114,008 research outputs found
Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification
Mining discriminative subgraph patterns from graph data has attracted great
interest in recent years. It has a wide variety of applications in disease
diagnosis, neuroimaging, etc. Most research on subgraph mining focuses on the
graph representation alone. However, in many real-world applications, the side
information is available along with the graph data. For example, for
neurological disorder identification, in addition to the brain networks derived
from neuroimaging data, hundreds of clinical, immunologic, serologic and
cognitive measures may also be documented for each subject. These measures
compose multiple side views encoding a tremendous amount of supplemental
information for diagnostic purposes, yet are often ignored. In this paper, we
study the problem of discriminative subgraph selection using multiple side
views and propose a novel solution to find an optimal set of subgraph features
for graph classification by exploring a plurality of side views. We derive a
feature evaluation criterion, named gSide, to estimate the usefulness of
subgraph patterns based upon side views. Then we develop a branch-and-bound
algorithm, called gMSV, to efficiently search for optimal subgraph features by
integrating the subgraph mining process and the procedure of discriminative
feature selection. Empirical studies on graph classification tasks for
neurological disorders using brain networks demonstrate that subgraph patterns
selected by the multi-side-view guided subgraph selection approach can
effectively boost graph classification performances and are relevant to disease
diagnosis.Comment: in Proceedings of IEEE International Conference on Data Mining (ICDM)
201
Spotting the diffusion of New Psychoactive Substances over the Internet
Online availability and diffusion of New Psychoactive Substances (NPS)
represent an emerging threat to healthcare systems. In this work, we analyse
drugs forums, online shops, and Twitter. By mining the data from these sources,
it is possible to understand the dynamics of drugs diffusion and their
endorsement, as well as timely detecting new substances. We propose a set of
visual analytics tools to support analysts in tackling NPS spreading and
provide a better insight about drugs market and analysis
Cascading Behavior in Large Blog Graphs
How do blogs cite and influence each other? How do such links evolve? Does
the popularity of old blog posts drop exponentially with time? These are some
of the questions that we address in this work. Our goal is to build a model
that generates realistic cascades, so that it can help us with link prediction
and outlier detection.
Blogs (weblogs) have become an important medium of information because of
their timely publication, ease of use, and wide availability. In fact, they
often make headlines, by discussing and discovering evidence about political
events and facts. Often blogs link to one another, creating a publicly
available record of how information and influence spreads through an underlying
social network. Aggregating links from several blog posts creates a directed
graph which we analyze to discover the patterns of information propagation in
blogspace, and thereby understand the underlying social network. Not only are
blogs interesting on their own merit, but our analysis also sheds light on how
rumors, viruses, and ideas propagate over social and computer networks.
Here we report some surprising findings of the blog linking and information
propagation structure, after we analyzed one of the largest available datasets,
with 45,000 blogs and ~ 2.2 million blog-postings. Our analysis also sheds
light on how rumors, viruses, and ideas propagate over social and computer
networks. We also present a simple model that mimics the spread of information
on the blogosphere, and produces information cascades very similar to those
found in real life
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