Identification Of Influential Nodes Research Articles

Compact models for influential nodes identification problem in directed networks

Influential nodes identification problem (INIP) is one of the most important problems in complex networks. Existing methods mainly deal with this problem in undirected networks, while few studies focus on it in directed networks. Moreover, the methods designed for identifying influential nodes in undirected networks do not work for directed networks. Therefore, in this paper, we investigate INIP in directed networks. We first propose a novel metric to assess the influence effect of nodes in directed networks. Then, we formulate a compact model for INIP and prove it to be NP-Complete. Furthermore, we design a novel heuristic algorithm for the proposed model by integrating a 2-opt local search into a greedy framework. The experimental results show that, in most cases, the proposed methods outperform traditional measure-based heuristic methods in terms of accuracy and discrimination.

A social network based approach to identify and rank influential nodes for smart city

Purpose In online social network analysis, the problem of identification and ranking of influential nodes based on their prominence has attracted immense attention from researchers and practitioners. Identification and ranking of influential nodes is a challenging problem using Twitter, as data contains heterogeneous features such as tweets, likes, mentions and retweets. The purpose of this paper is to perform correlation between various features, evaluation metrics, approaches and results to validate selection of features as well as results. In addition, the paper uses well-known techniques to find topical authority and sentiments of influential nodes that help smart city governance and to make importance decisions while understanding the various perceptions of relevant influential nodes. Design/methodology/approach The tweets fetched using Twitter API are stored in Neo4j to generate graph-based relationships between various features of Twitter data such as followers, mentions and retweets. In this paper, consensus approach based on Twitter data using heterogeneous features has been proposed based on various features such as like, mentions and retweets to generate individual list of top-k influential nodes based on each features. Findings The heterogeneous features are meant for integrating to accomplish identification and ranking tasks with low computational complexity, i.e. O(n), which is suitable for large-scale online social network with better accuracy than baselines. Originality/value Identified influential nodes can act as source in making public decisions and their opinion give insights to urban governance bodies such as municipal corporation as well as similar organization responsible for smart urban governance and smart city development.

Influential Nodes Identification in Complex Networks via Information Entropy.

Identifying a set of influential nodes is an important topic in complex networks which plays a crucial role in many applications, such as market advertising, rumor controlling, and predicting valuable scientific publications. In regard to this, researchers have developed algorithms from simple degree methods to all kinds of sophisticated approaches. However, a more robust and practical algorithm is required for the task. In this paper, we propose the EnRenew algorithm aimed to identify a set of influential nodes via information entropy. Firstly, the information entropy of each node is calculated as initial spreading ability. Then, select the node with the largest information entropy and renovate its l-length reachable nodes’ spreading ability by an attenuation factor, repeat this process until specific number of influential nodes are selected. Compared with the best state-of-the-art benchmark methods, the performance of proposed algorithm improved by 21.1%, 7.0%, 30.0%, 5.0%, 2.5%, and 9.0% in final affected scale on CEnew, Email, Hamster, Router, Condmat, and Amazon network, respectively, under the Susceptible-Infected-Recovered (SIR) simulation model. The proposed algorithm measures the importance of nodes based on information entropy and selects a group of important nodes through dynamic update strategy. The impressive results on the SIR simulation model shed light on new method of node mining in complex networks for information spreading and epidemic prevention.

A Novel Centrality of Influential Nodes Identification in Complex Networks

Influential nodes identification in complex networks is vital for understanding and controlling the propagation process in complex networks. Some existing centrality measures ignore the impacts of neighbor node. It is well-known that degree is a famous centrality measure for influential nodes identification, and the contributions of neighbors also should be taken into consideration. Furthermore, topological connections among neighbors will affect nodes' spreading ability, that is, the denser the connections among neighbors, the greater the chance of infection. In this paper, we propose a novel centrality, called DCC, to identify influential nodes by comprehensively considering degree and clustering coefficient as well as neighbors. The weights of degree and clustering coefficient are calculated by entropy technology. To verify the feasibility and effectiveness of DCC, the comparisons between DCC and other centrality measures in four aspects are conducted based on four real networks. The experimental results demonstrate that DCC is more effective in identifying influential nodes.

