Enhancing network capacity by weakening community structure in scale-free network

Cai, Jun; Wang, Yu; Liu, Yan; Luo, Jianzhen; Wei, Wenguo; Xu, Xiaoping

doi:10.1016/j.future.2017.08.014

Cited by 47 publications

(25 citation statements)

References 22 publications

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“…Moreover, we propose a slope one algorithm based on the fusion of trusted data and user similarity. The algorithm we proposed can applyed in many applications, such as the recommendation system for social networks (Peng et al 2017a;Cai et al 2017;Jiang et al 2016), or loaction-based services (Peng et al 2017b).…”

Section: Resultsmentioning

confidence: 99%

A trust-based collaborative filtering algorithm for E-commerce recommendation system

Jiang

Cheng

Yang

et al. 2018

J Ambient Intell Human Comput

191

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The rise of e-commerce has not only given consumers more choice but has also caused information overload. In order to quickly find favorite items from vast resources, users are eager for technology by which websites can automatically deliver items in which they may be interested. Thus, recommender systems are created and developed to automate the recommendation process. In the field of collaborative filtering recommendations, the accuracy requirement of the recommendation algorithm always makes it complex and difficult to implement one algorithm. The slope one algorithm is not only easy to implement but also works efficient and effective. However, the prediction accuracy of the slope one algorithm is not very high. Moreover, the slope one algorithm does not perform so well when dealing with personalized recommendation tasks that concern the relationship among users. To solve these problems, we propose a slope one algorithm based on the fusion of trusted data and user similarity, which can be deployed in various recommender systems. This algorithm comprises three procedures. First, we should select trusted data. Second, we should calculate the similarity between users. Third, we need to add this similarity to the weight factor of the improved slope one algorithm, and then, we get the final recommendation equation. We have carried out a number of experiments with the Amazon dataset, and the results prove that our recommender algorithm performs more accurately than the traditional slope one algorithm.

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Section: Resultsmentioning

confidence: 99%

A trust-based collaborative filtering algorithm for E-commerce recommendation system

Jiang

Cheng

Yang

et al. 2018

J Ambient Intell Human Comput

191

View full text Add to dashboard Cite

show abstract

“…There are several papers talking about the community structure using in social media network 12,13,14 in the recent years. A network is said to have community structure if the nodes of the network can be easily grouped into (potentially overlapping) sets of nodes such that each set of nodes is densely connected internally.…”

Section: Community Detection In Predicting Electionmentioning

confidence: 99%

“…A network is said to have community structure if the nodes of the network can be easily grouped into (potentially overlapping) sets of nodes such that each set of nodes is densely connected internally. There are several papers talking about the community structure using in social media network 12,13,14 in the recent years. We believe that tweets on social media could construct complex network and community methods could be used to predict the election.…”

Section: Community Detection In Predicting Electionmentioning

confidence: 99%

Every word is valuable: Studied influence of negative words that spread during election period in social media

et al. 2018

Concurrency and Computation

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Studying the influence of negative words that spread during election period is an important work in social media. Most of current methods rely on sentiment analysis of tweets to determine the users' preference. However, sentiment analysis can only makes use of emotional words (ie, adverbs and adjectives), which only take 30 percent of the context in the Internet. According to our empirical analysis based on real datasets, the bias on word selection largely reduced the accuracy of the context in the Internet. In order to address this critical problem, we propose a new method that makes use of nouns with emotional context to determine the election preference of each user. By collecting the frequencies of words in context, we weigh the impact of each supportive/objective noun to strengthen the determination of users' preference. Final results will further be integrated to examine the effectiveness and efficiency of our proposed method. To indicate this idea, we collect and adopt real datasets (UK Prime Minister 2017 and US President Campaign 2016) in the experiments. All the experiment results suggested that our integrated method largely outperformed previous prediction methods. In particular, the prediction results were quite similar to the final results of the UK and US election. Meanwhile, for UK election, we found that the daily approval rate is closely related to the event happened everyday. KEYWORDS negative information influence, social media 1 Concurrency Computat Pract Exper. 2019;31:e4525.wileyonlinelibrary.com/journal/cpe

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“…A complete review for Blockchain and intrusion detection is available in [33]. Reference [34] introduced JFCGuard for detecting juice filming charging attack and [35] enhanced network capacity. A privacy-preserving scheme based on location is introduced in [36].…”

mentioning

confidence: 99%

RoughDroid: Operative Scheme for Functional Android Malware Detection

Riad

2018

Security and Communication Networks

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There are thousands of malicious applications that invade Google Play Store every day and seem to be legal applications. These malicious applications have the ability to link the malware referred to as Dresscode created for network hacking as well as scrolling information. Since Android smartphones are indispensable, there should be an efficient and also unusual protection. Therefore, Android smartphones usually continue to be safeguarded from novel malware. In this paper, we propose RoughDroid, a floppy analysis technique that can discover Android malware applications directly on the smartphone. RoughDroid is based on seven feature sets ( 1 , 2 , . . . , 7 ) from the XML manifest file of an Android application, plus three feature sets ( 8 , 9 , and 10 ) from the Dex file. Those feature sets pass through the Rough Set algorithm to elastically classify the Android application as either benign or malicious. The experimental results mainly consider 20 most common malware families, plus three new malware families (Grabos, TrojanDropper.Agent.BKY, and AsiaHitGroup) that invade Google Play Store at 2017. According to the experimental results, RoughDroid has 95.6% detection performance for the malware families at 1% false-positive rate. Finally, RoughDroid is a lightweight approach for straightly examining downloaded applications on the smartphone.

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Enhancing network capacity by weakening community structure in scale-free network

Cited by 47 publications

References 22 publications

A trust-based collaborative filtering algorithm for E-commerce recommendation system

A trust-based collaborative filtering algorithm for E-commerce recommendation system

Every word is valuable: Studied influence of negative words that spread during election period in social media

RoughDroid: Operative Scheme for Functional Android Malware Detection

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