Identification of influential nodes in complex networks: A local degree dimension approach

Zhong, Shen; Zhang, Haotian; Deng, Yong

doi:10.1016/j.ins.2022.07.172

Cited by 51 publications

(13 citation statements)

References 42 publications

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“…The basic statistical information of the four real datasets used in this study is presented in Table 2 . It can be observed that the real networks differ significantly in terms of node size, average degree, clustering coefficient, and average distance of the shortest path, and these parameters have a certain impact on the information spread [ 12 , 13 , 16 , 17 , 18 ]. The selection of networks with different parameters ensures the generalizability of the proposed SpreadRank method.…”

Section: Resultsmentioning

confidence: 99%

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SpreadRank: A Novel Approach for Identifying Influential Spreaders in Complex Networks

Zhu

Huang

2023

Entropy

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Identifying influential spreaders in complex networks is critical for information spread and malware diffusion suppression. In this paper, we propose a novel influential spreader identification method, called SpreadRank, which considers the path reachability in information spreading and uses its quantitative index as a measure of node spread centrality to obtain the spread influence of a single node. To avoid the overlapping of the influence range of the node spread, this method establishes a dynamic influential node set selection mechanism based on the spread centrality value and the principle of minimizing the maximum connected branch after network segmentation, and it selects a group of nodes with the greatest overall spread influence. Experiments based on the SIR model demonstrate that, compared to other existing methods, the selected influential spreaders of SpreadRank can quickly diffuse or suppress information more effectively.

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Section: Resultsmentioning

confidence: 99%

“…In recent years, many centrality methods have also been developed that combine multiple factors. These methods also consider network topology information, such as node degree, clustering coefficient, and node neighbors [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. Keyou et al.…”

Section: Introductionmentioning

confidence: 99%

SpreadRank: A Novel Approach for Identifying Influential Spreaders in Complex Networks

Zhu

Huang

2023

Entropy

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“…When nodes carrying messages forward messages, those most interested in the same region are selected for forwarding messages [36]. Some symbols are defined as follows: G = (M, E), where M represents the set of points and E represents the set of edges; M(v) denotes the set of neighbors of node v and d v represents the degree of node v. Node degree refers to the number of edges associated with a node, represented by d. The larger the value of d, the more nodes are connected to the current node, and the larger the degree of a node, the more important the node is within the network [37]. There is a high probability that such a node is in a key position and the message is forwarded frequently.…”

Section: System Modelmentioning

confidence: 99%

Multi-Copy Relay Node Selection Strategy Based on Reinforcement Learning

Yang

Zhang

2023

Sensors

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Delay tolerant networks (DTNs), are characterized by their difficulty in establishing end-to-end paths and and large message propagation delays. To control network overhead costs, reduce message delays, and improve delivery rates in DTNs, it is essential to not only delete messages that have reached their destination but also to more precisely determine appropriate relay nodes. Based on the above goals, this paper constructs a multi-copy relay node selection router algorithm based on Q-lambda reinforcement learning with reference to the idea of community division (QLCR). In community division, if a node has the highestdegree, it is considered the core node, and nodes with similar interests and structural properties are divided into a community. Node degree refers to the number of nodes associated with the node, indicating its importance in the network. Structural similarity determines the distance between nodes. The selection of relay nodes considers node degree, interests, and structural similarity. The Q-lambda reinforcement learning algorithm enables each node to learn from the entire network, setting corresponding reward values based on encountered nodes meeting the specified conditions. Through iterative processes, the node with the most cumulative reward value is chosen as the final relay node. Experimental results demonstrate that the proposed algorithm achieves a high delivery rate while maintaining low network overhead and delay.

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“…Extensive research has been conducted on robustness of single-layer networks, including the identification of key nodes [6][7][8][9][10][11][12] and edges. [13][14][15] However, with the increasing interconnectivity of networks, single-layer models are no longer sufficient to meet practical needs.…”

Section: Introductionmentioning

confidence: 99%

Assessing edge-coupled interdependent network disintegration via rank aggregation and elite enumeration

Liu

Bai

2023

Chinese Phys. B

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The disintegration of networks is a widely researched topic with significant applications in fields such as counter-terrorism and infectious disease control. While the traditional approaches to achieving network disintegration involve identifying critical sets of nodes or edges, limited research has been done on edge-based disintegration strategies. In this paper, we propose a novel algorithm, the Rank Aggregation Elite Enumeration Algorithm based on Edge-Coupled Networks (RAEEC), which aims to implement tiling for edge-coupled networks by finding important sets of edges in the network while balancing effectiveness and efficiency. Our algorithm is based on a two-layer edge-coupled network model with one-to-one links, and utilizes three advanced edge importance metrics to rank the edges separately. A comprehensive ranking of edges is obtained using a rank aggregation approach proposed in this paper. The top few edges from the ranking set obtained by RAEEC are then used to generate an enumeration set, which is continuously iteratively updated to identify the set of elite attack edges. We conducted extensive experiments on synthetic networks to evaluate the performance of our proposed method, and the results indicate that RAEEC achieves a satisfactory balance between efficiency and effectiveness. Our approach represents a significant contribution to the field of network disintegration, particularly for edge-based strategies.

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Identification of influential nodes in complex networks: A local degree dimension approach

Cited by 51 publications

References 42 publications

SpreadRank: A Novel Approach for Identifying Influential Spreaders in Complex Networks

SpreadRank: A Novel Approach for Identifying Influential Spreaders in Complex Networks

Multi-Copy Relay Node Selection Strategy Based on Reinforcement Learning

Assessing edge-coupled interdependent network disintegration via rank aggregation and elite enumeration

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