TY - CHAP
T1 - Social group dynamics in networks
AU - Palla, Gergely
AU - Pollner, Péter
AU - Barabási, Albert László
AU - Vicsek, Tamás
PY - 2009
Y1 - 2009
N2 - The rich set of interactions between individuals in the society results in complex community structure, capturing highly connected circles of friends, families, or professional cliques in a social network. Due to the frequent changes in the activity and communication patterns of individuals, the associated social and communication network is subject to constant evolution. The cohesive groups of people in such networks can grow by recruiting new members, or contract by loosing members; two (or more) groups may merge into a single community, while a large enough social group can split into several smaller ones; new communities are born and old ones may disappear. We discuss a new algorithm based on a clique percolation technique, that allows to investigate in detail the time dependence of communities on a large scale and as such, to uncover basic relationships of the statistical features of community evolution. According to the results, the behaviour of smaller collaborative or friendship circles and larger communities, e.g., institutions show significant differences. Social groups containing only a few members persist longer on average when the fluctuations of the members is small. In contrast, we find that the condition for stability for large communities is continuous changes in their membership, allowing for the possibility that after some time practically all members are exchanged.
AB - The rich set of interactions between individuals in the society results in complex community structure, capturing highly connected circles of friends, families, or professional cliques in a social network. Due to the frequent changes in the activity and communication patterns of individuals, the associated social and communication network is subject to constant evolution. The cohesive groups of people in such networks can grow by recruiting new members, or contract by loosing members; two (or more) groups may merge into a single community, while a large enough social group can split into several smaller ones; new communities are born and old ones may disappear. We discuss a new algorithm based on a clique percolation technique, that allows to investigate in detail the time dependence of communities on a large scale and as such, to uncover basic relationships of the statistical features of community evolution. According to the results, the behaviour of smaller collaborative or friendship circles and larger communities, e.g., institutions show significant differences. Social groups containing only a few members persist longer on average when the fluctuations of the members is small. In contrast, we find that the condition for stability for large communities is continuous changes in their membership, allowing for the possibility that after some time practically all members are exchanged.
UR - http://www.scopus.com/inward/record.url?scp=69549086775&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-01284-6_2
DO - 10.1007/978-3-642-01284-6_2
M3 - Chapter
AN - SCOPUS:69549086775
SN - 9783642012839
T3 - Understanding Complex Systems
SP - 11
EP - 38
BT - Adaptive Networks
PB - Springer Verlag
ER -