Title :
Community Evolution in Dynamic Social Networks -- Challenges and Problems
Author :
Takaffoli, Mansoureh
Author_Institution :
Dept. of Comput. Sci., Univ. of Alberta, Edmonton, AB, Canada
Abstract :
Information networks that describe the relationship between individuals are called social networks and are usually modeled by a graph structure. Social network analysis is the study of these information networks which leads to uncover patterns of interaction among the entities. Most social networks are dynamic, and studying the evolution of these networks over time could provide insight into the changes that occurred in the iteration patterns and also the future trends of the networks. Furthermore, in a dynamic scenario, communities, which are groups of densely interconnected nodes, are affected by changes in the underlying population. The analysis of communities and their evolutions can help determine the characteristics and structural properties of the network. Here, we provide a brief overview of the existing research in the area of dynamic social network analysis, their limitations, and the challenges that are exists for further analysis.
Keywords :
graph theory; iterative methods; social networking (online); community evolution; dynamic social network analysis; graph structure; information networks; iteration pattern; Accuracy; Communities; Data mining; Heuristic algorithms; Measurement; Predictive models; Social network services; Community evolution; Community mining; Dynamic social network; Evolutionary analysis;
Conference_Titel :
Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
978-1-4673-0005-6
DOI :
10.1109/ICDMW.2011.52