Analysis of Social Networks Extracted from Log Files

Slaninová, Kateřina; Martinovič, Jan; Dráždilová, Pavla; Obadi, Gamila; Snåšel, Václav

doi:10.1007/978-1-4419-7142-5_6

Cited by 13 publications

(6 citation statements)

References 77 publications

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“…In this paper, we have used the UCINET 6, a software package useful for Social Network analysis. In the literature of SNA, there are many metrics proposed to discover the characteristics of a Social Networks, like degree/size, density, different types of centralities, clustering coefficient, path analysis, flow, cohesion and influence, and other essential information which is obtained by various types of analysis [30]. In this section, for our analysis, we use the following metrics:…”

Section: Social Network Analysismentioning

confidence: 99%

TreeNet Analysis of Human Stress Behavior using Socio-Mobile Data

Padmaja

Prasad²,

Sunitha³

2016

IJEECS

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<p>Human behavior is essentially social and humans start their daily routines by interacting with others. There are many forms of social interactions and we have used mobile phone based social interaction features and social surveys for finding human stress behavior. For this, we gathered mobile phone call logs data set containing 111444 voice calls of 131 adult members of a living community for a period of more than 5 months. And we identified that top 5 social network measures like hierarchy, density, farness, reachability and eigenvector of individuals have profound influence on individuals stress levels in a social network. If an ego lies in the shortest path of all other alters then the ego receives more information and hence is more stressed. In this paper, we have used TreeNet machine learning algorithm for its speed and immune to outliers. We have tested our results with another Random Forest classifier as well and yet, we found TreeNet to be more efficient. This research can be of vital importance to economists, professionals, analysts, and policy makers.<em></em></p>

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Section: Social Network Analysismentioning

confidence: 99%

TreeNet Analysis of Human Stress Behavior using Socio-Mobile Data

Padmaja

Prasad²,

Sunitha³

2016

IJEECS

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“…Clique percolation is presented as a means for the analysis of network dynamics under collaboration in the seminal paper [8] with a result that stability of large cliques is related to their dynamic change, while small cliques have to remain unchanged. Follow-up studies identified communities from evaluation of log files [9] or general evaluation of graphs [10]. The focus on procedural collaboration beyond identification of collaboration can be found in other works like collaborative tagging [11] with regard to the modeling of folksonomy, and collaborative filtering, e.g.…”

Section: Introductionmentioning

confidence: 99%

A Relational Approach to Collaborative Fairness

Köppen

Verschae

Tsuru

2012

2012 Fourth International Conference on Intelligent Networking and Collaborative Systems

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In the relational approach to fairness, fairness is considered as a social choice that coincides with the maximum set of a fairness relation. Here we consider the application of this approach to achieve general fairness in collaborative systems. The approach is based on posing additional conditions on the fairness relation based on representation of collaboration among agents by a social graph, and various social types of agents. The relation can be used for the formal analysis of various collaboration scenarios by justifying tractable sizes of maximum sets of corresponding collaborative fairness relations. As an example result, the introduction of cliques of larger size appears to be in favor of achievability of collaborate fairness.

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“…In the literature of SNA, there are many metrics proposed to discover the characteristics of a social networks, like degree/size, density, different types of centralities, clustering coefficient, path analysis, flow, cohesion and influence, and other essential information which is obtained by various types of analysis [38]. In this section, for our analysis, we use the following metrics:…”

Section: Social Network Analysismentioning

confidence: 99%