A framework for analysis of dynamic social networks

Berger-Wolf, Tanya Y.; Saia, Jared

doi:10.1145/1150402.1150462

Cited by 201 publications

(97 citation statements)

References 24 publications

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“…This correlates to results from comparing clustering in social networks to that of non-human networks where interactions are defined more by network topology than other affinity measures; for instance, the Internet topology compared to the Live-Journal community (Kumar, Novak, Raghavan & Tomkins 2004). Centrality measures are valuable in quantifying network topologies; evaluating these metrics on dynamic and evolving networks is an open research question and the authors leave centrality evaluation to future work (Berger-Wolf & Saia 2006). Quantitative analysis of DynSNIC's infection dynamic capabilities, in conjunction with health policies and interventions strategies, is provided in the following case study.…”

Section: Resultsmentioning

confidence: 55%

Dynamic intimate contact social networks and epidemic interventions

Corley

Mikler

Cook

et al. 2008

IJFIPM

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Sexually transmitted diseases and infections are, by definition, transferred among intimate social settings. Although the circumstances under which these social settings are established and maintained may vary, the common prerequisite remains an intimate level of social atmosphere. For this reason, the development of sexually transmitted disease mathematical and computational models must utilize dynamic and evolving social network simulation. This paper presents DynSNIC (Dynamic Social Network of Intimate Contacts), a computational simulator created to embody the intimate dynamic and evolving social networks related to the transmission of sexually transmitted diseases and infections. DynSNIC's utilization by health professionals will facilitate evaluation of targeted intervention strategies and public health policies.

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Section: Resultsmentioning

confidence: 55%

Dynamic intimate contact social networks and epidemic interventions

Corley

Mikler

Cook

et al. 2008

IJFIPM

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“…The traditional static social analysis method has a significant disadvantage that it ignores the important factors that cause network status change [7], which results in information deficiency. For example, this method ignores the connections between different individuals, the mutual effects between related individuals and time-related change trend [8]. Second method: use the graph partitioning method, i.e.…”

Section: Related Workmentioning

confidence: 99%

Study of Recognition Algorithm for Core Node in KAD Network Based on BP Model

Li¹,

Tang²,

Yang³

2016

IJFCC

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Abstract-In view of the core node recognition in the KAD (Kademlia), a model based on BP is presented to determine whether a node is core node in real time. According to the result of the measurement for KAD, some attribute characteristics of core nodes in the network have been extracted and normalized to obtain an attribute set with higher separable degree. An algorithm in MatLab is designed to train the BP network until the results are limited in a predetermined error range. In addition, the trained BP model is adopted to determine the tested nodes, and the results of the experiment show that the method can judge degrees of importance of nodes in real time, and the recognition accuracy rate is up to about 70%.

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“…Social network analysis is used to understand the pattern of interaction caused by social dynamics and events [8,9,10,11,12,13]. One special kind of network is known as social network and has been studying for long time [3,4,5,6].…”

Section: Social Networkmentioning

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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A framework for analysis of dynamic social networks

Cited by 201 publications

References 24 publications

Dynamic intimate contact social networks and epidemic interventions

Dynamic intimate contact social networks and epidemic interventions

Study of Recognition Algorithm for Core Node in KAD Network Based on BP Model

Link Mining: A Computer Vision and Pattern Mining Approach

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