Constructing a reliable Web graph with information on browsing behavior

Liu, Yiqun; Xue, Yufei; Xu, Danqing; Cen, Rongwei; Zhang, Min; Ma, Shaoping; Ru, Liyun

doi:10.1016/j.dss.2012.06.001

Cited by 8 publications

(5 citation statements)

References 13 publications

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“…During users' network transactions, the interdependencies and interactions of different operation sequences will generate complex behavior graphs, and different users result in different behavior graph based on their unique behavior preferences. At present, studies on graph behavior mainly include (i) studies on users' network interaction behaviors and motivations [318]; (ii) the understanding of user network behavior and the construction of behavior graph data [319], [320]; (iii) potential correlation analyses of user behavior graphs and research on multiorganization network behavior transfers [321], [322]; (iv) graph-based behavioral motivation analysis [323]; (v) research on behavior analysis, modeling, and prediction methods based on stochastic models and Bayesian models [324], [325]; (vi) research on graph structure analysis algorithms and the development of analysis tools [326]; and (vii) methods for analyzing sequence consistency between two different user graphs [149]. The behavior graph records the complicated interdependencies and interactive logic of users in a network transaction.…”

Section: ) User Network Transaction Behavior Analysismentioning

confidence: 99%

Behavior Analysis for Electronic Commerce Trading Systems: A Survey

et al. 2019

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With the rapid development of the Internet and the continuous progress of computer science and technology, Internet-based online trading has developed rapidly and improved the convenience of everyday life. Increasingly, people conduct business activities through online transactions and payment methods, and the development prospects for online transactions are quite broad. In addition, e-commerce trading systems have attracted considerable attention. Many studies have been conducted to analyze the behaviors of these systems. This paper focuses on analyzing both system behavior and user behavior in e-commerce trading systems. To reflect the characteristics of online trading systems, system behavior reflects the strength of the system, while user behavior reflects various user actions. Consistency, which is a basis for determining whether two objects are similar, provides a reference for designers and modelers. This paper also focuses on analyzing the consistency of system behavior, especially regarding the measurement and diagnosis of e-commerce trading systems. Then, we study the existing user behavior analysis methods for e-commerce trading systems. We divide user behaviors into four groups: web browsing behavior, keystroke behavior, network transaction behavior, and mobile terminal behavior. The performance and correlation analysis techniques of these methods are discussed and compared in detail. Finally, we provide ideas for future research involving behavior analysis for online trading systems.

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Section: ) User Network Transaction Behavior Analysismentioning

confidence: 99%

Behavior Analysis for Electronic Commerce Trading Systems: A Survey

et al. 2019

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“…Naturally, web documents in the web space are linked by hyper-references from one document to another which is referred as structural linkage or relationship. In literature [8] [4], we observed web graph has been formed to represent URLs and their structural linkage. In such a web-graph, nodes represent URLs referring to web pages and the structural linkage between two URLs is represented by an edge.…”

Section: Introductionmentioning

confidence: 99%

A New Type of Web Graph for Personalized Visualization

Saleheen

Lai

2014

2014 IEEE Pacific Visualization Symposium

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Over the past few years, research on web information visualization is focusing on how to present the web information to end users effectively and efficiently. Nevertheless, with increasing size of the information in the web-space, it has become very difficult to visualize information and the inherent relationships among the information. Because of web graphs being messy and large, end users need to allow extra effort to mine interested information from those. Existing web graphs produce relationships among nodes (URLs) based on structural linkage information. As a result, by producing similar visualization to different end users, this approach lacks effectiveness considering diversity of users' need. In our work, we encounter this issue of personalization in visualization of web graph. We propose a different web graph which capitalizes on user interests to develop relationships among nodes of the web graph. Examples show that this web graph can represent web information to the end user for more effective and effortless information search.

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“…Thus, webpages that are visited and hyperlinks that are clicked by users should be regarded as more important than those that are not, even if they belong to the same web page. It is therefore reasonable to use users' preferences to redesign the hyperlink graph of a website [8]. Google Toolbar and Live Toolbar collect user browsing information.…”

Section: Hyperlink Analysismentioning

confidence: 99%

“…Although hyperlink analysis algorithms that produced significant results have been developed, they still need to tackle challenges such as how to incorporate web user behaviour in hyperlink analysis [8]. A website is considered as a graph of nodes and edges representing webpages and hyperlinks respectively.…”

Section: Hyperlink Analysismentioning

confidence: 99%

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Fuzzy Equivalence Relation Based Clustering and Its Use to Restructuring Websites’ Hyperlinks and Web Pages

Kardaras

Mamakou

Karakostas

2013

IFIP Advances in Information and Communication Technology

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This is the unspecified version of the paper.This version of the publication may differ from the final published version. Abstract. Quality design of websites implies that among other factors, hypelinks' structure should allow the users to reach the information they seek with the minimum number of clicks. This paper utilises the fuzzy equivalence relation based clustering in adapting website hyperlinks' structure so that the redesigned website allows users to meet as effectively as possible their informational and navigational requirements. The fuzzy tolerance relation is calculated based on the usage rate of hyperlinks in a website. The equivalence relation identifies clusters of hyperlinks. The clusters are then used to realocate hyperlinks in webpages and to rearrange webpages into the website structure hierarchy. Permanent

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Constructing a reliable Web graph with information on browsing behavior

Cited by 8 publications

References 13 publications

Behavior Analysis for Electronic Commerce Trading Systems: A Survey

Behavior Analysis for Electronic Commerce Trading Systems: A Survey

A New Type of Web Graph for Personalized Visualization

Fuzzy Equivalence Relation Based Clustering and Its Use to Restructuring Websites’ Hyperlinks and Web Pages

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