215 research outputs found
Defining and Evaluating Network Communities based on Ground-truth
Nodes in real-world networks organize into densely linked communities where
edges appear with high concentration among the members of the community.
Identifying such communities of nodes has proven to be a challenging task
mainly due to a plethora of definitions of a community, intractability of
algorithms, issues with evaluation and the lack of a reliable gold-standard
ground-truth.
In this paper we study a set of 230 large real-world social, collaboration
and information networks where nodes explicitly state their group memberships.
For example, in social networks nodes explicitly join various interest based
social groups. We use such groups to define a reliable and robust notion of
ground-truth communities. We then propose a methodology which allows us to
compare and quantitatively evaluate how different structural definitions of
network communities correspond to ground-truth communities. We choose 13
commonly used structural definitions of network communities and examine their
sensitivity, robustness and performance in identifying the ground-truth. We
show that the 13 structural definitions are heavily correlated and naturally
group into four classes. We find that two of these definitions, Conductance and
Triad-participation-ratio, consistently give the best performance in
identifying ground-truth communities. We also investigate a task of detecting
communities given a single seed node. We extend the local spectral clustering
algorithm into a heuristic parameter-free community detection method that
easily scales to networks with more than hundred million nodes. The proposed
method achieves 30% relative improvement over current local clustering methods.Comment: Proceedings of 2012 IEEE International Conference on Data Mining
(ICDM), 201
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
Detecting Cohesive and 2-mode Communities in Directed and Undirected Networks
Networks are a general language for representing relational information among
objects. An effective way to model, reason about, and summarize networks, is to
discover sets of nodes with common connectivity patterns. Such sets are
commonly referred to as network communities. Research on network community
detection has predominantly focused on identifying communities of densely
connected nodes in undirected networks.
In this paper we develop a novel overlapping community detection method that
scales to networks of millions of nodes and edges and advances research along
two dimensions: the connectivity structure of communities, and the use of edge
directedness for community detection. First, we extend traditional definitions
of network communities by building on the observation that nodes can be densely
interlinked in two different ways: In cohesive communities nodes link to each
other, while in 2-mode communities nodes link in a bipartite fashion, where
links predominate between the two partitions rather than inside them. Our
method successfully detects both 2-mode as well as cohesive communities, that
may also overlap or be hierarchically nested. Second, while most existing
community detection methods treat directed edges as though they were
undirected, our method accounts for edge directions and is able to identify
novel and meaningful community structures in both directed and undirected
networks, using data from social, biological, and ecological domains.Comment: Published in the proceedings of WSDM '1
Salience and Market-aware Skill Extraction for Job Targeting
At LinkedIn, we want to create economic opportunity for everyone in the
global workforce. To make this happen, LinkedIn offers a reactive Job Search
system, and a proactive Jobs You May Be Interested In (JYMBII) system to match
the best candidates with their dream jobs. One of the most challenging tasks
for developing these systems is to properly extract important skill entities
from job postings and then target members with matched attributes. In this
work, we show that the commonly used text-based \emph{salience and
market-agnostic} skill extraction approach is sub-optimal because it only
considers skill mention and ignores the salient level of a skill and its market
dynamics, i.e., the market supply and demand influence on the importance of
skills. To address the above drawbacks, we present \model, our deployed
\emph{salience and market-aware} skill extraction system. The proposed \model
~shows promising results in improving the online performance of job
recommendation (JYMBII) ( job apply) and skill suggestions for job
posters ( suggestion rejection rate). Lastly, we present case studies to
show interesting insights that contrast traditional skill recognition method
and the proposed \model~from occupation, industry, country, and individual
skill levels. Based on the above promising results, we deployed the \model
~online to extract job targeting skills for all M job postings served at
LinkedIn.Comment: 9 pages, to appear in KDD202
Attenuation correction for brain PET imaging using deep neural network based on dixon and ZTE MR images
Positron Emission Tomography (PET) is a functional imaging modality widely
used in neuroscience studies. To obtain meaningful quantitative results from
PET images, attenuation correction is necessary during image reconstruction.
For PET/MR hybrid systems, PET attenuation is challenging as Magnetic Resonance
(MR) images do not reflect attenuation coefficients directly. To address this
issue, we present deep neural network methods to derive the continuous
attenuation coefficients for brain PET imaging from MR images. With only Dixon
MR images as the network input, the existing U-net structure was adopted and
analysis using forty patient data sets shows it is superior than other Dixon
based methods. When both Dixon and zero echo time (ZTE) images are available,
we have proposed a modified U-net structure, named GroupU-net, to efficiently
make use of both Dixon and ZTE information through group convolution modules
when the network goes deeper. Quantitative analysis based on fourteen real
patient data sets demonstrates that both network approaches can perform better
than the standard methods, and the proposed network structure can further
reduce the PET quantification error compared to the U-net structure.Comment: 15 pages, 12 figure
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Effect of Time-of-Flight and Regularized Reconstructions on Quantitative Measurements and Qualitative Assessments in Newly Diagnosed Prostate Cancer With 18F-Fluorocholine Dual Time Point PET/MRI.
Recent technical advances in positron emission tomography/magnetic resonance imaging (PET/MRI) technology allow much improved time-of-flight (TOF) and regularized iterative PET reconstruction regularized iterative reconstruction (RIR) algorithms. We evaluated the effect of TOF and RIR on standardized uptake values (maximum and peak SUV [SUVmax and SUVpeak]) and their metabolic tumor volume dependencies and visual image quality for 18F-fluorocholine PET/MRI in patients with newly diagnosed prostate cancer. Fourteen patients were administered with 3 MBq/kg of 18F-fluorocholine and scanned dynamically for 30 minutes. Positron emission tomography images were divided to early and late time points (1-6 minutes summed and 7-30 minutes summed). The values of the different SUVs were documented for dominant PET-avid lesions, and metabolic tumor volume was estimated using a 50% isocontour and SUV threshold of 2.5. Image quality was assessed via visual acuity scoring (VAS). We found that incorporation of TOF or RIR increased lesion SUVs. The lesion to background ratio was not improved by TOF reconstruction, while RIR improved the lesion to background ratio significantly ( P < .05). The values of the different VAS were all significantly higher ( P < .05) for RIR images over TOF, RIR over non-TOF, and TOF over non-TOF. In conclusion, our data indicate that TOF or RIR should be incorporated into current protocols when available
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