A novel method for identifying influential nodes in complex networks based on multiple attributes

2018 ◽  
Vol 32 (28) ◽  
pp. 1850307 ◽  
Author(s):  
Dong Liu ◽  
Hao Nie ◽  
Baowen Zhang
Keyword(s):  
Large Scale ◽  
Real Life ◽  
Entropy Method ◽  
Viral Marketing ◽  
Clustering Coefficient ◽  
Node Location ◽  
Influential Nodes ◽  
Multiple Attributes ◽  
Comparison Algorithms ◽  
Large Scale Networks

Identifying influential nodes is a crucial issue in epidemic spreading, controlling the propagation process of information and viral marketing. Thus, algorithms for exploring vital nodes have aroused more and more concern among researchers. Recently, scholars have proposed various types of algorithms based on different perspectives. However, each of these methods has their own strengths and weaknesses. In this work, we introduce a novel multiple attributes centrality for identifying significant nodes based on the node location and neighbor information attributes. We call our proposed method the MAC. Specifically, we utilize the information of the number of iterations per node to enhance the accuracy of the K-shell algorithm, so that the location attribute can be used to distinguish the important nodes more deeply. And the neighbor information attribute we selected can effectively avoid the overlapping problem of neighbor information propagation caused by large clustering coefficient of networks. Because these two indexes have different emphases, we use entropy method to assign them reasonable weights. In addition, MAC has low time complexity O(n), which makes the algorithm suitable for large-scale networks. In order to objectively assess its performance, we utilize the Susceptible-Infected-Recovered (SIR) model to verify the propagation capability of each node and compare the MAC method with several classic methods in six real-life datasets. Extensive experiments verify the superiority of our algorithm to other comparison algorithms.

scholarly journals Novel Node Centrality-Based Efficient Empirical Robustness Assessment for Directed Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xiaolong Deng ◽  
Hao Ding ◽  
Yong Chen ◽  
Cai Chen ◽  
Tiejun Lv
Keyword(s):  
Social Network ◽  
Vulnerability Assessment ◽  
Online Social Networks ◽  
Information Transfer ◽  
Large Scale ◽  
Real Life ◽  
Clustering Coefficient ◽  
Network Robustness ◽  
Directed Network ◽  
Online Social Media

In recent years, while extensive researches on various networks properties have been proposed and accomplished, little has been proposed and done on network robustness and node vulnerability assessment under cascades in directed large-scale online community networks. In essential, an online directed social network is a group-centered and information spread-dominated online platform which is very different from the traditional undirected social network. Some further research studies have indicated that the online social network has high robustness to random removals of nodes but fails to the intentional attacks, particularly to those attacks based on node betweenness or node directed coefficient. To explore on the robustness of directed social network, in this article, we have proposed two novel node centralities of ITG (information transfer gain-based probability clustering coefficient) and I M p v (directed path-based node importance centrality). These two new centrality models are designed to capture this cascading effect in directed online social networks. Furthermore, we also propose a new and highly efficient computing method based on iterations for I M p v . Then, with the abundant experiments on the synthetic signed network and real-life networks derived from directed online social media and directed human mobile phone calling network, it has been proved that our ITG and I M p v based on directed social network robustness and node vulnerability assessment method is more accurate, efficient, and faster than several traditional centrality methods such as degree and betweenness. And we also have proposed the solid reasoning and proof process of iteration times k in computation of I M p v . To the best knowledge of us, our research has drawn some new light on the leading edge of robustness on the directed social network.

Scalable Influence Maximization Meets Efficiency and Effectiveness in Large-Scale Social Networks

2020 ◽  
Vol 30 (08) ◽  
pp. 1079-1096
Author(s):  
Liqing Qiu ◽  
Shuang Zhang ◽  
Chunmei Gu ◽  
Xiangbo Tian
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Influence Maximization ◽  
Scalable Algorithm ◽  
Influence Spread ◽  
Efficiency And Effectiveness ◽  
Spread Of Influence ◽  
Influential Nodes ◽  
Comparison Algorithms

Influence maximization is a problem that aims to select top [Formula: see text] influential nodes to maximize the spread of influence in social networks. The classical greedy-based algorithms and their improvements are relatively slow or not scalable. The efficiency of heuristic algorithms is fast but their accuracy is unacceptable. Some algorithms improve the accuracy and efficiency by consuming a large amount of memory usage. To overcome the above shortcoming, this paper proposes a fast and scalable algorithm for influence maximization, called K-paths, which utilizes the influence tree to estimate the influence spread. Additionally, extensive experiments demonstrate that the K-paths algorithm outperforms the comparison algorithms in terms of efficiency while keeping competitive accuracy.

