opinions, behaviors, and information are propagated through connections between entities. Indeed, one of the main issues about a dynamic process is to find a set of individuals with a high influence on other’s decisions which is defined as the “influence maximization” problem, and aims to find a subset of nodes to maximize the total number of adopters at the end of the process. In this paper, by combining the community structure and influence maximization problem, we proposed a two-layered method for identifying influential nodes so that in the first layer an optimization-based method is applied to detect the potential communities. Then, in the second layer, a criterion is used which is a tradeoff between the low-relevant centralities and methods with high complexity. Our method is implemented on real social networks with different scales, and the performance is evaluated by using the total number of infected nodes at the end of the process. The experimental results indicate the superiority of our method in comparison to other considered approaches by considering the efficiency and scalability. [ABSTRACT FROM AUTHOR]

TheHindex, also known as Hirsch index, quantifies and compares the citation impact of scientific researchers. In the general context of networks, we define a node as a leader if itsHindex is not less than the average of theHindices of its neighbors. We show that in a randomly connected network, the proportion of leaders is almost always close to a half. [ABSTRACT FROM PUBLISHER]

How do the k-core structures of real-world graphs look like? What are the common patterns and the anomalies? How can we exploit them for applications? A k-core is the maximal subgraph in which all vertices have degree at least k. This concept has been applied to such diverse areas as hierarchical structure analysis, graph visualization, and graph clustering. Here, we explore pervasive patterns related to k-cores and emerging in graphs from diverse domains. Our discoveries are: (1) Mirror Pattern: coreness (i.e., maximum k such that each vertex belongs to the k-core) is strongly correlated with degree. (2) Core-Triangle Pattern: degeneracy (i.e., maximum k such that the k-core exists) obeys a 3-to-1 power-law with respect to the count of triangles. (3) Structured Core Pattern: degeneracy–cores are not cliques but have non-trivial structures such as core–periphery and communities. Our algorithmic contributions show the usefulness of these patterns. (1) Core-A, which measures the deviation from Mirror Pattern, successfully spots anomalies in real-world graphs, (2) Core-D, a single-pass streaming algorithm based on Core-Triangle Pattern, accurately estimates degeneracy up to 12×faster than its competitor. (3) Core-S, inspired by Structured Core Pattern, identifies influential spreaders up to 17×faster than its competitors with comparable accuracy. [ABSTRACT FROM AUTHOR]

Social network is an abstract presentation of social systems where ideas and information propagate through the interactions between individuals. It is an essential issue to find a set of most influential individuals in a social network so that they can spread influence to the largest range on the network. Traditional methods for identifying influential nodes in networks are based on greedy algorithm or specific centrality measures. Some recent researches have shown that community structure, which is a common and important topological property of social networks, has significant effect on the dynamics of networks. However, most influence maximization methods do not take into consideration the community structure in the network, which limits their applications on social networks with community structure. In this paper, we propose a new algorithm for identifying influential nodes in social networks with community structure based on label propagation. The proposed algorithm can find the core nodes of different communities in the network through the label propagation process. Moreover, our algorithm has low time complexity, which makes it applicable to large-scale networks. Extensive experiments on both synthetic and real-world networks under common diffusion models demonstrate the effectiveness and efficiency of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Background The popularity of social networks has triggered a number of research efforts on network analyses of research collaborations in the Clinical and Translational Science Award (CTSA) community. Those studies mainly focus on the general understanding of collaboration networks by measuring common network metrics. More fundamental questions about collaborations still remain unanswered such as recognizing “influential” nodes and identifying potential new collaborations that are most rewarding. Methods We analyzed biomedical research collaboration networks (RCNs) constructed from a dataset of research grants collected at a CTSA institution (i.e., University of Arkansas for Medical Sciences (UAMS)) in a comprehensive and systematic manner. First, our analysis covers the full spectrum of a RCN study: from network modeling to network characteristics measurement, from key nodes recognition to potential links (collaborations) suggestion. Second, our analysis employs non-conventional model and techniques including a weighted network model for representing collaboration strength, rank aggregation for detecting important nodes, and Random Walk with Restart (RWR) for suggesting new research collaborations. Results By applying our models and techniques to RCNs at UAMS prior to and after the CTSA, we have gained valuable insights that not only reveal the temporal evolution of the network dynamics but also assess the effectiveness of the CTSA and its impact on a research institution. We find that collaboration networks at UAMS are not scale-free but small-world. Quantitative measures have been obtained to evident that the RCNs at UAMS are moving towards favoring multidisciplinary research. Moreover, our link prediction model creates the basis of collaboration recommendations with an impressive accuracy (AUC: 0.990, MAP@3: 1.48 and MAP@5: 1.522). Last but not least, an open-source visual analytical tool for RCNs is being developed and released through Github . Conclusions Through this study, we have developed a set of techniques and tools for analyzing research collaboration networks and conducted a comprehensive case study focusing on a CTSA institution. Our findings demonstrate the promising future of these techniques and tools in understanding the generative mechanisms of research collaborations and helping identify beneficial collaborations to members in the research community. [ABSTRACT FROM AUTHOR]

In complex networks, the identification of influential nodes is very important to study the transmission and control of viruses, the location of key points of network attacks, the spread of public opinion, and the marketing promotion of markets. Therefore, based on analysis of the existing algorithms for the identification of influential nodes in complex networks, this paper proposes a new method to identify influential nodes by aggregating local structure information (ALSI). This method considers two factors: the influence of the node itself and the influence contributed by the neighbor node. The degree and K-shell value of the node are introduced when calculating the influence of the node itself, and the degree and K-shell value of the neighbor node are introduced in the calculation of the influence contributed by the neighbor node. Different calculation methods are adopted according to the comparison result of the K-shell value with the node. The greater the K-shell value and the node degree are, the more important the node is. To evaluate the performance of the algorithm, the susceptible–infected–recovered (SIR) model is used to analyze and compare the running results of 9 algorithms on 8 different networks. The experimental results show that the proposed algorithm can effectively detect the influence of nodes and outperforms many state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