Social Network Influencer Rank Recommender Using Diverse Features from Topical Graph

With the amelioration of digital technology, study of Online Social Networks (OSNs) like Twitter has gain a lot of attention from research community. Analysing OSN data enhances the understanding of relationships among people to address several issues regarding society. OSN data as a graph depicts people/user as node and their relationship as link/edge. Influence Analysis is an important area of research on OSN data. It analyses the links between users to identify influential node in the network graph. Graph database and processing models are suitable to perform such analysis. Neo4j a Graph Database facilitates to store, to portray relationships, to study the connection between users and user-friendly visualization of Twitter data. Study of Twitter metrics create opportunities for researchers and scientist for identifying topical influential node. This research aims to identify and rank topical influential nodes and propose algorithm i.e. Aggregation Consensus Rank Algorithm (ACRA) that combines various selected Twitter metrics to generate top-k influencer list. ACRA is applied on time intervals-based data for identification and ranking of influential nodes. The experimental results of proposed algorithm are evaluated against standard metrics. The results show that ACRA perform better for selected metric set as compared to other approaches.

Consensus-based aggregation for identification and ranking of top-k influential nodes

Technology has continuously been a crucially influenced and acutely tangled with the progress of society. Online Social Networks (OSN) are interesting and valuable datasets that can be leveraged to improve understanding about society and to know inter-personal choices. Identification and Ranking of Influential Nodes (IRIN) is non-trivial task for real time OSN like Twitter which accustom with ever-changing network, demographics and contents having heterogeneous features such as Tweets, Likes, Mentions and Retweets. Existing techniques such as Centrality Measures and Influence Maximization ignores vital information available on OSN, which are inappropriate for IRIN. Most of these approaches have high computational complexity i.e. $$O(n^{3} )$$ . This research aims to put forward holistic approach using Heterogeneous Surface Learning Features (HSLF) for IRIN on specific topic and proposes two approaches: Average Consensus Ranking Aggregation and Weighted Average Consensus Ranking Aggregation using HSLF. The effectiveness and efficiency of the proposed approaches are tested and analysed using real world data fetched from Twitter for two topics, Politics and Economy and achieved superior results compared to existing approaches. The empirical analysis validate that the proposed approach is highly scalable with low computational complexity and applicable for large datasets.

Identifying influential nodes based on ant colony optimization to maximize profit in social networks

Abstract 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.

Influential Node Identification in Command and Control Networks Based on Integral k-Shell

Influential nodes act as a hub for information transmission in a command and control network. The identification of influential nodes in a network of this nature is a significant and challenging task; however, it is necessary if the invulnerability of the network is to be increased. The existing k-shell method is problematic in that it features a coarse sorting granularity and does not consider the local centrality of nodes. Thus, the degree of accuracy with which the influential nodes can be identified is relatively low. This motivates us to propose a method based on an integral k-shell to identify the influential nodes in a command and control network. This new method takes both the global and local information of nodes into account, introduces the historical k-shell and a 2-order neighboring degree, and refines the k-shell decomposition process in a network. Simulation analysis is carried out from two perspectives: to determine the impact on network performance when influential nodes are removed and to obtain the correlation between the integral k-shell value and its propagation value. The simulation results show that the integral k-shell method, which employs an algorithm of lower complexity, accurately identifies the influence of those nodes with the same k-shell values. Furthermore, the method significantly improves the accuracy with which the influential nodes can be identified.