Characteristics of disrupted topological organization in white matter functional connectome in schizophrenia

2020 ◽  
pp. 1-11
Author(s):  
Yuchao Jiang ◽  
Dezhong Yao ◽  
Jingyu Zhou ◽  
Yue Tan ◽  
Huan Huang ◽  
...  
Keyword(s):  
White Matter ◽  
Large Scale ◽  
Negative Symptoms ◽  
Functional Organization ◽  
Clustering Coefficient ◽  
Theoretical Approaches ◽  
Topological Organization ◽  
Large Scale Networks ◽  
Nodal Level

Abstract Background Neuroimaging characteristics have demonstrated disrupted functional organization in schizophrenia (SZ), involving large-scale networks within grey matter (GM). However, previous studies have ignored the role of white matter (WM) in supporting brain function. Methods Using resting-state functional MRI and graph theoretical approaches, we investigated global topological disruptions of large-scale WM and GM networks in 93 SZ patients and 122 controls. Six global properties [clustering coefficient (Cp), shortest path length (Lp), local efficiency (Eloc), small-worldness (σ), hierarchy (β) and synchronization (S) and three nodal metrics [nodal degree (Knodal), nodal efficiency (Enodal) and nodal betweenness (Bnodal)] were utilized to quantify the topological organization in both WM and GM networks. Results At the network level, both WM and GM networks exhibited reductions in Eloc, Cp and S in SZ. The SZ group showed reduced σ and β only for the WM network. Furthermore, the Cp, Eloc and S of the WM network were negatively correlated with negative symptoms in SZ. At the nodal level, the SZ showed nodal disturbances in the corpus callosum, optic radiation, posterior corona radiata and tempo-occipital WM tracts. For GM, the SZ manifested increased nodal centralities in frontoparietal regions and decreased nodal centralities in temporal regions. Conclusions These findings provide the first evidence for abnormal global topological properties in SZ from the perspective of a substantial whole brain, including GM and WM. Nodal centralities enhance GM areas, along with a reduction in adjacent WM, suggest that WM functional alterations may be compensated for adjacent GM impairments in SZ.

GPN: A novel gravity model based on position and neighborhood to identify influential nodes in complex networks

2021 ◽  
pp. 2150183
Author(s):  
Dengqin Tu ◽  
Guiqiong Xu ◽  
Lei Meng
Keyword(s):  
Gravity Model ◽  
Large Scale ◽  
Linear Time ◽  
Simulation Experiments ◽  
Research Issues ◽  
Model Based ◽  
Topological Features ◽  
Influential Nodes ◽  
Large Scale Networks ◽  
Iteration Index

The identification of influential nodes is one of the most significant and challenging research issues in network science. Many centrality indices have been established starting from topological features of networks. In this work, we propose a novel gravity model based on position and neighborhood (GPN), in which the mass of focal and neighbor nodes is redefined by the extended outspreading capability and modified k-shell iteration index, respectively. This new model comprehensively considers the position, local and path information of nodes to identify influential nodes. To test the effectiveness of GPN, a number of simulation experiments on nine real networks have been conducted with the aid of the susceptible–infected–recovered (SIR) model. The results indicate that GPN has better performance than seven popular methods. Furthermore, the proposed method has near linear time cost and thus it is suitable for large-scale networks.

scholarly journals Algorithms for generating large-scale clustered random graphs

2014 ◽  
Vol 2 (3) ◽  
pp. 403-415 ◽  
Author(s):  
CHENG WANG ◽  
OMAR LIZARDO ◽  
DAVID HACHEN
Keyword(s):  
Random Graph ◽  
Random Graphs ◽  
Real World ◽  
Local Structure ◽  
Topological Structure ◽  
Large Scale ◽  
Random Processes ◽  
Clustering Coefficient ◽  
Computation Efficiency ◽  
Large Scale Networks

AbstractReal-world networks are often compared to random graphs to assess whether their topological structure could be a result of random processes. However, a simple random graph in large scale often lacks social structure beyond the dyadic level. As a result we need to generate clustered random graph to compare the local structure at higher network levels. In this paper a generalized version of Gleeson's algorithm G(VS, VT, ES, ET, S, T) is advanced to generate a clustered random graph in large-scale which persists the number of vertices |V|, the number of edges |E|, and the global clustering coefficient CΔ as in the real network and it works successfully for nine large-scale networks. Our new algorithm also has advantages in randomness evaluation and computation efficiency when compared with the existing algorithms.

scholarly journals Ranking Influential Nodes in Complex Networks with Information Entropy Method

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Nan Zhao ◽  
Jingjing Bao ◽  
Nan Chen
Keyword(s):  
Information Entropy ◽  
Information Dissemination ◽  
Sir Model ◽  
Entropy Method ◽  
Local Information ◽  
Viral Marketing ◽  
Algorithm Performance ◽  
Position Index ◽  
Evaluation Measure ◽  
Influential Nodes

