Dynamic Community Evolution Analysis Framework for Large-scale Complex Networks Based on Strong and Weak Events

2020 ◽  
pp. 1-15
Author(s):  
Shaojie Qiao ◽  
Nan Han ◽  
Yunjun Gao ◽  
Rong-Hua Li ◽  
Jianbin Huang ◽  
...  
Keyword(s):  
Complex Networks ◽  
Large Scale ◽  
Analysis Framework ◽  
Evolution Analysis ◽  
Community Evolution ◽  
Dynamic Community

Crowd attraction-driven community evolution on social network

2021 ◽  
pp. 2250009
Author(s):  
Fuzhong Nian ◽  
Li Luo ◽  
Xuelong Yu
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Social Network ◽  
Large Scale ◽  
Detection Algorithm ◽  
Information Propagation ◽  
Evolution Analysis ◽  
Dynamic Social Networks ◽  
Community Evolution ◽  
Community Detection Algorithm

The evolution analysis of community structure of social network will help us understand the composition of social organizations and the evolution of society better. In order to discover the community structure and the regularity of community evolution in large-scale social networks, this paper analyzes the formation process and influencing factors of communities, and proposes a community evolution analysis method of crowd attraction driven. This method uses the traditional community division method to divide the basic community, and introduces the theory of information propagation into complex network to simulate the information propagation of dynamic social networks. Then defines seed node, the activity of basic community and crowd attraction to research the influence of groups on individuals in social networks. Finally, making basic communities as fixed groups in the network and proposing community detection algorithm based on crowd attraction. Experimental results show that the scheme can effectively detect and identify the community structure in large-scale social networks.

scholarly journals Network isolators inhibit failure spreading in complex networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Franz Kaiser ◽  
Vito Latora ◽  
Dirk Witthaut
Keyword(s):  
Complex Networks ◽  
Large Scale ◽  
Power Transmission ◽  
Power Grids ◽  
Cascading Failure ◽  
Critical Infrastructures ◽  
Daily Lives ◽  
Supply Networks ◽  
Water Transmission ◽  
Complete Collapse

AbstractIn our daily lives, we rely on the proper functioning of supply networks, from power grids to water transmission systems. A single failure in these critical infrastructures can lead to a complete collapse through a cascading failure mechanism. Counteracting strategies are thus heavily sought after. In this article, we introduce a general framework to analyse the spreading of failures in complex networks and demostrate that not only decreasing but also increasing the connectivity of the network can be an effective method to contain damages. We rigorously prove the existence of certain subgraphs, called network isolators, that can completely inhibit any failure spreading, and we show how to create such isolators in synthetic and real-world networks. The addition of selected links can thus prevent large scale outages as demonstrated for power transmission grids.

A new spectral coarse-graining algorithm based on K-means clustering in complex networks

2019 ◽  
Vol 33 (01) ◽  
pp. 1850421 ◽  
Author(s):  
Lang Zeng ◽  
Zhen Jia ◽  
Yingying Wang
Keyword(s):  
Complex Networks ◽  
Large Scale ◽  
Dynamic Properties ◽  
Coarse Graining ◽  
Kuramoto Model ◽  
Coarse Grained ◽  
Original Network ◽  
Topological Information ◽  
Real World Applications ◽  
Large Scale Networks

Coarse-graining of complex networks is one of the important algorithms to study large-scale networks, which is committed to reducing the size of networks while preserving some topological information or dynamic properties of the original networks. Spectral coarse-graining (SCG) is one of the typical coarse-graining algorithms, which can keep the synchronization ability of the original network well. However, the calculation of SCG is large, which limits its real-world applications. And it is difficult to accurately control the scale of the coarse-grained network. In this paper, a new SCG algorithm based on K-means clustering (KCSCG) is proposed, which cannot only reduce the amount of calculation, but also accurately control the size of coarse-grained network. At the same time, KCSCG algorithm has better effect in keeping the network synchronization ability than SCG algorithm. A large number of numerical simulations and Kuramoto-model example on several typical networks verify the feasibility and effectiveness of the proposed algorithm.

scholarly journals Identifying Node Importance in a Complex Network Based on Node Bridging Feature

2018 ◽  
Vol 8 (10) ◽  
pp. 1914 ◽  
Author(s):  
Lincheng Jiang ◽  
Yumei Jing ◽  
Shengze Hu ◽  
Bin Ge ◽  
Weidong Xiao
Keyword(s):  
Complex Networks ◽  
Large Scale ◽  
Shortest Paths ◽  
Evaluation Criteria ◽  
Local Network ◽  
Network Efficiency ◽  
Network Attack ◽  
Node Importance ◽  
Pass Through ◽  
Large Scale Networks

Identifying node importance in complex networks is of great significance to improve the network damage resistance and robustness. In the era of big data, the size of the network is huge and the network structure tends to change dynamically over time. Due to the high complexity, the algorithm based on the global information of the network is not suitable for the analysis of large-scale networks. Taking into account the bridging feature of nodes in the local network, this paper proposes a simple and efficient ranking algorithm to identify node importance in complex networks. In the algorithm, if there are more numbers of node pairs whose shortest paths pass through the target node and there are less numbers of shortest paths in its neighborhood, the bridging function of the node between its neighborhood nodes is more obvious, and its ranking score is also higher. The algorithm takes only local information of the target nodes, thereby greatly improving the efficiency of the algorithm. Experiments performed on real and synthetic networks show that the proposed algorithm is more effective than benchmark algorithms on the evaluation criteria of the maximum connectivity coefficient and the decline rate of network efficiency, no matter in the static or dynamic attack manner. Especially in the initial stage of attack, the advantage is more obvious, which makes the proposed algorithm applicable in the background of limited network attack cost.

