Community-diversified influence maximization in social networks

Li, Jianxin; Cai, Taotao; Deng, Ke; Wang, Xinjue; Sellis, Timos; Xia, Feng

doi:10.1016/j.is.2020.101522

Cited by 183 publications

(90 citation statements)

References 46 publications

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“…In upcoming research, we will update the suggested PDFM method by considering the possible diversity of data types [32][33][34] and data structure [35][36][37][38]. In addition, how to fuse multiple existing privacy solution for better performances is still an open problem that requires intensive and continuous study.…”

Section: Discussionmentioning

confidence: 99%

Multi-Source Medical Data Integration and Mining for Healthcare Services

et al. 2020

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With the advent of Internet of Health (IoH) age, traditional medical or healthy services are gradually migrating to the Web or Internet and have been producing a considerable amount of medical data associated with patients, doctors, medicine, medical infrastructure and so on. Effective fusion and analyses of these IoH data are of positive significances for the scientific disaster diagnosis and medical care services. However, IoH data are often distributed across different departments and contain partial user privacy. Therefore, it is often a challenging task to effectively integrate or mine the sensitive IoH data, during which user privacy is not disclosed. To overcome the above difficulty, we put forward a novel multi-source medical data integration and mining solution for better healthcare services, named PDFM (Privacy-free Data Fusion and Mining). Through PDFM, we can search for similar medical records in a time-efficient and privacy-preserving manner, so as to offer patients with better medical and health services. A group of experiments are enacted and implemented to demonstrate the feasibility of the proposal in this work.

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Section: Discussionmentioning

confidence: 99%

Multi-Source Medical Data Integration and Mining for Healthcare Services

et al. 2020

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“…Adam is considered to be robust in selecting hyperparameters [11]. Therefore, this paper adopts an adaptive Adam learning rate to optimize the proposed model.…”

Section: Loss Function and Optimizationmentioning

confidence: 99%

Lightweight image classifier using dilated and depthwise separable convolutions

Sun

Zhang

2020

J Cloud Comp

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The image classification based on cloud computing suffers from difficult deployment as the network depth and data volume increase. Due to the depth of the model and the convolution process of each layer will produce a great amount of calculation, the GPU and storage performance of the device are extremely demanding, and the GPU and storage devices equipped on the embedded and mobile terminals cannot support large models. So it is necessary to compress the model so that the model can be deployed on these devices. Meanwhile, traditional compression based methods often miss many global features during the compression process, resulting in low classification accuracy. To solve the problem, this paper proposes a lightweight neural network model based on dilated convolution and depthwise separable convolution with twenty-nine layers for image classification. The proposed model employs the dilated convolution to expand the receptive field during the convolution process while maintaining the number of convolution parameters, which can extract more high-level global semantic features to improve the classification accuracy. Also, the depthwise separable convolution is applied to reduce the network parameters and computational complexity in convolution operations, which reduces the size of the network. The proposed model introduces three hyperparameters: width multiplier, image resolution, and dilated rate, to compress the network on the premise of ensuring accuracy. The experimental results show that compared with GoogleNet, the network proposed in this paper improves the classification accuracy by nearly 1%, and the number of parameters is reduced by 3.7 million.

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“…However, the ground truth in correlation analysis is some kinds of degree in networks, which is a rough metric in evaluating the influence of developers or projects. To rank more precisely, dynamic models are needed for simulating the influence diffusion process [33]. SIR model [34] is a classical epidemic model and is often used to evaluate the ability of information spreading of a node in social networks.…”

Section: Evaluation Metricsmentioning

confidence: 99%

M-BiRank: Co-ranking developers and projects using multiple developer-project interactions in open source software community

Yan

Zhang

et al. 2020

Preprint

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Social collaborative coding is a popular trend in software development and such platforms as GitHub provides rich social and technical functionalities for developers to collaborate on open source projects through multiple interactions. Developers often follow popular developers and projects for learning, technical selection and collaboration. Thus identifying popular developers and projects is very meaningful. In this paper, we propose a multiplex bipartite network ranking model, M-BiRank, to co-rank developers and projects using multiple developer-project interactions. Firstly, multiple developer-project interactions such as commit, issue and watch is extracted and a multiplex developer-project bipartite network is constructed. Secondly, a random layer is selected from this multiplex bipartite network and initial ranking scores are calculated for developers and projects using BiRank. Finally, initial ranking scores diffuse to other layers and mutual reinforcement is taken into consideration to iteratively calculate ranking scores of developers and projects in different layers. Experiments on real world GitHub dataset show that M-BiRank outperforms degree centrality, traditional single layer ranking methods as well as multiplex ranking method.

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Community-diversified influence maximization in social networks

Cited by 183 publications

References 46 publications

Multi-Source Medical Data Integration and Mining for Healthcare Services

Multi-Source Medical Data Integration and Mining for Healthcare Services

Lightweight image classifier using dilated and depthwise separable convolutions

M-BiRank: Co-ranking developers and projects using multiple developer-project interactions in open source software community

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