MutatedSocioAgentSim (MSAS): semisupervised modelling of multiagent   simulation to predict and detect the mutation in a camouflaged social   network

Karthika SUBBARAJ; Bose SUNDAN

Makale detayları

MutatedSocioAgentSim (MSAS): semisupervised modelling of multiagent simulation to predict and detect the mutation in a camouflaged social network

Yazarlar:

Karthika SUBBARAJ | Bose SUNDAN

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2018 Vol.26 Issue 2, pp.961-973

Belge Tipi:

Makale

Yayın Tarihi:

3/2018

Belge Dili:

Türkçe

Document ID (Org No.):

2000735304

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

A social network is a networked structure formed by a set   of agents/actors. It describes their interrelationships that facilitate the   exchange and flow of resources and information. A camouflaged social network   is one such community that influences the underlying structure and the   profile of the agents, to cause mutation. The proposed MSAM is a novel   system that simulates a multiagent network whose community structure is   analyzed to identify the critical agents by studying the mutations caused   due to attachment and detachment of agents. The isolation of the tagged   agents will demonstrate disruption of information flow, which leads to the   dismantling of the camouflaged community and giving scope for a predictive   study about near future reconciliation. The proposed system simulates the   9/11 covert network based on the belief matrix and uses the novel   density-based link prediction and suite of fragmentation algorithms for   predictive community analysis. MSAM is claimed to be an intelligent system   as agents perceive the knowledge from the dynamic environment through the   belief matrix and further co-evolve as a community upon which semisupervised   methodologies are used to predict the critical agents causing serious   mutation.

Makale detayları

MutatedSocioAgentSim (MSAS): semisupervised modelling of multiagent simulation to predict and detect the mutation in a camouflaged social network

Yazarlar: Karthika SUBBARAJ | Bose SUNDAN

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2018 Vol.26 Issue 2, pp.961-973

Belge Tipi: Makale

Yayın Tarihi: 3/2018

Belge Dili: Türkçe

Document ID (Org No.): 2000735304

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

Karthika SUBBARAJ | Bose SUNDAN

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2018 Vol.26 Issue 2, pp.961-973

Belge Tipi:

Makale

Yayın Tarihi:

3/2018

Belge Dili:

Türkçe

Document ID (Org No.):

2000735304

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632