scholarly journals An Efficient Partition-Based Approach to Identify and Scatter Multiple Relevant Spreaders in Complex Networks

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1216
Author(s):  
Jedidiah Yanez-Sierra ◽  
Arturo Diaz-Perez ◽  
Victor Sosa-Sosa
Keyword(s):  
Complex Networks ◽  
Information Diffusion ◽  
Large Scale ◽  
Centrality Measures ◽  
Correct Identification ◽  
Graph Analysis ◽  
Relevant Role ◽  
Two Phases ◽  
Large Scale Networks ◽  
Spreading Processes

One of the main problems in graph analysis is the correct identification of relevant nodes for spreading processes. Spreaders are crucial for accelerating/hindering information diffusion, increasing product exposure, controlling diseases, rumors, and more. Correct identification of spreaders in graph analysis is a relevant task to optimally use the network structure and ensure a more efficient flow of information. Additionally, network topology has proven to play a relevant role in the spreading processes. In this sense, more of the existing methods based on local, global, or hybrid centrality measures only select relevant nodes based on their ranking values, but they do not intentionally focus on their distribution on the graph. In this paper, we propose a simple yet effective method that takes advantage of the underlying graph topology to guarantee that the selected nodes are not only relevant but also well-scattered. Our proposal also suggests how to define the number of spreaders to select. The approach is composed of two phases: first, graph partitioning; and second, identification and distribution of relevant nodes. We have tested our approach by applying the SIR spreading model over nine real complex networks. The experimental results showed more influential and scattered values for the set of relevant nodes identified by our approach than several reference algorithms, including degree, closeness, Betweenness, VoteRank, HybridRank, and IKS. The results further showed an improvement in the propagation influence value when combining our distribution strategy with classical metrics, such as degree, outperforming computationally more complex strategies. Moreover, our proposal shows a good computational complexity and can be applied to large-scale networks.

scholarly journals An open source analysis framework for large-scale building energy modeling

2020 ◽  
Vol 13 (5) ◽  
pp. 487-500
Author(s):  
Brian L. Ball ◽  
Nicholas Long ◽  
Katherine Fleming ◽  
Chris Balbach ◽  
Phylroy Lopez
Keyword(s):  
Open Source ◽  
Large Scale ◽  
Building Energy ◽  
Energy Modeling ◽  
Source Analysis ◽  
Analysis Framework ◽  
Building Energy Modeling

scholarly journals Locating the Source of Diffusion in Complex Networks via Gaussian-Based Localization and Deduction

2019 ◽  
Vol 9 (18) ◽  
pp. 3758 ◽  
Author(s):  
Xiang Li ◽  
Xiaojie Wang ◽  
Chengli Zhao ◽  
Xue Zhang ◽  
Dongyun Yi
Keyword(s):  
Complex Networks ◽  
Source Localization ◽  
Information Diffusion ◽  
Large Scale ◽  
Computational Cost ◽  
Likelihood Estimation ◽  
Power Grids ◽  
Optimization Approach ◽  
Localization Accuracy ◽  
Diffusion Source

Locating the source that undergoes a diffusion-like process is a fundamental and challenging problem in complex network, which can help inhibit the outbreak of epidemics among humans, suppress the spread of rumors on the Internet, prevent cascading failures of power grids, etc. However, our ability to accurately locate the diffusion source is strictly limited by incomplete information of nodes and inevitable randomness of diffusion process. In this paper, we propose an efficient optimization approach via maximum likelihood estimation to locate the diffusion source in complex networks with limited observations. By modeling the informed times of the observers, we derive an optimal source localization solution for arbitrary trees and then extend it to general graphs via proper approximations. The numerical analyses on synthetic networks and real networks all indicate that our method is superior to several benchmark methods in terms of the average localization accuracy, high-precision localization and approximate area localization. In addition, low computational cost enables our method to be widely applied for the source localization problem in large-scale networks. We believe that our work can provide valuable insights on the interplay between information diffusion and source localization in complex networks.

scholarly journals Large-Scale Complex Network Community Detection Combined with Local Search and Genetic Algorithm

2020 ◽  
Vol 10 (9) ◽  
pp. 3126
Author(s):  
Desheng Lyu ◽  
Bei Wang ◽  
Weizhe Zhang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Community Structure ◽  
Local Search ◽  
Complex Networks ◽  
Community Detection ◽  
Large Scale ◽  
Particle Swarm ◽  
Search Performance ◽  
Swarm Optimization

With the development of network technology and the continuous advancement of society, the combination of various industries and the Internet has produced many large-scale complex networks. A common feature of complex networks is the community structure, which divides the network into clusters with tight internal connections and loose external connections. The community structure reveals the important structure and topological characteristics of the network. The detection of the community structure plays an important role in social network analysis and information recommendation. Therefore, based on the relevant theory of complex networks, this paper introduces several common community detection algorithms, analyzes the principles of particle swarm optimization (PSO) and genetic algorithm and proposes a particle swarm-genetic algorithm based on the hybrid algorithm strategy. According to the test function, the single and the proposed algorithm are tested, respectively. The results show that the algorithm can maintain the good local search performance of the particle swarm optimization algorithm and also utilizes the good global search ability of the genetic algorithm (GA) and has good algorithm performance. Experiments on each community detection algorithm on real network and artificially generated network data sets show that the particle swarm-genetic algorithm has better efficiency in large-scale complex real networks or artificially generated networks.

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