Paper Info
Title
Understanding community patterns in large attributed social networks.
Abstract
There is an inherent presence of communities in online social networks. These communities can be defined based on i) link structure or ii) the attributes of individuals. Attributes can indicate as interests in specific topics, like science-fiction books or romantic movies, or more in general their explicit affiliation to a group inside the network. In this paper, we analyze community structures as defined by how people are associated to third concepts like attributes. To understand the community patterns we analyze three large and one small social network datasets. Our analysis shows that, irrespective of the number of nodes for any particular interest in the network, at least 50% of the nodes are part of the same connected component in the graph induced by each interest. Another interesting result of our analysis is that the majority of sub-communities (50% or above) for any interest are separated by small hops (two to three) from each other.
Year
DOI
Venue
2015
10.1145/2808797.2809330
ASONAM
Keywords
Field
DocType
Community Patterns, Social Network Analysis, Attribute Based Community Analysis
Data science,Data mining,Social network,Computer science,Artificial intelligence,Dynamic network analysis,Graph,World Wide Web,Algorithm design,Social network analysis,Connected component,Knowledge engineering,Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
19
Authors
3
Name
Order
Citations
PageRank
Rajesh Sharma1327.77
Matteo Magnani242838.21
Danilo Montesi3585130.19