Social Network Analysis

Chen, Liming; Mulvenna, Maurice; Quinn, Darren

doi:10.4018/jaci.2012070104

Cited by 26 publications

(14 citation statements)

References 40 publications

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“…for ← 1 to do (8) if ≥ ℎ sending to contact end if (10) end for (11)end for (13)return according to their relationships [9][10][11]. Social trust is a key factor that affects the sharing of knowledge and the development of social relationships [12,13]: users are more likely to accept suggestions from others with high trust value and interests similarity [14].…”

Section: Inputmentioning

confidence: 99%

CPSFS: A Credible Personalized Spam Filtering Scheme by Crowdsourcing

Liu

Zou

Zhang

et al. 2017

Wireless Communications and Mobile Computing

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Email spam consumes a lot of network resources and threatens many systems because of its unwanted or malicious content. Most existing spam filters only target complete-spam but ignore semispam. This paper proposes a novel and comprehensive CPSFS scheme: Credible Personalized Spam Filtering Scheme, which classifies spam into two categories: complete-spam and semispam, and targets filtering both kinds of spam. Complete-spam is always spam for all users; semispam is an email identified as spam by some users and as regular email by other users. Most existing spam filters target complete-spam but ignore semispam. In CPSFS, Bayesian filtering is deployed at email servers to identify complete-spam, while semispam is identified at client side by crowdsourcing. An email user client can distinguish junk from legitimate emails according to spam reports from credible contacts with the similar interests. Social trust and interest similarity between users and their contacts are calculated so that spam reports are more accurately targeted to similar users. The experimental results show that the proposed CPSFS can improve the accuracy rate of distinguishing spam from legitimate emails compared with that of Bayesian filter alone.

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Section: Inputmentioning

confidence: 99%

CPSFS: A Credible Personalized Spam Filtering Scheme by Crowdsourcing

Liu

Zou

Zhang

et al. 2017

Wireless Communications and Mobile Computing

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“…Calculated by: a count of the number of ties to other nodes in the network (Quinn, Chen and Mulvenna, 2012) Closeness Centrality…”

Section: Case Selectionmentioning

confidence: 99%

“…Calculated by: the sum of geodesic distances to all other nodes (Quinn, Chen and Mulvenna, 2012) To complete these projects, more than 1000 relevant companies were involved in the construction activities. These companies and their relations form a giant organizational network system with complex relationships.…”

Section: Case Selectionmentioning

confidence: 99%

Organizational Network Evolution and Governance Strategies in Megaprojects

Pang

et al. 2015

CEB

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The organization is the key factor for megaprojects in which thousands of connections and relations intertwine and influence the project performance. However, organizational evolution in megaprojects has not been fully studied. This study investigates the evolution of the organizational network of a megaproject in China using social network analysis (SNA), and then proposes corresponding governance strategies. The result shows that megaproject organizations evolve towards more connected networks but are differentiated for various investors. For government invested projects, the organizational network is well connected, cooperative, yet unstable and require strategic long-term governance policies; for private invested projects, the network is stable, but collaboration among participants is low, which indicates a need to establish collaborative governance structures. The result complements the organizational evolution theory for megaprojects and offers effective strategies for governing megaproject organizations. This study also helps practitioners better understand the nature and characteristics of megaproject organizations.

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“…\\ The World Wide Web has brought change to a point where it would be difficult to imagine a world not connected through online networks. In 2010, over 58\% of European's population (813 million) were internet users (Internet Worlds Stats, 2010), and online social network sites has emerged as one of the latest innovative applications from the web [4]. The idea starts with connecting people and evolved to content or opinion sharing.…”

Section: Introductionmentioning

confidence: 99%

“…Online Social Networking(OSN) has been designed for two primary purposes; (i) to enable the sharing and interaction of data, (ii) to support the social activities of users, Meanwhile, Trends in social networking has be seen to have been driven by the advancements in technology, with many of today's popular sites developed after the emergence of web2.0, it provided users with generation of sites which facilitated interactive information sharing, enabling users to become active authors and contributors of content [4]. With the appearance of "Facebook Developer Platform", the number of applications uses Social Network horribly rises.…”

Section: Introductionmentioning

confidence: 99%

The Road to Emotion Mining in Social Network

Mohamed¹,

Ezzat²,

Sami³

2015

IJCA

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Affective computing is a highly computer research trend in last years, which related to human emotions and how computer interacts. While emotion is a fundamental in human experience, it becomes an ideal resource for servicing business or decision making. In ancient times, natural interfaces are likely to be used to provide ubiquitous computing. Although great achievement done, there still exist three challenges which are Cheap, Low power and software system. With the explosive growth of social media, people are using it to express their emotion or opinion. Currently, there are large amount of user generated data in different format ( i.e. Blog, Tweets, Posts, discussion forums) that represent individual expression feelings towards daily life activates whether it is product, topic, event, news, or personal life. As a result, a lot of researchers are done for detecting what humans feel now in social network; they fall under the scope of topic called emotion mining, opinion mining, or sentiment analysis. In this paper, we will survey the development done for emotion mining with a comparative study for different approaches. In addition, an investigation for technology used in this area and how it is applied, will be presented.

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Social Network Analysis

Cited by 26 publications

References 40 publications

CPSFS: A Credible Personalized Spam Filtering Scheme by Crowdsourcing

CPSFS: A Credible Personalized Spam Filtering Scheme by Crowdsourcing

Organizational Network Evolution and Governance Strategies in Megaprojects

The Road to Emotion Mining in Social Network

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