A Neighborhood-Impact Based Community Detection Algorithm via Discrete PSO

Zhou, Dongqing; Wang, Xing

doi:10.1155/2016/3790590

Cited by 13 publications

(3 citation statements)

References 35 publications

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“…The green and red groups represent a large community, which is consistent with the actual network division. The sub community contains the nodes 4,5,9,12,16,19,24,25,30,36,40,46,52,56, and 60, which are represented by green color in the community (Fig. 8).…”

Section: The Dolphin Sociality Networkmentioning

confidence: 99%

A Fast Algorithm for Community Detection of Network Systems in Smart City

Liu

Xie

2019

IEEE Access

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In this paper, a novel algorithm is designed to detect the community structure of network systems in the smart city based on the biogeography-based optimization (BBO) algorithm and the Newman, Moore, and Watts (NMW) small-world network. We have incorporated the NMW small-world network to the BBO algorithm to enhance the ability of migration of the habitat by using the connection mechanism of the NMW small-world network. With the help of small-world network information sharing, the convergence speed of the BBO algorithm has significantly improved. The first step of the algorithm design is to generate an NMW small-world network containing nodes equal to the number of habitats with good connectivity, which facilitates better information exchange between the nodes. In the second step, the habitat in the BBO algorithm is dynamically assigned to the small world network, and then, the BBO algorithm migrates and mutates according to the connection relationship of the NMW small-world network. Finally, the new designed NMW-BBO algorithm is evaluated for community detection via four real networks and computer-generated networks, and one of them is exhibited the characteristics of a large network. The numeric simulations are also employed to demonstrate that the new algorithm exhibits better accuracy and robustness.INDEX TERMS BBO algorithm, NMW small world network, smart city, complex network, community detection.

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Section: The Dolphin Sociality Networkmentioning

confidence: 99%

A Fast Algorithm for Community Detection of Network Systems in Smart City

Liu

Xie

2019

IEEE Access

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“…Zhou et al [23] have addressed particle swarm optimization (PSO) into community detection problem, and an algorithm based on new label strategy.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Optimization Algorithm for Community and Fraud Detection in Complex Networks for High Immunity Towards Link and Node Failures

Singh¹,

Singh²

2018

IJIES

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Abstract:The complex networks are offering a high resource of heterogeneous data and the proper and efficient analysis discovers the unknown information and relations in networks. Due to the huge number of users and nonfamiliar fraud detection system in complex networks, a lot of online frauds introduce to affects the networks. In this paper, we concentrate on both community and fraud detection to minimize the link and node failures in the complex networks. A hybrid optimization algorithm proposed for community and fraud detection in the complex networks (HCFD-Net). The first contribution is to detect the community based on fruit fly optimization algorithm with differential evolution (FOADE). The second contribution is that the fraud detection is achieved by contingency table terminology with multi-link metrics. The performance of the HCFD-Net is analyzed on different five real-world networks are Zachary's karate club, Bottlenose dolphins', American college football, American political books, and Amazon online purchase network. The simulation result shows that the proposed HCFD-NET perform very efficient than existing algorithms in terms of normalized mutual information (NMI) and network lifetime.

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“…The proposal of modularity makes the problem of non-overlapping community detection unprecedented developed. A bunch of modularity optimization algorithms were subsequently proposed, some of which are based on splitting or aggregation [13]- [16], while many others use optimization algorithms to maximizes the modular Q, such as annealing [17], Particle Swarm Optimization (PSO) [18], external optimization [19] and spectral optimization [20]. These algorithms translate community detection into an optimization problem and try to find the optimal community division by maximizing certain fitness.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Evolutionary Perturbation Attack on Community Detection

Chen

Zhao

et al. 2019

Preprint

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Community detection, aiming to group nodes based on their connections, plays an important role in network analysis, since communities, treated as meta-nodes, allow us to create a large-scale map of a network to simplify its analysis. However, for privacy reasons, we may want to prevent communities from being discovered in certain cases, leading to the topics on community deception. In this paper, we formalize this community detection attack problem in three scales, including global attack (macroscale), target community attack (mesoscale) and target node attack (microscale). We treat this as an optimization problem and further propose a novel Evolutionary Perturbation Attack (EPA) method, where we generate adversarial networks to realize the community detection attack. Numerical experiments validate that our EPA can successfully attack network community algorithms in all three scales, i.e., hide target nodes or communities and further disturb the community structure of the whole network by only changing a small fraction of links. By comparison, our EPA behaves better than a number of baseline attack methods on six synthetic networks and three real-world networks. More interestingly, although our EPA is based on the louvain algorithm, it is also effective on attacking other community detection algorithms, validating its good transferability.

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A Neighborhood-Impact Based Community Detection Algorithm via Discrete PSO

Cited by 13 publications

References 35 publications

A Fast Algorithm for Community Detection of Network Systems in Smart City

A Fast Algorithm for Community Detection of Network Systems in Smart City

Hybrid Optimization Algorithm for Community and Fraud Detection in Complex Networks for High Immunity Towards Link and Node Failures

Multiscale Evolutionary Perturbation Attack on Community Detection

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