An AHP-TOPSIS Based Framework for the Selection of Node Ranking Techniques in Complex Networks

2020 ◽  
pp. 527-542
Author(s):  
Kushal Kanwar ◽  
Sakshi Kaushal ◽  
Harish Kumar
Keyword(s):  
Complex Networks ◽  
Node Ranking ◽  
Selection Of

scholarly journals Identifying Multiple Influential Spreaders in Complex Networks by Considering the Dispersion of Nodes

2022 ◽  
Vol 9 ◽  
Author(s):  
Li Tao ◽  
Mutong Liu ◽  
Zili Zhang ◽  
Liang Luo
Keyword(s):  
Complex Networks ◽  
Independent Set ◽  
Sir Model ◽  
Second Order ◽  
Propagation Process ◽  
Ranking Problem ◽  
Local Index ◽  
Practical Applications ◽  
Node Ranking ◽  
New Algorithms

Identifying multiple influential spreaders, which relates to finding k (k > 1) nodes with the most significant influence, is of great importance both in theoretical and practical applications. It is usually formulated as a node-ranking problem and addressed by sorting spreaders’ influence as measured based on the topological structure of interactions or propagation process of spreaders. However, ranking-based algorithms may not guarantee that the selected spreaders have the maximum influence, as these nodes may be adjacent, and thus play redundant roles in the propagation process. We propose three new algorithms to select multiple spreaders by taking into account the dispersion of nodes in the following ways: (1) improving a well-performed local index rank (LIR) algorithm by extending its key concept of the local index (an index measures how many of a node’s neighbors have a higher degree) from first-to second-order neighbors; (2) combining the LIR and independent set (IS) methods, which is a generalization of the coloring problem for complex networks and can ensure the selected nodes are non-adjacent if they have the same color; (3) combining the improved second-order LIR method and IS method so as to make the selected spreaders more disperse. We evaluate the proposed methods against six baseline methods on 10 synthetic networks and five real networks based on the classic susceptible-infected-recovered (SIR) model. The experimental results show that our proposed methods can identify nodes that are more influential. This suggests that taking into account the distances between nodes may aid in the identification of multiple influential spreaders.

scholarly journals MANET Performance Optimization Using Network-Based Criteria and Unmanned Aerial Vehicles

2021 ◽  
Vol 10 (1) ◽  
pp. 8
Author(s):  
Ekaterina Gromova ◽  
Sergei Kireev ◽  
Alina Lazareva ◽  
Anna Kirpichnikova ◽  
Dmitry Gromov
Keyword(s):  
Performance Optimization ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Optimal Solution ◽  
Optimal Strategies ◽  
Centrality Measures ◽  
Node Ranking ◽  
Game Theoretic ◽  
Mobile Ad Hoc ◽  
Selection Of

In this contribution we consider the problem of optimal drone positioning for improving the operation of a mobile ad hoc network. We build upon our previous results devoted to the application of game-theoretic methods for computing optimal strategies. One specific problem that arises in this context is that the optimal solution cannot be uniquely determined. In this case, one has to use some other criteria to choose the best (in some sense) of all optimal solutions. It is argued that centrality measures as well as node ranking can provide a good criterion for the selection of a unique solution. We showed that for two specific networks most criteria yielded the same solution, thus demonstrating good coherence in their predictions.

scholarly journals MULTI-HOP GENERALIZED CORE PERCOLATION ON COMPLEX NETWORKS

2020 ◽  
Vol 23 (01) ◽  
pp. 2050001 ◽  
Author(s):  
YILUN SHANG
Keyword(s):  
Phase Transition ◽  
Complex Networks ◽  
Heterogeneous Networks ◽  
Large Scale ◽  
Real Life ◽  
Continuous Phase ◽  
Global Vision ◽  
Discontinuous Phase ◽  
Analytical Frameworks ◽  
Selection Of

Recent theoretical studies on network robustness have focused primarily on attacks by random selection and global vision, but numerous real-life networks suffer from proximity-based breakdown. Here we introduce the multi-hop generalized core percolation on complex networks, where nodes with degree less than [Formula: see text] and their neighbors within [Formula: see text]-hop distance are removed progressively from the network. The resulting subgraph is referred to as [Formula: see text]-core, extending the recently proposed [Formula: see text]-core and classical core of a network. We develop analytical frameworks based upon generating function formalism and rate equation method, showing for instance continuous phase transition for [Formula: see text]-core and discontinuous phase transition for [Formula: see text]-core with any other combination of [Formula: see text] and [Formula: see text]. We test our theoretical results on synthetic homogeneous and heterogeneous networks, as well as on a selection of large-scale real-world networks. This unravels, e.g., a unique crossover phenomenon rooted in heterogeneous networks, which raises a caution that endeavor to promote network-level robustness could backfire when multi-hop tracing is involved.

scholarly journals RNA: A Reject Neighbors Algorithm for Influence Maximization in Complex Networks

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1313
Author(s):  
Dongqi Wang ◽  
Jiarui Yan ◽  
Dongming Chen ◽  
Bo Fang ◽  
Xinyu Huang
Keyword(s):  
Diffusion Process ◽  
Complex Networks ◽  
Information Diffusion ◽  
Influence Maximization ◽  
Maximization Problem ◽  
Network Centrality ◽  
Diffusion Effect ◽  
Node Ranking ◽  
Key Nodes ◽  
Influence Maximization Problem

The influence maximization problem (IMP) in complex networks is to address finding a set of key nodes that play vital roles in the information diffusion process, and when these nodes are employed as ”seed nodes”, the diffusion effect is maximized. First, this paper presents a refined network centrality measure, a refined shell (RS) index for node ranking, and then proposes an algorithm for identifying key node sets, namely the reject neighbors algorithm (RNA), which consists of two main sequential parts, i.e., node ranking and node selection. The RNA refuses to select multiple-order neighbors of the seed nodes, scatters the selected nodes from each other, and results in the maximum influence of the identified node set on the whole network. Experimental results on real-world network datasets show that the key node set identified by the RNA exhibits significant propagation capability.

Overlapping influence inspires the selection of multiple spreaders in complex networks

2018 ◽  
Vol 508 ◽  
pp. 76-83 ◽  
Author(s):  
Ming-Yang Zhou ◽  
Wen-Man Xiong ◽  
Xiang-Yang Wu ◽  
Yu-Xia Zhang ◽  
Hao Liao
Keyword(s):  
Complex Networks ◽  
Selection Of
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