A Társadalomtudományi Kutatóközpont (MTA Kiváló Kutatóhely) 

Szociológiai Intézete 

tisztelettel meghívja 116. Jour Fixe eseményére

 

  Brys Zoltán: Estimating the effect of inaccurate edge information on centrality ranking order - case study of a university classroom network

 

Előadó: Brys Zoltán (TK SZI)

Társszerző: Galántai Júlia (TK SZI) 

Hozzászólók: László Hajdu (University of Primorska), András Bóta (Lulea University of Technology, and Bence Kollányi (Centre for Social Sciences, RECENS)

Időpont: 2023. május 25. csütörtök 13:00 

Helyszín: Az eseményt hibrid formában tartjuk meg.

Személyesen: Szociológiai Intézet 1097 Budapest Tóth Kálmán utca 4.;  B.1.15 tárgyaló

 

Online: Zoom link: 

https://us06web.zoom.us/j/87213752214?pwd=OXkrNFBDV1lVWG95eGEycVRhRUFJdz09

Meeting ID: 872 1375 2214

Passcode: 104159

 

Absztrakt:

Collecting complete data on classroom networks is rarely possible. Absences, recall bias, response bias, compliance bias, and mistyping all result in incomplete input data. These noises affect the reliability of various centrality measures. Earlier studies highlighted the relative stability of degree centrality and eigenvector centrality and showed the instability of closeness centrality and betweenness centrality. Also it is known that the topology of the graphs also influences the robustness of centrality measures.

As in classroom network studies researchers typically know the number of nodes, we argue that specific studies are required which only estimate the effect of edge noise in classroom networks.

In this case study we used non-surveyed data of a university classroom (Fire et al. 2012) and we tested the sensitivity of various centrality measures against random edge deletion and random edge addition.  The proportion of discordant pairs between the original and sampled ranking was used as sensitivity/robustness measure. Using various exponential and sigmoid approximations we estimated the robustness of centrality measures against random edge deletion and to random edge addition.

Albeit our findings are limited to analyzed graph, our results suggest that edge information might have stronger effect on centrality measures of classroom networks than we thought.

 

Az esemény angol nyelvű!