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University of Ljubljana
Faculty of Arts
Department of Ethnology
and Cultural Anthropology
Aškerčeva 2
SI-1000 Ljubljana
T: +386 (1) 241 15 20
Email contact:
eika@ff.uni-lj.si


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News - January

31.1.2012

Secretary´s office

Due to leave the secretary´s office will be closed from February 6 till February 10 2012.

Thank you for understanding!

18.1.2012

ANTHROPOLOGY OF COMPLEX SYSTEMS: INDIVIDUAL CONSULTATIONS
Individual consultations for the course Anthropology of Complex Systems will take place on Thursday, January 19th 2012, from 10am to 12am in room 307.
Dr Dan Podjed

5.1.2012

ANTHROPOLOGY OF COMPLEX SYSTEMS: LECTURE OF DR JURE LESKOVEC

On Thursday, January 5th 2012, we will attend a lecture of Dr Jure Leskovec (Stanford University) about networks and formation of communities in nature and society. The lecture will take place at 1pm in Room 239 (Large Lecture Room) of the Jozef Stefan Institute (Jamova cesta 39, Ljubljana). The institute is near the Department of Ethnology and Cultural Anthropology, so we will take a walk there. We will meet in front of the department at 12:30pm and leave at 12:40pm.

Dr Dan Podjed

ABOUT JURE LESKOVEC
Dr Jure Leskovec is an assistant professor of Computer Science at the Stanford University and one of central researchers of online social networks. He focuses on data mining and modeling large social and information networks, their evolution, and diffusion of information and influence over them. He uses sampled data to analyse online human behaviour and to predict future outcomes.


LECTURE
Networks, communities and the ground-truth
The Web, society, information, cells and brain can all be represented and studied as complex networks of interactions. Nodes in such networks tend to organize into clusters and communities, which represent the fundamental structures for understanding the organization of complex systems. Even though detection of network communities is of significant importance for computer science, sociology and biology, our understanding of the community structure of large networks remains limited.
We study a set of more than 200 large networks with the goal to understand and identify communities in networks. We challenge the conventional view of network community structure and show that it is not exhibited by the large real-world networks. We then present a new conceptual model of network community structure, which reliably captures the overall structure of networks and accurately identifies the overlapping nature of network communities.
(This is joint work with Jaewon Yang.)

 

 

 

 

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