Effector Detection in Social Networks

Tong, Guangmo; Li, Shasha; Wu, Weili; Du, Ding‐Zhu

doi:10.1109/tcss.2016.2627811

Cited by 21 publications

(7 citation statements)

References 25 publications

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“…Social networks promote communication between network members, and the network topology often facilitates the efficient and effective propagation of information [22]. A fundamental, and accepted principle, in network analysis is the presumption that nodes are not independent, but in fact influence each other through the relationships and interactions between them.…”

Section: B the Social Network Of Large Enterprise System Implementationsmentioning

confidence: 99%

Conflict Propagation Within Large Technology and Software Engineering Programmes: A Multi-Partner Enterprise System Implementation as Case Study

Williams

2019

IEEE Access

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Enterprise system implementations are increasingly outsourced to multiple third-party service providers. These multi-partner technology and software engineering programmes are usually organized through project teams that align to the functional areas of the software. Cognitive, occupational and personal differences between members of project teams increases the potential for conflict, which in extreme cases may propagate throughout the entire social network of the programme. Using social network analysis and thematic coding analysis, within a single case study, new insights are provided into the development of conflict within and between individual project teams of large technology and software programmes, such as those seen within enterprise system implementations. A conceptual framework has been developed that builds on existing literature around conflict in groups, to explore how task, process and relationship conflict can develop in large enterprise system implementations. The conceptual framework illustrates how conflict, once developed, can propagate throughout the social network of the wider programme. Finally, we argue that high-conflict organizations, such as the temporary multi-partner implementation team that forms to deliver large technology and software programmes, have a tendency to contain competing networks, which actively promotes conflict. We conclude by setting the agenda for further research on how we may contain the spread of conflict once it has developed within technology and software engineering programme environments.INDEX TERMS Conflict, conceptual framework, enterprise system, software engineering project, project and programme management.

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Section: B the Social Network Of Large Enterprise System Implementationsmentioning

confidence: 99%

Conflict Propagation Within Large Technology and Software Engineering Programmes: A Multi-Partner Enterprise System Implementation as Case Study

Williams

2019

IEEE Access

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“…Here V, E and W are same with in G; ℙ is the sign matrix of edges. Each value in ℙ is determined by (1). Please note that, ℙ u,v ≠ ℙ v,u .…”

Section: Modellenmesi)mentioning

confidence: 99%

“…The EGA eliminates the weak nodes, and then applies greedy approach and discount strategy for selecting seed set. The EGA picks all the nodes oneby-one as seed node, and repeats the propagation 20.000 times in each iteration [1]. Only one node is selected as the seed in one iteration.…”

Section: Aç Gözlü Algoritma (Ega))mentioning

confidence: 99%

“…the influencers) and spread across the network. In a more formal description, the IM problem is the detection of top-k influencers that will maximize the propagation of a desired influence on a social network modeled as Graph G under a particular propagation model (analytical model showing how information is spread over a network) [1], [2]. The IM problem has been formulated as a combinatorial optimization problem, and its complexity is NP-Hard under many propagation models such as Independent Cascade (IC) model and Linear Threshold (LT) model [2]- [4].…”

Section: Introduction (Gi̇ri̇ş)mentioning

confidence: 99%

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A New Greedy Algorithm For Influence Maximization On Signed Social Networks

