LED: A fast overlapping communities detection algorithm based on structural clustering

Ma, Tinghuai; Wang, Yao; Tang, Meili; Cao, Jie; Tian, Yuan; Al-Dhelaan, Abdullah

doi:10.1016/j.neucom.2016.05.020

Cited by 82 publications

(17 citation statements)

References 42 publications

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“…And machine learning classification model [ 27 – 29 ] such as the state-of-art v-support vector machine methods [ 30 – 32 ], dimension reduction techniques [ 33 – 35 ], neural-like computing models [ 36 – 38 ] and spiking neural networks [ 39 , 40 ] could be tested. Community detection methods, especially overlapping communities detection [ 41 ] also can be considered for the further improvement.…”

Section: Discussionmentioning

confidence: 99%

A decision support model for investment on P2P lending platform

et al. 2017

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Peer-to-peer (P2P) lending, as a novel economic lending model, has triggered new challenges on making effective investment decisions. In a P2P lending platform, one lender can invest N loans and a loan may be accepted by M investors, thus forming a bipartite graph. Basing on the bipartite graph model, we built an iteration computation model to evaluate the unknown loans. To validate the proposed model, we perform extensive experiments on real-world data from the largest American P2P lending marketplace—Prosper. By comparing our experimental results with those obtained by Bayes and Logistic Regression, we show that our computation model can help borrowers select good loans and help lenders make good investment decisions. Experimental results also show that the Logistic classification model is a good complement to our iterative computation model, which motivates us to integrate the two classification models. The experimental results of the hybrid classification model demonstrate that the logistic classification model and our iteration computation model are complementary to each other. We conclude that the hybrid model (i.e., the integration of iterative computation model and Logistic classification model) is more efficient and stable than the individual model alone.

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Section: Discussionmentioning

confidence: 99%

A decision support model for investment on P2P lending platform

et al. 2017

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“…Ma et al [18] have presented a loop edges delete (LED) algorithm. It is an efficient detection algorithm used for finding overlapping communities in the network based on structural clustering.…”

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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“…Rong et al [45] proposed a novel K+-isomorphism method to achieve the K-anonymization state among subgraphs. Ma et al [46] proposed an efficient overlapping community detection algorithm based on structural clustering. Deng et al [47] proposed a novel collaborative optimization algorithm for solving complex problems.…”

Section: Related Workmentioning

confidence: 99%

Study on an Airport Gate Reassignment Method and Its Application

Zhao

2017

Symmetry

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Bad weather, mechanical failures, air control, and crew members of the discomfort health are very likely to cause flight delays. If these events occur, decision-makers of airport operation must rediscover the flight schedules through reassigning gates to these flights, delaying flights, and canceling flights. Therefore, it is important to study the recovery strategy with the feasibility and the least cost for delayed flights and to improve the airport operation efficiency. In this paper, a mathematical model of gate reassignment based on the objectives of the loss of passengers, airport operating, and airlines, and the most important index of disturbance value of the gate reassignment for delayed flights is constructed. Then, the genetic algorithm (GA) and ant colony optimization (ACO) algorithm are combined in order to propose a two-stage hybrid(GAOTWSH) algorithm, which is used to solve the constructed mathematical model of gate reassignment for delayed flights. The test data from the operations of the one airport is used to simulate and demonstrate the performance of the constructed mathematical model of gate reassignment for irregular flights. The results show that the proposed GAOTWSH algorithm has better optimization performance and the constructed gate reassignment model is feasible and effective. The study provides a new idea and method for irregular flights.

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LED: A fast overlapping communities detection algorithm based on structural clustering

Cited by 82 publications

References 42 publications

A decision support model for investment on P2P lending platform

A decision support model for investment on P2P lending platform

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

Study on an Airport Gate Reassignment Method and Its Application

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