The ranking of influential nodes in networks is of great significance. Influential nodes play an enormous role during the evolution process of information dissemination, viral marketing, and public opinion control. The sorting method of multiple attributes is an effective way to identify the influential nodes. However, these methods offer a limited improvement in algorithm performance because diversity between different attributes is not properly considered. On the basis of the k-shell method, we propose an improved multiattribute k-shell method by using the iterative information in the decomposition process. Our work combines sigmod function and iteration information to obtain the position index. The position attribute is obtained by combining the shell value and the location index. The local information of the node is adopted to obtain the neighbor property. Finally, the position attribute and neighbor attribute are weighted by the method of information entropy weighting. The experimental simulations in six real networks combined with the SIR model and other evaluation measure fully verify the correctness and effectiveness of the proposed method.

scholarly journals Approximation Algorithm for Shortest Path in Large Social Networks

Algorithms ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 36 ◽  
Author(s):  
Dennis Nii Ayeh Mensah ◽  
Hui Gao ◽  
Liang Wei Yang
Keyword(s):  
Approximation Algorithm ◽  
Large Scale ◽  
High Efficiency ◽  
Shortest Paths ◽  
Real Life ◽  
Original Network ◽  
Hierarchical Networks ◽  
Life Data ◽  
Real Life Data ◽  
Large Scale Networks

Proposed algorithms for calculating the shortest paths such as Dijikstra and Flowd-Warshall’s algorithms are limited to small networks due to computational complexity and cost. We propose an efficient and a more accurate approximation algorithm that is applicable to large scale networks. Our algorithm iteratively constructs levels of hierarchical networks by a node condensing procedure to construct hierarchical graphs until threshold. The shortest paths between nodes in the original network are approximated by considering their corresponding shortest paths in the highest hierarchy. Experiments on real life data show that our algorithm records high efficiency and accuracy compared with other algorithms.

scholarly journals Multi-Objective Evolutionary Algorithms to Find Community Structures in Large Networks

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2048 ◽  
Author(s):  
Manuel Guerrero ◽  
Consolación Gil ◽  
Francisco G. Montoya ◽  
Alfredo Alcayde ◽  
Raúl Baños
Keyword(s):  
Evolutionary Algorithms ◽  
Power Systems ◽  
Community Detection ◽  
Large Scale ◽  
Real Life ◽  
Resolution Limit ◽  
Detection Problem ◽  
Multi Objective ◽  
Large Scale Networks ◽  
Structural Metrics

Real-world complex systems are often modeled by networks such that the elements are represented by vertices and their interactions are represented by edges. An important characteristic of these networks is that they contain clusters of vertices densely linked amongst themselves and more sparsely connected to nodes outside the cluster. Community detection in networks has become an emerging area of investigation in recent years, but most papers aim to solve single-objective formulations, often focused on optimizing structural metrics, including the modularity measure. However, several studies have highlighted that considering modularityas a unique objective often involves resolution limit and imbalance inconveniences. This paper opens a new avenue of research in the study of multi-objective variants of the classical community detection problem by applying multi-objective evolutionary algorithms that simultaneously optimize different objectives. In particular, they analyzed two multi-objective variants involving not only modularity but also the conductance metric and the imbalance in the number of nodes of the communities. With this aim, a new Pareto-based multi-objective evolutionary algorithm is presented that includes advanced initialization strategies and search operators. The results obtained when solving large-scale networks representing real-life power systems show the good performance of these methods and demonstrate that it is possible to obtain a balanced number of nodes in the clusters formed while also having high modularity and conductance values.

scholarly journals Spatio-Temporal and Power–Energy Scheduling of Mobile Battery Storage for Mitigating Wind and Solar Energy Curtailment in Distribution Networks

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4853
Author(s):  
Hedayat Saboori ◽  
Shahram Jadid ◽  
Mehdi Savaghebi
Keyword(s):  
Large Scale ◽  
Reactive Power ◽  
Real Life ◽  
Temporal Distribution ◽  
Distribution Networks ◽  
Test System ◽  
Energy Status ◽  
Spatio Temporal ◽  
Large Scale Networks ◽  
Battery Energy

Several technical, computational, and economic barriers have caused curtailing a share of renewable-based power generation, especially in systems with higher penetration levels. The Mobile Battery Energy Storage (MBES) can cope with this problem considering the spatial and temporal distribution of the curtailed energy. Accordingly, a new operation model is proposed for optimal scheduling of the MBES in a distribution network with wind and photovoltaic (PV) resources. The network experiences curtailment situations because of bus overvoltage, feeder overload, and power over-generation. The MBES is a truck-mounted battery system compacted in a container. The proposed model seeks to determine the optimal spatio-temporal and power–energy status of the MBES to achieve a minimum curtailment ratio. The model considers transportation time and cost of the MBES efficiently while both active and reactive power exchanges are modeled. The model is linear, without convergence and optimality problems, applicable to real-life large-scale networks, and can be easily integrated into the commercial distribution management software. The implementation results on a test system demonstrate its functionality to recover a considerable share of the curtailed energy for both wind and PV resources at all curtailment patterns and scenarios.

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