Searching Steiner trees for web graph query

Lee, Wookey; Loh, Woong-Kee; Sohn, Mye M.

doi:10.1016/j.cie.2011.11.013

Cited by 11 publications

(6 citation statements)

References 28 publications

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“…The basic algorithm for circuit searching is backtracking approach based on depth-first search [5,13,17]. In depth-first search, edges are explored out of the most recently discovered vertex v that still has unexplored edges leaving it.…”

Section: Circuit Searching Algorithmsmentioning

confidence: 99%

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Social Network Community Detection Using Strongly Connected Components

Lee

Kim

2014

Lecture Notes in Computer Science

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Abstract. The hasty information growth of social network poses the information searching efficiency trials for network mining research. Social network graphs and web graphs are huge sources of highly densely connected hypertext links so that the social networks can be described by a directed graph. This kind of network has inherent structural characteristics such as overly expanded, duplicated, connectedness, and circuit paths, which could generate serious challenges for structured searching for sub-network isomorphism and community detection. In this paper, an efficient searching algorithm is suggested to discover social network communities for overcoming the circuit path issue embedded in the social network environment. Experimental results indicate that the proposed algorithm has better performance than the traditional circuit searching algorithms in terms of the time complexity as well as performance criteria.

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Section: Circuit Searching Algorithmsmentioning

confidence: 99%

“…1, a Social networkis suited to represent the Web, where a web page is set as a vertex and hyperlink as an edge that is widely accepted as the basic approach to analyze the Web structure [1,5,6,8]. The Web structure can be easily constructed the search and storing data structure by using graphs.…”

Section: Introductionmentioning

confidence: 99%

Social Network Community Detection Using Strongly Connected Components

Lee

Kim

2014

Lecture Notes in Computer Science

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“…Most current desktop applications of Internet behavior management are designed to limit the online behavior of employees in order to protect corporation's information security. The function of this kind software includes the prohibition or monitoring something such as BT, stocks, chat, MSN, QQ, monitoring e-mail and bandwidth, in order to reduce viruses infecting and achieve the purpose of the correct guidance of employees Internet usage [1][2][3][4][5] . However, mobile devices have small memory and limited resources, what's more, for security reason, most of mobile operating systems have strict restrictions on the functionality of applications and permission management, so it is difficult to obtain permission to manage any application such as stocks, QQ.…”

Section: Introductionmentioning

confidence: 99%

An Internet behavior management system based on Android

Liu¹,

Hou²,

Gao

et al. 2013

JSW

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With the popularity of smart phones and mobile Internet, how to prevent young people from being addicted to Internet and games has become one of the issues which are most concern to parents. To solve this problem, we design and implement an Internet behavior management system which is based on Android. The system implements the mobile phone remote control, network interception and application running control. By using the system, users can filter communication data which contain sensitive words out of children’s mobile phones, monitor behavior of software in the children’s mobile phones, and limit the running time of them.

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“…Secondly, there is no guarantee whether the movement is correctly made in each iterations. To get over this problem, a two-stage method is suggested both of these problems [19,27,36]. It is possible to guarantee that the correct move is made by using statistical selection methods.…”

Section: Optimized Combinatorial Cluster Algorithmsmentioning

confidence: 99%

“…As a new data processing ear like Big Data, Cloud Computing, and Internet of Things (IoT) approaches, the amount of data being collected in databases far exceeds the ability to reduce and analyze these data without the use of automated analysis techniques, data mining [25][26][27]34,36]. As the importance of data mining has grown, one of the critical issues to emerge is how to scale data mining techniques to larger and larger databases [2,24,35].…”

Section: Introductionmentioning

confidence: 99%

Optimized combinatorial clustering for stochastic processes

Kim¹,

Lee

Song

et al. 2017

Cluster Comput

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As a new data processing era like Big Data, Cloud Computing, and Internet of Things approaches, the amount of data being collected in databases far exceeds the ability to reduce and analyze these data without the use of automated analysis techniques, data mining. As the importance of data mining has grown, one of the critical issues to emerge is how to scale data mining techniques to larger and complex databases so that it is particularly imperative for computationally intensive data mining tasks such as identifying natural clusters of instances. In this paper, we suggest an optimized combinatorial clustering algorithm for noisy performance which is essential for large data with random sampling. The algorithm outperforms conventional approaches through various numerical and qualitative thresholds like mean and standard deviation of accuracy and computation speed.

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Searching Steiner trees for web graph query

Cited by 11 publications

References 28 publications

Social Network Community Detection Using Strongly Connected Components

Social Network Community Detection Using Strongly Connected Components

An Internet behavior management system based on Android

Optimized combinatorial clustering for stochastic processes

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