The growing popularity of social networks is providing a promising opportunity for different practical applications. The influence analysis is an essential technique supporting the understanding of real-life activities. Accordingly, certain reviews and surveys have been presented, focusing on models, methods, and evaluation aspects related to social influence analysis. However, the ultimate goal is that the background social influence analysis methods developed in research could be employed in real applications. In this context, social influence analysis still remains a number of challenges including the privacy of the massive networks that have been recently mentioned by researchers. Motivated from these facts, in this paper we provide a state-of-the-art survey on the influence analysis techniques in addressing these challenges. In this detailed survey, we divide the diffusion models into two categories, individual and group node-based models. Our primary focus is to investigate the research methods and techniques and compare them according to the above categories. In the sequel, we especially further provide an overview of the existing methods for influence maximization under privacy protection. The recent advanced applications of social influence are also surveyed. In the end, open issues are discussed to enable the researchers to a better understanding of the present scenario and suggest several potential future directions for research in influence maximization. • A comprehensive literature review of pertinent studies on social influence analysis. • A detailed taxonomy of individual node and group-based influence analysis models. • Parallel influence maximization approaches and their methods are illustrated. • The main contribution of the survey is to highlight the research trends for analysis of social influence under privacy protection. • Challenges/limitations in influence analysis and their perspective research direction are discussed. [ABSTRACT FROM AUTHOR]

In data science, networks provide a useful abstraction of the structure of many complex systems, ranging from social systems and computer networks to biological networks and physical systems. Healthcare service systems are one of the main social systems that can also be understood using network-based approaches, for example, to identify and evaluate influential providers. In this paper, we propose a network-based method with privacy-preserving for identifying influential providers in large healthcare service systems. First, the provider-interacting network is constructed by employing publicly available information on locations and types of healthcare services of providers. Second, the ranking of nodes in the generated provider-interacting network is conducted in parallel on the basis of four nodal influence metrics. Third, the impact of the top-ranked influential nodes in the provider-interacting network is evaluated using three indicators. Compared with other research work based on patient-sharing networks, in this paper, the provider-interacting network of healthcare service providers can be roughly created according to the locations and the publicly available types of healthcare services, without the need for personally private electronic medical claims, thus protecting the privacy of patients. The proposed method is demonstrated by employing Physician and Other Supplier Data CY 2017, and can be applied to other similar datasets to help make decisions for the optimization of healthcare resources in the response to public health emergencies., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2020 Elsevier B.V. All rights reserved.)

Identifying the influential nodes in complex networks is still a significant topic in theoretical and practical recently. Many efficient and practical centrality indices have been proposed on the understanding of network topology features. But the indices still have more or less limitations. Hence, improving the accuracy of centrality indices is an important topic. In the paper, a fusion index named as spreading influence related centrality is proposed to identify the influence of nodes by extracting and synthesizing topology feature information of traditional centrality indices and spreading influence. The simulation experiment of spreading and node removal on four real networks are employed to verify the accuracy of proposed centrality. The experiment shows that the fusion index can provides a more reasonable ranking list than traditional methods. • An fusion model is proposed based on topology features and spreading of networks. • A spreading influence related centrality is defined according to the fusion model. • Experiments indicate that the centrality can identify influential nodes accurately. [ABSTRACT FROM AUTHOR]

One of the most important applications for identification of influential nodes in social networks is viral marketing. In viral marketing, there are valuable users from which companies or smaller businesses benefit most at the lowest cost. Inspired from the behavior of real ants and based on the ant colony optimization algorithm, we propose new methods named PMACO and IMOACO in this paper to find the most valuable users. First, the influence graph is derived from the analysis of users' interactions and communications in a social network. The negative influence among users is also considered in the process of generating the influence graph. For reduction of computational complexity and removal of unimportant nodes from the influence graph, the nodes the levels of influence of which on their neighbors are less than a specific threshold value are eliminated. Then, the representation of the search space as a weighted graph is constructed by the remaining nodes, where the weight of each edge is the similarity between the two nodes of which that edge is composed. Next, the ants begin their search processes with the goal of maximizing profit and minimizing the similarity among the selected nodes. Assessments have been made on real and synthetic datasets, and compared the proposed algorithm with well-known ones. The results of the experiments demonstrate the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Subgraph matching is an important research topic in the area of graph theory and it has been applied in many areas in nowdays. Filtering and verification are two main processes of subgraph matching algorithms. However, there exists many invalid nodes in candidate matching set after initializing the candidate set for each query node, which may result in a quantity of redundant computation during the filtering period. Regarding the problem mentioned above, in this paper, we propose a subgraph matching algorithm based on node influence, denoted as InfMatch, to improve the performance of subgraph matching on a large target graph. Specially, we find the central node of query graph by calculating the global and local influence value of each query node, after which candidate matching nodes for each query node are found from the neighborhood region of the candidate nodes for the central node. Since the central node we choose connects tightly with other nodes, isolated nodes can ′ t be added into the candidate matching set for central node and thus a number of unqualified candidate vertices are pruned. To further prune the unqualified candidate nodes, we propose several filter strategies according to the characteristics of our method. What ′ s more, considering edge limitation, we improve the matching order selection strategy. Extensive experiments demonstrate that our method is more efficient. • Propose a method based on the influence value of the vertices to determine the matching order. • Propose a extending method based on the central node for generating a sub-areas. • Propose a subgraph matching algorithm to find the candidate nodes on sub-areas instead of the whole target graph. [ABSTRACT FROM AUTHOR]