17,005 research outputs found
Revisiting Interval Graphs for Network Science
The vertices of an interval graph represent intervals over a real line where
overlapping intervals denote that their corresponding vertices are adjacent.
This implies that the vertices are measurable by a metric and there exists a
linear structure in the system. The generalization is an embedding of a graph
onto a multi-dimensional Euclidean space and it was used by scientists to study
the multi-relational complexity of ecology. However the research went out of
fashion in the 1980s and was not revisited when Network Science recently
expressed interests with multi-relational networks known as multiplexes. This
paper studies interval graphs from the perspective of Network Science
DEMON: a Local-First Discovery Method for Overlapping Communities
Community discovery in complex networks is an interesting problem with a
number of applications, especially in the knowledge extraction task in social
and information networks. However, many large networks often lack a particular
community organization at a global level. In these cases, traditional graph
partitioning algorithms fail to let the latent knowledge embedded in modular
structure emerge, because they impose a top-down global view of a network. We
propose here a simple local-first approach to community discovery, able to
unveil the modular organization of real complex networks. This is achieved by
democratically letting each node vote for the communities it sees surrounding
it in its limited view of the global system, i.e. its ego neighborhood, using a
label propagation algorithm; finally, the local communities are merged into a
global collection. We tested this intuition against the state-of-the-art
overlapping and non-overlapping community discovery methods, and found that our
new method clearly outperforms the others in the quality of the obtained
communities, evaluated by using the extracted communities to predict the
metadata about the nodes of several real world networks. We also show how our
method is deterministic, fully incremental, and has a limited time complexity,
so that it can be used on web-scale real networks.Comment: 9 pages; Proceedings of the 18th ACM SIGKDD International Conference
on Knowledge Discovery and Data Mining, Beijing, China, August 12-16, 201
Principled Multilayer Network Embedding
Multilayer network analysis has become a vital tool for understanding
different relationships and their interactions in a complex system, where each
layer in a multilayer network depicts the topological structure of a group of
nodes corresponding to a particular relationship. The interactions among
different layers imply how the interplay of different relations on the topology
of each layer. For a single-layer network, network embedding methods have been
proposed to project the nodes in a network into a continuous vector space with
a relatively small number of dimensions, where the space embeds the social
representations among nodes. These algorithms have been proved to have a better
performance on a variety of regular graph analysis tasks, such as link
prediction, or multi-label classification. In this paper, by extending a
standard graph mining into multilayer network, we have proposed three methods
("network aggregation," "results aggregation" and "layer co-analysis") to
project a multilayer network into a continuous vector space. From the
evaluation, we have proved that comparing with regular link prediction methods,
"layer co-analysis" achieved the best performance on most of the datasets,
while "network aggregation" and "results aggregation" also have better
performance than regular link prediction methods
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