Node importance evaluation method based on multi-attribute decision-making model in wireless sensor networks

Identifying important nodes is very crucial to design efficient communication networks or contain the spreading of information such as diseases and rumors. The problem is formulated as follows: given a network, which nodes are the more important? Most current studies did not incorporate the structure change as well as application features of a network. Aiming at the node importance evaluation in wireless sensor networks, a new method which ranks nodes according to their structural importance and performance impact is proposed. Namely, this method considers two aspects of the network, network structural characteristics and application requirements. This method integrates four indicators which reflect the node importance, namely, node degree, number of spanning trees, delay, and network energy consumption. Firstly, the changes in the four indicators are analyzed using the node deletion method. Then, the TOPSIS multi-attribute decision-making method is applied to merge these four evaluation indicators. On this basis, a more comprehensive evaluation method (MADME) for node importance is obtained. Theory study reveals MADME method saves computational time. And the simulation results show the superiority of the MADME method over various algorithms such as the N-Burt method, betweenness method, DEL-Node method, and IE-Matrix method. The accuracy of the evaluation can be improved, and the key nodes determined by the MADME method have a more obvious effect on the network performance. Our method can provide guidance on influential node identification in the network.

Identifying Influential Nodes in Complex Networks Based on Local Effective Distance

With the rapid development of Internet technology, the social network has gradually become an indispensable platform for users to release information, obtain information, and share information. Users are not only receivers of information, but also publishers and disseminators of information. How to select a certain number of users to use their influence to achieve the maximum dissemination of information has become a hot topic at home and abroad. Rapid and accurate identification of influential nodes in the network is of great practical significance, such as the rapid dissemination, suppression of social network information, and the smooth operation of the network. Therefore, from the perspective of improving computational accuracy and efficiency, we propose an influential node identification method based on effective distance, named KDEC. By quantifying the effective distance between nodes and combining the position of the node in the network and its local structure, the influence of the node in the network is obtained, which is used as an indicator to evaluate the influence of the node. Through experimental analysis of a lot of real-world networks, the results show that the method can quickly and accurately identify the influential nodes in the network, and is better than some classical algorithms and some recently proposed algorithms.

Influential Nodes Identification Based on Activity Behaviors and Network Structure With Personality Analysis in Egocentric Online Social Networks

Online social network (OSN) platforms have become a place where the users share their details through various activities. Nowadays, OSNs have major impact not only on the users' lives but also on business and society. Organizations use OSN platforms to reach the target audiences and influential nodes may perform a major role to reach the target audience effectively and efficiently. In this article, a model has been proposed to identify influential nodes for a user in egocentric online social networks by considering the activity behaviors of the user and its network members along with network structure of the user. Finally, the personalities of both the user and the influencers has been analyzed using sentiment analysis and hashtag terms analysis. This article has derived a novel and efficient influence measurement model to evaluate an influence factor of each influential interacted node in the user's network for any of the classified social fields with sentiment types. To achieve that, the influence measurement process has been divided into three different categories namely behavioral influence, structural influence, and collaborative influence which is derived from the first two. Finally, the model for personality analysis has been incorporated.

RANKING NODES IN COMPLEX NETWORKS: A CASE STUDY OF THE GAUBUS

Abstract. Connecting points of interest through a well-planned, inter-connected network provides manifold benefits to commuters and service providers. In the South African context, traffic congestion has become of great concern. Given how the South Africa community is slowly developing towards the use of multi-modes of mobility, the Gautrain network can be used to promote the use of multi-modes of mobility, as the Gautrain has been identified as the backbone of mobility within the Gauteng province. Currently commuters have the option to board the Gaubus (a form of Bus Rapid Transit) at their origin points which will take them to the Gautrain station to board the Gautrain. The problem to be solved arises when a commuter wishes to traverse from any bus stop to the Gautrain station, currently he/she only has one option and if the bus network has a shutdown at any point in the network the commuter’s journey will not be possible. In solving this problem, we consider the problem of graph robustness (that is creating new alternative routes to increase node/bus stop connectivity). We initial use Strava data, to identify locations were cyclist prefer to cycle and at what time of day. In graph theory, the nodes with most spreading ability are called influential nodes. Identification of most influential nodes and ranking them based on their spreading ability is of vital importance. Closeness centrality and betweenness are one of the most commonly used methods to identify influential nodes in complex networks. Using the Gaubus network we identify the influential nodes/ bus stops, using the betweenness centrality measure. The results reveal the influential nodes with the highest connectivity as these have cross-connections in the network. Identification of the influential nodes presents an important implication for future planning, accessibility, and, more generally, quality of life.