Şimşek¹

2019

GMBD

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Sosyal etki insanların görüşlerini şekillendiren büyük olgulardan biridir. Bu bakımdan, Etki Maksimizasyonu (EM) problemi viral pazarlama, kamuoyu şekillendirme gibi pratik faydaları olduğu için sosyal ağ analizinde en fazla ilgili çeken araştırma alanlarından biridir. EM probleminin amacı bir sosyal ağ üzerindeki etkili kişi olarak adlandırılan az sayıdaki kişiyi kullanarak bir etkinin (bir fikir veya reklam) ağ üzerindeki yayılımını maksimize etmektir. Etkili kişilerin tespiti birçok durumda NP-Zor bir olasılıksal en iyileme problemidir. Bundan dolayı, EM problemi için birçok algoritma geliştirilmiştir ve geliştirilmeye devam etmektedir. Ne var ki, geliştirilen algoritmalar henüz çözüm kalitesi ve hız açısından istenen seviyede değildirler. Bu çalışmada, bireyler arasındaki olumlu ve olumsuz ilişkileri göz önünde bulunduran işaretli EM problemine odaklanılmıştır. Bu amaçla, en iyi k adet etkili kişiyi tespit etmek için Elitist Aç Gözlü Algoritma (EGA) olarak adlandırılan bir aç gözlü algoritma geliştirmiştir. EGA'nın performansı 2 adet açık veriseti üzerinde rasgele seçim, çıkış derecesi merkeziliği, ve bir güncel algoritma ile kıyaslanmıştır. EGA çözüm kalitesi açısından rakiplerine göre daha iyi sonuçlar vermiştir.https://dx.The social influence is one of the major phenomenons that shape people's decisions. In this respect, Influence Maximization (IM) problem is one of the most attractive research topics in the social network analysis because its practical benefits in viral marketing, public opinions shaping etc. The IM problem aims to maximize the spread of an influence (e.g. an opinion, an advertisement) in a social network by using a small number of the most effective individuals, whom is called influencers. Detecting the influencers is the NP-Hard combinatorial optimization problem in most cases. Therefore, many algorithms have been and are being developed for the IM problem. However, the algorithms have not yet achieved to the desired solution quality and speed. In this study, we focused on the signed IM problem that considers both positive and negative influence between the individuals. For this purpose, we developed a greedy algorithm called the Elitist Greedy Algorithm (EGA) for detecting top-k influencers set. We compared the EGA's performance on 2 public datasets with random seed selection, out degree heuristic, and one stateof-the-art greedy algorithm. The EGA outperforms the competitors in terms of the achieved total influence. https://dx.

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“…More than 90% of traffic accidents for light-vehicles in the US were reported to be caused by driver errors such as misbehaviour and inadvertent errors, which is similar to other countries worldwide. It was also mentioned in [7][8][9][10][11] that traffic accidents could be reduced by 10% to 20% by correctly recognizing driver behaviours. Therefore, it is critical to have a clear perspective of driver behaviour and the tasks being performed.…”

Section: A Motivationsmentioning

confidence: 99%

Identification and Analysis of Driver Postures for In-Vehicle Driving Activities and Secondary Tasks Recognition

Xing

Zhang

Wang

et al. 2018

IEEE Trans. Comput. Soc. Syst.

121

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Driver decisions and behaviours regarding the surrounding traffic are critical to traffic safety. It is important for an intelligent vehicle to understand driver behaviour and assist in driving tasks according to their status. In this study, the consumer range camera Kinect is used to monitor drivers and identify driving tasks in a real vehicle. Specifically, seven common tasks performed by multiple drivers during driving are identified in this study. The tasks include normal driving, left, right, and rear mirror-checking, mobile phone answering, texting using a mobile phone with one or both hands, and the setup of in-vehicle video devices. The first four tasks are considered safe driving tasks while the other three tasks are regarded as dangerous and distracting tasks. The driver behaviour signals collected from the Kinect consist of a colour and depth image of the driver inside the vehicle cabin. Additionally, three-dimensional head rotation angles and the upper body (hand and arm at both sides) joint positions are recorded. Then, the importance of these features to behaviour recognition is evaluated using Random Forests (RF) and Maximal Information Coefficient (MIC) methods. Next, a Feedforward Neural Network (FFNN) is used to identify the seven tasks. Finally, the model performance for task recognition is evaluated with different features (body only, head only, and combined). The final detection result for the seven driving tasks among five participants achieved an average of greater than 80% accuracy, and the FFNN tasks detector is proved to be an efficient model that can be implemented for real-time driver distraction and dangerous behaviour recognition.

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Effector Detection in Social Networks

Cited by 21 publications

References 25 publications

Conflict Propagation Within Large Technology and Software Engineering Programmes: A Multi-Partner Enterprise System Implementation as Case Study

Conflict Propagation Within Large Technology and Software Engineering Programmes: A Multi-Partner Enterprise System Implementation as Case Study

A New Greedy Algorithm For Influence Maximization On Signed Social Networks

Identification and Analysis of Driver Postures for In-Vehicle Driving Activities and Secondary Tasks Recognition

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