19,070 research outputs found
Community Structure Characterization
This entry discusses the problem of describing some communities identified in
a complex network of interest, in a way allowing to interpret them. We suppose
the community structure has already been detected through one of the many
methods proposed in the literature. The question is then to know how to extract
valuable information from this first result, in order to allow human
interpretation. This requires subsequent processing, which we describe in the
rest of this entry
Graph Summarization
The continuous and rapid growth of highly interconnected datasets, which are
both voluminous and complex, calls for the development of adequate processing
and analytical techniques. One method for condensing and simplifying such
datasets is graph summarization. It denotes a series of application-specific
algorithms designed to transform graphs into more compact representations while
preserving structural patterns, query answers, or specific property
distributions. As this problem is common to several areas studying graph
topologies, different approaches, such as clustering, compression, sampling, or
influence detection, have been proposed, primarily based on statistical and
optimization methods. The focus of our chapter is to pinpoint the main graph
summarization methods, but especially to focus on the most recent approaches
and novel research trends on this topic, not yet covered by previous surveys.Comment: To appear in the Encyclopedia of Big Data Technologie
Topological Feature Based Classification
There has been a lot of interest in developing algorithms to extract clusters
or communities from networks. This work proposes a method, based on
blockmodelling, for leveraging communities and other topological features for
use in a predictive classification task. Motivated by the issues faced by the
field of community detection and inspired by recent advances in Bayesian topic
modelling, the presented model automatically discovers topological features
relevant to a given classification task. In this way, rather than attempting to
identify some universal best set of clusters for an undefined goal, the aim is
to find the best set of clusters for a particular purpose.
Using this method, topological features can be validated and assessed within
a given context by their predictive performance.
The proposed model differs from other relational and semi-supervised learning
models as it identifies topological features to explain the classification
decision. In a demonstration on a number of real networks the predictive
capability of the topological features are shown to rival the performance of
content based relational learners. Additionally, the model is shown to
outperform graph-based semi-supervised methods on directed and approximately
bipartite networks.Comment: Awarded 3rd Best Student Paper at 14th International Conference on
Information Fusion 201
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