Named entity discovery and linking is the fundamental and core component of question answering. In Question Entity Discovery and Linking (QEDL) problem, traditional methods are challenged because multiple entities in ...
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With the rapid development of Internet,Internet traffic and end hosts continue to grow in *** behavior analysis for a large-scale network is becoming more and more *** address these challenges,this paper proposes an I...
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With the rapid development of Internet,Internet traffic and end hosts continue to grow in *** behavior analysis for a large-scale network is becoming more and more *** address these challenges,this paper proposes an Internet traffic analysis approach based on community detection to discover community consisted of end hosts with similar traffic behavior in a large campus ***,we use only the IP-to-IP information without packet payloads to model the similarity of end hosts in campus *** the similarity graph which represent the social behavior similarity of all end hosts is ***,we leverage label Propagation algorithm to discover end hosts community on the similarity *** satisfy demands for the scalable analysis of evergrowing Internet traffic data,a Spark-based Internet traffic analysis system is developed,including implementing the above *** experimental results based on real campus network traffic show the benefits of the proposed approach in analyzing traffic behavior of a large-scale network on host community level and detecting potential anomalous traffic *** proposed approach reduces the complexity of analyzing the traffic behavior of a large network compare with analyzing individual *** addition,the experimental results also demonstrate the Spark-based Internet traffic analysis system can analyze Internet traffic efficiently.
The forwarding strategy is the key to the resiliency and efficiency of Named data Networking (NDN), which is a new and fundamental research area. For forwarding strategy, dynamically selecting an optimal interface fro...
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ISBN:
(纸本)9781467364300
The forwarding strategy is the key to the resiliency and efficiency of Named data Networking (NDN), which is a new and fundamental research area. For forwarding strategy, dynamically selecting an optimal interface from multiple alternative interfaces to forward an Interest packet is indeed a multiple attribute decision making (MADM) problem. In this paper, an entropy-based probabilistic forwarding (EPF) strategy is proposed to make a stochastic interface selection based on the combination of interfaces' dynamic availabilities and static routing information, which achieves better load balance in comparison with deterministic interface selection. By objectively assigning weights to attributes and considering multiple real-time network condition metrics, EPF can obtain the availabilities of interfaces more accurately and comprehensively. Since additional network metrics can be easily added and integrated into interfaces' assessment model, EPF provides good extensibility. In addition, we innovatively define two parameters (γ, δ) which can be used to trade off the effect factors between static routing information and dynamic running status of interfaces to customize EPF strategy for different network and application scenarios. Experiments show that EPF can realize preferable load balance and achieve higher throughput compared to the representative BestRoute forwarding strategy.
The new social media such as Twitter and Sina Weibo has become an increasingly popular channel for spreading influence, challenging traditional media such as TVs and news-papers. The most influential and verified user...
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ISBN:
(纸本)9781467364300
The new social media such as Twitter and Sina Weibo has become an increasingly popular channel for spreading influence, challenging traditional media such as TVs and news-papers. The most influential and verified users, also called big-V accounts on Sina Weibo often attract million of followers and fans, creating massive "celebrity-centric" social networks on the social media, which play a key role in disseminating breaking news, latest events, and controversial opinions on social issues. Given the importance of these accounts, it is very crucial to understand social networks and user influence of these accounts and profile their followers' behaviors. Towards this end, this paper monitors a selected group of influential users on Sina Weibo and collects their tweet streams as well as retweeting and commenting activities on these tweets from their followers. Our analysis on tweet data streams from Sina Weibo reveals when and what the followers comment on the tweets of these influential users, and discovers different temporal patterns and word diversity in the comments. Based on the insight gained from follower characteristics, we further develop simple and intuitive algorithms for classifying the followers into spammers and normal fans. Our experimental results demonstrate that the proposed algorithms are able to achieve an average accuracy of 95.20% in detecting spammers from the followers who have commented on the tweets of these influential accounts.
Sina Weibo, a Twitter-like microblogging site attracting over 240 million monthly active users to tweet, retweet, and comment, has rapidly become one of the most popular social media sites in China. As many users crea...
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ISBN:
(纸本)9781467364300
Sina Weibo, a Twitter-like microblogging site attracting over 240 million monthly active users to tweet, retweet, and comment, has rapidly become one of the most popular social media sites in China. As many users create new and innovative words on their tweets and comments, it is necessary to extract these emerging words, which do not exist in today's Chinese vocabulary or dictionary. Towards this end, this paper proposes a novel method based on data clustering of Weibo users and tweets for extracting unknown words from Weibo tweets and comments. Specifically, relying on the similarity of the users who post the tweets, we apply a hierarchical clustering to divide Weibo data into distinct groups, e.g., sports, news stories, movies, before extraction. Comparing with the method of unclustered Weibo data, our experimental results have successfully demonstrated the benefits of the proposed data clustering scheme for improving the recall and accuracy of extracting unknown Chinese words from tweets and comments.
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