606,480 research outputs found
Ubiquitousness of link-density and link-pattern communities in real-world networks
Community structure appears to be an intrinsic property of many complex
real-world networks. However, recent work shows that real-world networks reveal
even more sophisticated modules than classical cohesive (link-density)
communities. In particular, networks can also be naturally partitioned
according to similar patterns of connectedness among the nodes, revealing
link-pattern communities. We here propose a propagation based algorithm that
can extract both link-density and link-pattern communities, without any prior
knowledge of the true structure. The algorithm was first validated on different
classes of synthetic benchmark networks with community structure, and also on
random networks. We have further applied the algorithm to different social,
information, technological and biological networks, where it indeed reveals
meaningful (composites of) link-density and link-pattern communities. The
results thus seem to imply that, similarly as link-density counterparts,
link-pattern communities appear ubiquitous in nature and design
Impact of community structure on information transfer
The observation that real complex networks have internal structure has important implication for dynamic processes occurring on such topologies. Here we investigate the impact of community structure on a model of information transfer able to deal with both search and congestion simultaneously. We show that networks with fuzzy community structure are more efficient in terms of packet delivery than those with pronounced community structure. We also propose an alternative packet routing algorithm which takes advantage of the knowledge of communities to improve information transfer and show that in the context of the model an intermediate level of community structure is optimal. Finally, we show that in a hierarchical network setting, providing knowledge of communities at the level of highest modularity will improve network capacity by the largest amount
Map equation for link community
Community structure exists in many real-world networks and has been reported
being related to several functional properties of the networks. The
conventional approach was partitioning nodes into communities, while some
recent studies start partitioning links instead of nodes to find overlapping
communities of nodes efficiently. We extended the map equation method, which
was originally developed for node communities, to find link communities in
networks. This method is tested on various kinds of networks and compared with
the metadata of the networks, and the results show that our method can identify
the overlapping role of nodes effectively. The advantage of this method is that
the node community scheme and link community scheme can be compared
quantitatively by measuring the unknown information left in the networks
besides the community structure. It can be used to decide quantitatively
whether or not the link community scheme should be used instead of the node
community scheme. Furthermore, this method can be easily extended to the
directed and weighted networks since it is based on the random walk.Comment: 9 pages,5 figure
Community Detection in Networks with Node Attributes
Community detection algorithms are fundamental tools that allow us to uncover
organizational principles in networks. When detecting communities, there are
two possible sources of information one can use: the network structure, and the
features and attributes of nodes. Even though communities form around nodes
that have common edges and common attributes, typically, algorithms have only
focused on one of these two data modalities: community detection algorithms
traditionally focus only on the network structure, while clustering algorithms
mostly consider only node attributes. In this paper, we develop Communities
from Edge Structure and Node Attributes (CESNA), an accurate and scalable
algorithm for detecting overlapping communities in networks with node
attributes. CESNA statistically models the interaction between the network
structure and the node attributes, which leads to more accurate community
detection as well as improved robustness in the presence of noise in the
network structure. CESNA has a linear runtime in the network size and is able
to process networks an order of magnitude larger than comparable approaches.
Last, CESNA also helps with the interpretation of detected communities by
finding relevant node attributes for each community.Comment: Published in the proceedings of IEEE ICDM '1
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