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Based on the existing research of Chinese text clustering, this paper proposes an improved algorithm for the optimization of short term semantic clustering based on social media. The method of weighted factor is introduced to optimize text distance formula and related mathematical proof, the calculation process optimization design from text, written text distance calculation algorithm, the simulation...
Due to the emerging Big Data paradigm, traditional data management techniques result inadequate in many real life scenarios. In particular, the availability of huge amounts of data pertaining to social interactions among users calls for advanced analysis strategies. Furthermore, heterogeneity and high speed of this data require suitable data storage and management tools to be designed from scratch...
With tags widely used in organizing and searching contents in massive data era, how to automatically generate appropriate tags of resource for users became a hot issue on social networks research. Tag recommendation for text resource can be modeled as a keyword extraction problem, hence topic modeling such as LDA which extracts latent semantic topics from text is suitable for tag recommendation. However,...
Forum has become one of the main platforms for people to express their personal point of view, with a lot of information surging in the forum everyday. How to detect automatically a forum topic among the massive information becomes an important and hard task. Though there are plenty of studies for topic detection, it is still a challenge to make it fast and accurately. This paper introduces the principle...
Online Social Networks (OSNs) provide platform to raise opinions on various issues, create and spread news rapidly in Online Social Network Forums (OSNFs). This work proposes a novel method for Profiling Forum Users (PFU) by exploring their behavioral characteristics based on their involvement in various topics of discussion and number of posts in respective topics posted by them in OSNFs dynamically...
The paper identifies the scope of improvement for the search result of a web site. The study includes some commonly used clustering algorithms to identify the usage of clustering approach for improving web elements analysis, in various ways. As the Search result option is extensively used at almost every web site, the main focus is to optimize search result of a web site using clustering approach...
Social network analysis comprises a popular set of tools for the analysis of online social networks. Among these techniques, k-shell decomposition of a graph is a popular technique that has been used for centrality analysis, for communities discovery, for the detection of influential spreaders, and so on. The huge volume of input graphs and the environments where the algorithm needs to run i.e., large...
The incredible growth of the internet use for all kinds of businesses has generated at the same time an increase of fraudulent activities, which calls for developing new methods and tools for detecting fraud and other crimes against banks and customers. Fraud detection needs to analyze and link data, which are gathered from heterogeneous data repositories, and to address problem solving algorithms...
In order to enhance the service quality of network community, satisfy the growing demand of community users, this paper analyzes the content of network community from different aspects and provides a comprehensive method to divide the network community user group. On one hand, this method use link analysis techniques to study hyperlink, calculate the number of output links and input links, and then...
Reputation mechanisms are widely used in online networks to rank users or products, but despite their importance, very few studies have been done or published on their real behavior. In this paper, we study an Internet-deployed distributed reputation mechanism called BarterCast that is specifically designed for peer-to-peer file-sharing systems. The BarterCast mechanism is based on building a weighted...
The capacity of rapidly disseminating information such as latest news headlines has made online social networks a popular and disruptive venue for spreading influence and distributing contents. Given the importance of online social networks, it becomes increasingly imperative to understand the shared interests of users on the popular information or contents that circulate through these networks. This...
In mobility-aware of Internet of Things (IOT), according to the problem of data-aware and data-transmit which casued by nodes mobility and random. We propose the nodes social relations cognition algorithm which based on social network. Firstly, we would quantize the social relation of all nodes by introducing interconnection factor and distance factor. Then, build cohesive subgroups and node-mobile...
Currently, the information in the internet is becoming explosive. In order to help the users searching the items they are interested in, such as, the news, the books, in this paper, we propose an automatic personalized recommendation algorithm by constructing the social graph resting on the users' implicit interaction information. We at first introduce a metric to measure the users' affinity based...
Social network mining technology and its implementation are the subject of much interest and difficulty in recent research. A framework of web-based social network search system is designed to carry out social network search. The paper presents a method to implement web downloader in order to obtain web documents containing social network information. Characteristic vector weight is calculated with...
Being able to keep the graph scale small while capturing the properties of the original social graph, graph sampling provides an efficient, yet inexpensive solution for social network analysis. The challenge is how to create a small, but representative sample out of the massive social graph with millions or even billions of nodes. Several sampling algorithms have been proposed in previous studies,...
Social bookmarking services allow a user to make her personal collection of favorite web resources accessible by the public. The content of this collection can attract users of “similar minds” and therefore has tremendous potential to enable networking and collaboration. In this research, we analyzed a large dataset collected from one of the most popular social bookmarking services. To understand...
The rapidly growing amount of available digital documents of various formats and the possibility to access these through internet-based technologies in distributed environments, have led to the necessity to develop solid methods to properly organize and structure documents in large digital libraries and repositories. Specifically, the extremely large size of document collections make it impossible...
Online Social Networks (OSNs) are becoming more important in the web 2.0 paradigm. Although most implementations of OSN are not distributed applications, users conforming an OSN work autonomously posting their information in the OSN and interacting among them. Users are responsible of the information they post in their profile and, in the vast majority of social networks, they can limit the disclosure...
As the Web contains rich and convenient information, Web search engine is increasingly becoming the dominant information retrieving approach. In order to rank the query results of web pages in an effective and efficient fashion, we propose a new page rank algorithm based on similarity measure from the vector space model, called SimRank, to score web pages. Firstly, we propose a new similarity measure...
Social Networks Service (SNS), is becoming more and more popular and a lot of studies have been carried out in this active field. However, traditional analysis methods based on single machines is not suitable because the network is growing too large. MapReduce, a programming paradigm proposed by Google, gives us a new approach to solve large-scale social networks analysis problem by making use of...
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