23,617 research outputs found
Understanding Co-evolution in Large Multi-relational Social Networks
Understanding dynamics of evolution in large social networks is an important
problem. In this paper, we characterize evolution in large multi-relational
social networks. The proliferation of online media such as Twitter, Facebook,
Orkut and MMORPGs\footnote{Massively Multi-player Online Role Playing Games}
have created social networking data at an unprecedented scale. Sony's Everquest
2 is one such example. We used game multi-relational networks to reveal the
dynamics of evolution in a multi-relational setting by macroscopic study of the
game network. Macroscopic analysis involves fragmenting the network into
smaller portions for studying the dynamics within these sub-networks, referred
to as `communities'. From an evolutionary perspective of multi-relational
network analysis, we have made the following contributions. Specifically, we
formulated and analyzed various metrics to capture evolutionary properties of
networks. We find that co-evolution rates in trust based `communities' are
approximately higher than the trade based `communities'. We also find
that the trust and trade connections within the `communities' reduce as their
size increases. Finally, we study the interrelation between the dynamics of
trade and trust within `communities' and find interesting results about the
precursor relationship between the trade and the trust dynamics within the
`communities'
An efficient and principled method for detecting communities in networks
A fundamental problem in the analysis of network data is the detection of
network communities, groups of densely interconnected nodes, which may be
overlapping or disjoint. Here we describe a method for finding overlapping
communities based on a principled statistical approach using generative network
models. We show how the method can be implemented using a fast, closed-form
expectation-maximization algorithm that allows us to analyze networks of
millions of nodes in reasonable running times. We test the method both on
real-world networks and on synthetic benchmarks and find that it gives results
competitive with previous methods. We also show that the same approach can be
used to extract nonoverlapping community divisions via a relaxation method, and
demonstrate that the algorithm is competitively fast and accurate for the
nonoverlapping problem.Comment: 14 pages, 5 figures, 1 tabl
- …