Community detection and influential node identification in complex networks using mathematical programming

Integer programming models for community detection in relational networks have diverse applications in different fields. From making our lives easier by improving search engine optimization to saving our lives by aiding in threat detection and disaster management, researches in this niche have added value to human experience and knowledge. Besides the community structure, the influential nodes or members in a complex network are highly effective at diffusing information quickly to others in the community. Prior research dealing with the use of optimization models for clustering networks has independently focused on detecting communities. In this research, we propose a new integer linear programming model to detect community structure in real-life networks and also identify the most influential node within each community. We validate the proposed model by testing it on a well-established community network. Further, the performance of the proposed model is evaluated by comparing it with the existing best performing optimization model as well as three heuristic approaches for community detection. The experimental results indicate that in most cases the proposed integer programming model performs better than the existing optimization model with respect to modularity, Silhouette coefficient and computational time. Besides, our model yields superior Silhouette and competitive modularity values compared to the heuristic approaches in many cases.

General link prediction with influential node identification

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.

Multimodal ensemble approach to identify and rank top-k influential nodes of scholarly literature using Twitter network

Scholarly literature is an immense network of activities, linked via collaborations or information propagation. Analysing such network can be leveraged by harnessing rich semantic meaning of scholarly graph. Identifying and ranking top- k influential nodes from various domains of scholarly literature using social media data are still infancy. Social networking sites like Twitter provide an opportunity to create inventive graph-based measures to identify and rank influential nodes such as scholars, articles, journal, information spreading media and academic institutions of scholarly literature. Many network-based models such as centrality measures have been proposed to identify influential nodes. The empirical annotation shows that centrality measures for finding influential nodes are high in computational complexity. In addition, notion of these measures have high variance, which signifies an influential node deviation with change in application and nature of information flows in the network. The research aims to propose an ensemble learning approach based on multimodal majority voting influence (MMMVI) to identify and weighted multimodal ensemble average influence (WMMEAI) to rank top- k influential nodes in Twitter network data set of well-known three influential nodes, that is, academic institution, scholar and journal. The empirical analysis has been accomplished to learn practicability and efficiency of the proposed approaches when compared with state-of-the-art approaches. The experimental result shows that the ensemble approach using surface learning models (SLMs) can lead to better identification and ranking of influential nodes with low computational complexity.

Identifying Influential Nodes in Complex Networks Based on Local Neighbor Contribution

The identification of influential nodes in complex networks has been widely used to suppress rumor dissemination and control the spread of epidemics and diseases. However, achieving high accuracy and comprehensiveness in node influence ranking is time-consuming, and there are issues in using different measures on the same subject. The identification of influential nodes is very important for the maintenance of the entire network because they determine the stability and integrity of the entire network, which has strong practical application value in real life. Accordingly, a method based on local neighbor contribution (LNC) is proposed. LNC combines the influence of the nodes themselves with the contribution of the nearest and the next nearest neighbor nodes, thus further quantifying node influence in complex networks. LNC is applicable to networks of various scales, and its time complexity is considerably low. We evaluate the performance of LNC through extensive simulation experiments on seven real-world networks and two synthetic networks. We employ the SIR model to examine the spreading efficiency of each node and compare LNC with degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, PageRank, Hyperlink-Induced Topic Search(HITS), ProfitLeader, Gravity and Weighted Formal Concept Analysis(WFCA). It is demonstrated that LNC ranks nodes effectively and outperforms several state-of-the-art algorithms.

