42,174 research outputs found
Socially Constrained Structural Learning for Groups Detection in Crowd
Modern crowd theories agree that collective behavior is the result of the
underlying interactions among small groups of individuals. In this work, we
propose a novel algorithm for detecting social groups in crowds by means of a
Correlation Clustering procedure on people trajectories. The affinity between
crowd members is learned through an online formulation of the Structural SVM
framework and a set of specifically designed features characterizing both their
physical and social identity, inspired by Proxemic theory, Granger causality,
DTW and Heat-maps. To adhere to sociological observations, we introduce a loss
function (G-MITRE) able to deal with the complexity of evaluating group
detection performances. We show our algorithm achieves state-of-the-art results
when relying on both ground truth trajectories and tracklets previously
extracted by available detector/tracker systems
Batch kernel SOM and related Laplacian methods for social network analysis
Large graphs are natural mathematical models for describing the structure of
the data in a wide variety of fields, such as web mining, social networks,
information retrieval, biological networks, etc. For all these applications,
automatic tools are required to get a synthetic view of the graph and to reach
a good understanding of the underlying problem. In particular, discovering
groups of tightly connected vertices and understanding the relations between
those groups is very important in practice. This paper shows how a kernel
version of the batch Self Organizing Map can be used to achieve these goals via
kernels derived from the Laplacian matrix of the graph, especially when it is
used in conjunction with more classical methods based on the spectral analysis
of the graph. The proposed method is used to explore the structure of a
medieval social network modeled through a weighted graph that has been directly
built from a large corpus of agrarian contracts
Construction of embedded fMRI resting state functional connectivity networks using manifold learning
We construct embedded functional connectivity networks (FCN) from benchmark
resting-state functional magnetic resonance imaging (rsfMRI) data acquired from
patients with schizophrenia and healthy controls based on linear and nonlinear
manifold learning algorithms, namely, Multidimensional Scaling (MDS), Isometric
Feature Mapping (ISOMAP) and Diffusion Maps. Furthermore, based on key global
graph-theoretical properties of the embedded FCN, we compare their
classification potential using machine learning techniques. We also assess the
performance of two metrics that are widely used for the construction of FCN
from fMRI, namely the Euclidean distance and the lagged cross-correlation
metric. We show that the FCN constructed with Diffusion Maps and the lagged
cross-correlation metric outperform the other combinations
The Extraction of Community Structures from Publication Networks to Support Ethnographic Observations of Field Differences in Scientific Communication
The scientific community of researchers in a research specialty is an
important unit of analysis for understanding the field specific shaping of
scientific communication practices. These scientific communities are, however,
a challenging unit of analysis to capture and compare because they overlap,
have fuzzy boundaries, and evolve over time. We describe a network analytic
approach that reveals the complexities of these communities through examination
of their publication networks in combination with insights from ethnographic
field studies. We suggest that the structures revealed indicate overlapping
sub- communities within a research specialty and we provide evidence that they
differ in disciplinary orientation and research practices. By mapping the
community structures of scientific fields we aim to increase confidence about
the domain of validity of ethnographic observations as well as of collaborative
patterns extracted from publication networks thereby enabling the systematic
study of field differences. The network analytic methods presented include
methods to optimize the delineation of a bibliographic data set in order to
adequately represent a research specialty, and methods to extract community
structures from this data. We demonstrate the application of these methods in a
case study of two research specialties in the physical and chemical sciences.Comment: Accepted for publication in JASIS
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Integrative analysis of the inter-tumoral heterogeneity of triple-negative breast cancer.
Triple-negative breast cancers (TNBC) lack estrogen and progesterone receptors and HER2 amplification, and are resistant to therapies that target these receptors. Tumors from TNBC patients are heterogeneous based on genetic variations, tumor histology, and clinical outcomes. We used high throughput genomic data for TNBC patients (n = 137) from TCGA to characterize inter-tumor heterogeneity. Similarity network fusion (SNF)-based integrative clustering combining gene expression, miRNA expression, and copy number variation, revealed three distinct patient clusters. Integrating multiple types of data resulted in more distinct clusters than analyses with a single datatype. Whereas most TNBCs are classified by PAM50 as basal subtype, one of the clusters was enriched in the non-basal PAM50 subtypes, exhibited more aggressive clinical features and had a distinctive signature of oncogenic mutations, miRNAs and expressed genes. Our analyses provide a new classification scheme for TNBC based on multiple omics datasets and provide insight into molecular features that underlie TNBC heterogeneity
Mining a medieval social network by kernel SOM and related methods
This paper briefly presents several ways to understand the organization of a
large social network (several hundreds of persons). We compare approaches
coming from data mining for clustering the vertices of a graph (spectral
clustering, self-organizing algorithms. . .) and provide methods for
representing the graph from these analysis. All these methods are illustrated
on a medieval social network and the way they can help to understand its
organization is underlined
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