Influence Propagation Model for Clique-Based Community Detection in Social Networks

Alduaiji, Noha; Datta, Amitava; Li, Jianxin

doi:10.1109/tcss.2018.2831694

Cited by 42 publications

(19 citation statements)

References 44 publications

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“…Some authors who approach this theme, item 7 of Table 1, are [42,43]. Social media also compose this element, as reported by [30]. The interaction uses a medium and has content.…”

Section: Communication In Dsd and Related Workmentioning

confidence: 99%

CFDSD: a Communication Framework for Distributed Software Development

et al. 2020

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Due to geographical and/or temporal dispersion, communication between teams in distributed software projects is a critical factor for success. Notably, distributed teams suffer adverse physical and temporal dispersion effects during an information exchange. To mitigate problems arising from interactions, it is important to understand the communication structure of teams during distributed projects. The objective of this work is to present the Communication Framework for Distributed Software Development. This framework groups a set of distributed projects communication concepts and enables a unified view of all stakeholders' intercommunications that use many interaction technologies. The goal of this framework is to portray the dynamics of the interaction between distributed teams in a multi-tier structure, and each tier approaches a single function in the intercommunication process. We analyzed all interactions from distributed teams after developing an experimental IT Project, using the content analysis method to validate the Communication Framework for Distributed Software Development. The main contribution of this work is the framework specification, and the investigation, which has the potential to help mapping communication patterns in DSD. Moreover, this framework comprises interfaces for communication assessment and includes intercommunications from automated engineering tools as bots.

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“…Some authors who approach this theme, item 7 of Table 1, are [42,43]. Social media also compose this element, as reported by [30]. The interaction uses a medium and has content.…”

Section: Communication In Dsd and Related Workmentioning

confidence: 99%

CFDSD: a Communication Framework for Distributed Software Development

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Previous studies of information propagation prediction in social networks refer to one of the following three tasks: predicting information popularity [11][12][13][14], foretelling user influence [15][16][17][18], and divining information diffusion paths (links) [19][20][21][22]. Some of the literature focuses on the user influence in the social analysis [15,17]. Some significant studies are concerned with link prediction to reveal the evolution of real social networks [19,20].…”

Section: Related Workmentioning

confidence: 99%

Evolutionary Prediction of Nonstationary Event Popularity Dynamics of Weibo Social Network Using Time-Series Characteristics

Chen

Lan

Wan

et al. 2021

Discrete Dynamics in Nature and Society

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A growing number of web users around the world have started to post their opinions on social media platforms and offer them for share. Building a highly scalable evolution prediction model by means of evolution trend volatility plays a significant role in the operations of enterprise marketing, public opinion supervision, personalized recommendation, and so forth. However, the historical patterns cannot cover the systematical time-series dynamic and volatility features in the prediction problems of a social network. This paper aims to investigate the popularity prediction problem from a time-series perspective utilizing dynamic linear models. First, the stationary and nonstationary time series of Weibo hot events are detected and transformed into time-dependent variables. Second, a systematic general popularity prediction model N- SEP 2 M is proposed to recognize and predict the nonstationary event propagation of a hot event on the Weibo social network. Third, the explanatory compensation variable social intensity (SI) is introduced to optimize the model N- SEP 2 M. Experiments on three Weibo hot events with different subject classifications show that our prediction approach is effective for the propagation of hot events with burst traffic.

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“…Therefore, an object detection method based on image blocks is proposed. As shown in Figure 1(b), the input image is divided into blocks according to a certain strategy [15], and then each block is trained according to our SSD method.…”

Section: Image Block Architecturementioning

confidence: 99%

“…Our SSD model has a large training parameter. If we train all the characteristics of the network from scratch, it is not only time-consuming but also prone to data overfitting and gradient non-convergence [15]. In this paper, the transfer learning [16] method is applied.…”

Section: Training Of Our Ssd Modelmentioning

confidence: 99%

A Block Object Detection Method Based on Feature Fusion Networks for Autonomous Vehicles

Meng

Song

et al. 2019

Complexity

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Nowadays, automatic multi-objective detection remains a challenging problem for autonomous vehicle technologies. In the past decades, deep learning has been demonstrated successful for multi-objective detection, such as the Single Shot Multibox Detector (SSD) model. The current trend is to train the deep Convolutional Neural Networks (CNNs) with online autonomous vehicle datasets. However, network performance usually degrades when small objects are detected. Moreover, the existing autonomous vehicle datasets could not meet the need for domestic traffic environment. To improve the detection performance of small objects and ensure the validity of the dataset, we propose a new method. Specifically, the original images are divided into blocks as input to a VGG-16 network which add the feature map fusion after CNNs. Moreover, the image pyramid is built to project all the blocks detection results at the original objects size as much as possible. In addition to improving the detection method, a new autonomous driving vehicle dataset is created, in which the object categories and labelling criteria are defined, and a data augmentation method is proposed. The experimental results on the new datasets show that the performance of the proposed method is greatly improved, especially for small objects detection in large image. Moreover, the proposed method is adaptive to complex climatic conditions and contributes a lot for autonomous vehicle perception and planning.

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Influence Propagation Model for Clique-Based Community Detection in Social Networks

Cited by 42 publications

References 44 publications

CFDSD: a Communication Framework for Distributed Software Development

CFDSD: a Communication Framework for Distributed Software Development

Evolutionary Prediction of Nonstationary Event Popularity Dynamics of Weibo Social Network Using Time-Series Characteristics

A Block Object Detection Method Based on Feature Fusion Networks for Autonomous Vehicles

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