Evidential identification of influential nodes in network of networks

Network theory has been widely used to describe complex systems in the real world. However, following the research of networks science, a network with one relationship in it can not be used in a multi-relationship complex system. Therefore, new network models were proposed to describe different relationships between real systems, such as Multilayers Networks, Multiplex Networks, Interconnected Networks and Network of Networks (NON). One type of NON has the same nodes but different relationship between nodes in different layers. This work focuses on this type of NON.In this paper, a new method is proposed to identify the influential nodes in the NON mentioned above based on evidence theory. Depending on the new method, influence of each node in different layer is fused by combination rules of evidence theory. Thus, results of fusion are used to quantify the influence of each node in NON to identify the influential nodes. Here we use the China transport networks, which can be regarded as a NON, to test the new method. The results of experiment show that the new method has smaller information loss during calculation and it is a reasonable method to identify influential nodes in a NON.

Ranking nodes in complex networks based on local structure and improving closeness centrality

In complex networks, the nodes with most spreading ability are called influential nodes. In many applications such as viral marketing, identification of most influential nodes and ranking them based on their spreading ability is of vital importance. Closeness centrality is one of the most commonly used methods to identify influential spreaders in social networks. However, this method is time-consuming for dynamic large-scale networks and has high computational complexity. In this paper, we propose a novel ranking algorithm which improves closeness centrality by taking advantage of local structure of nodes and aims to decrease the computational complexity. In our proposed method, at first, a community detection algorithm is applied to extract community structures of the network. Thereafter, after ignoring the relationship between communities, one best node as local critical node for each community is extracted according to any centrality measure. Then, with the consideration of interconnection links between communities, another best node as gateway node is found. Finally, the nodes are sorted and ranked based on computing the sum of the shortest path length of nodes to obtained critical nodes. Our method can detect the most spreader nodes with high diffusion ability and low time complexity, which make it appropriately applicable to large-scale networks. Experiments on synthetic and real-world connected networks under common diffusion models demonstrate the effectiveness of our proposed method in comparison with other methods.

Identification of top-k influential nodes based on enhanced discrete particle swarm optimization for influence maximization

Influence maximization aims to select a subset of top-k influential nodes to maximize the influence propagation, and it remains an open research topic of viral marketing and social network analysis. Submodularity-based methods including greedy algorithm can provide solutions with performance guarantee, but the time complexity is unbearable especially in large-scale networks. Meanwhile, conventional centrality-based measures cannot provide steady performance for multiple influential nodes identification. In this paper, we propose an improved discrete particle swarm optimization with an enhanced network topology-based strategy for influence maximization. According to the strategy, the k influential nodes in a temporary optimal seed set are recombined firstly in ascending order by degree metric to let the nodes with lower degree centrality exploit more influential neighbors preferentially. Secondly, a local greedy strategy is applied to replace the current node with the most influential node from the direct neighbor set of each node from the temporary seed set. The experimental results conducted in six social networks under independent cascade model show that the proposed algorithm outperforms typical centrality-based heuristics, and achieves comparable results to greedy algorithm but with less time complexity.

Maximizing the spread of influence via the collective intelligence of discrete bat algorithm

Influence maximization aims to select a small set of k influential nodes to maximize the spread of influence. It is still an open research topic to develop effective and efficient algorithms for the optimization problem. Greedy-based algorithms utilize the property of “submodularity” to provide performance guarantee, but the computational cost is unbearable especially in large-scale networks. Meanwhile, conventional topology-based centrality methods always fail to provide satisfying identification of influential nodes. To identify the k influential nodes effectively, we propose a metaheuristic discrete bat algorithm (DBA) based on the collective intelligence of bat population in this paper. According to the evolutionary rules of the original bat algorithm (BA), a probabilistic greedy-based local search strategy based on network topology is presented and a CandidatesPool is generated according to the contribution of each node to the network topology to enhance the exploitation operation of DBA. The experimental results and statistic tests on five real-world social networks and a synthetic network under independent cascade model demonstrate that DBA outperforms other two metaheuristics and the Stop-and-Stair algorithm, and achieves competitive influence spread to CELF (Cost-Effective Lazy Forward) but has less time computation than CELF.