Discovery, analysis and monitoring of hidden social networks and their evolution

Goldberg, Mark; Hayvanovych, Mykola; Hoonlor, Apirak; Kelley, Stephen; Magdon‐Ismail, Malik; Mertsalov, Konstantin; Szymański, Bolesław K.; Wallace, William A.

doi:10.1109/ths.2008.4637294

Cited by 11 publications

(5 citation statements)

References 9 publications

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“…The algorithm was previously developed and applied successfully in inter-personal social networks to detect and analyze overlapping communities. In social networks, such communities are common, since many people are involved in more than one groups reflecting their various interests (Baumes et al, 2008;Goldberg et al, 2008Goldberg et al, , 2010. CIS starts by taking each vertex of the graph as a candidate set and attempting to add/delete each other vertex into the set as suggested by a density metric based on the input and output weights of the whole set.…”

Section: Introductionmentioning

confidence: 99%

Detecting overlapped functional clusters in resting state fMRI with Connected Iterative Scan: A graph theory based clustering algorithm

Yan

Kelley

Goldberg

et al. 2011

Journal of Neuroscience Methods

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Section: Introductionmentioning

confidence: 99%

Detecting overlapped functional clusters in resting state fMRI with Connected Iterative Scan: A graph theory based clustering algorithm

Yan

Kelley

Goldberg

et al. 2011

Journal of Neuroscience Methods

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“…While coordination patterns are used to lead a reverse engineering to recover architectural decisions, and are focused on low-level compositions of code, the architectural/design patterns work at a higher level, being used to define the architecture of a system in earlier phases of the software development process [16]. Patterns and patterns finding are a very interesting matter on data mining discipline [5], and are being applied on several areas, with focus on social networks [18,13]. Although the work reported in this paper is far from the area of data mining and social networks, the purpose of CoordL is to extract useful information based on patterns that are a result of previous knowledge on how coordination of components is made.…”

Section: Related Workmentioning

confidence: 99%

“…Pedro Rangel Henriques has supervised Ph.D. (11), and M.Sc. (13) thesis, and more than 50 graduating trainingships/projects, in the areas of: language processing (textual and visual), and structured document processing; code analysis, program visulaization/animation and program comprehension; knowledge discovery from databases, data-mining, and data-cleaning. He is co-author of the "XML & XSL: da teoria a pr ática" book, published by FCA in 2002; and has published 3 chapters in books, and 20 journal papers.…”

mentioning

confidence: 99%

Domain-specific language for coordination patterns

2011

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The integration and composition of software systems requires a good architectural design phase to speed up communications between (remote) components. However, during implementation phase, the code to coordinate such components often ends up mixed in the main business code. This leads to maintenance problems, raising the need for, on the one hand, separating the coordination code from the business code, and on the other hand, providing mechanisms for analysis and comprehension of the architectural decisions once made. In this context our aim is at developing a domain-specific language, CoordL, to describe typical coordination patterns. From our point of view, coordination patterns are abstractions, in a graph form, over the composition of coordination statements from the system code. These patterns would allow us to identify, by means of pattern-based graph search strategies, the code responsible for the coordination of the several components in a system. The recovering and separation of the architectural decisions for a better comprehension of the software is the main purpose of this pattern language.

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“…Here, we discuss about the new emerging means of communication and interaction is computer vision. Since computer vision techniques are being used tremendously in many areas of surveillance systems, face recognition [2,19], crowd tracking [15,16], detecting, recognizing and analysis of activities [17,18] and events.…”

Section: Introductionmentioning

confidence: 99%

Link Mining: A Computer Vision and Pattern Mining Approach

Mishra¹,

Nandi²

2013

IJCA

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This work addresses the important problem of discovery and analysis of social networks and link between frequent people observed from surveillance video footage where large amount of video data is collected routinely. A computer vision approach has been applied for detecting and extracting people within a group using HAAR classifier. This technique allows us to recognizing people by doing similarity matching between training faces and unknown detected face image. Therefore it is required to obtain high resolution face images of people in order to extract intrinsic feature information of facial images used in detection of person's faces. We present a novel frequent pattern mining based approach in the domain of frequent person detection i.e. apriori to solve frequent association problem between social networks obtained from low level task of face recognition. Our approach is illustrated with promising results from a fully integrated camera system.

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Discovery, analysis and monitoring of hidden social networks and their evolution

Cited by 11 publications

References 9 publications

Detecting overlapped functional clusters in resting state fMRI with Connected Iterative Scan: A graph theory based clustering algorithm

Detecting overlapped functional clusters in resting state fMRI with Connected Iterative Scan: A graph theory based clustering algorithm

Domain-specific language for coordination patterns

Link Mining: A Computer Vision and Pattern Mining Approach

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