1,824 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
Sparse Learning over Infinite Subgraph Features
We present a supervised-learning algorithm from graph data (a set of graphs)
for arbitrary twice-differentiable loss functions and sparse linear models over
all possible subgraph features. To date, it has been shown that under all
possible subgraph features, several types of sparse learning, such as Adaboost,
LPBoost, LARS/LASSO, and sparse PLS regression, can be performed. Particularly
emphasis is placed on simultaneous learning of relevant features from an
infinite set of candidates. We first generalize techniques used in all these
preceding studies to derive an unifying bounding technique for arbitrary
separable functions. We then carefully use this bounding to make block
coordinate gradient descent feasible over infinite subgraph features, resulting
in a fast converging algorithm that can solve a wider class of sparse learning
problems over graph data. We also empirically study the differences from the
existing approaches in convergence property, selected subgraph features, and
search-space sizes. We further discuss several unnoticed issues in sparse
learning over all possible subgraph features.Comment: 42 pages, 24 figures, 4 table
Matched Filters for Noisy Induced Subgraph Detection
The problem of finding the vertex correspondence between two noisy graphs
with different number of vertices where the smaller graph is still large has
many applications in social networks, neuroscience, and computer vision. We
propose a solution to this problem via a graph matching matched filter:
centering and padding the smaller adjacency matrix and applying graph matching
methods to align it to the larger network. The centering and padding schemes
can be incorporated into any algorithm that matches using adjacency matrices.
Under a statistical model for correlated pairs of graphs, which yields a noisy
copy of the small graph within the larger graph, the resulting optimization
problem can be guaranteed to recover the true vertex correspondence between the
networks.
However, there are currently no efficient algorithms for solving this
problem. To illustrate the possibilities and challenges of such problems, we
use an algorithm that can exploit a partially known correspondence and show via
varied simulations and applications to {\it Drosophila} and human connectomes
that this approach can achieve good performance.Comment: 41 pages, 7 figure
Matched filters for noisy induced subgraph detection
First author draftWe consider the problem of finding the vertex correspondence between two graphs with different number of vertices where the smaller graph is still potentially large. We propose a solution to this problem via a graph matching matched filter: padding the smaller graph in different ways and then using graph matching methods to align it to the larger network. Under a statistical model for correlated pairs of graphs, which yields a noisy copy of the small graph within the larger graph, the resulting optimization problem can be guaranteed to recover the true vertex correspondence between the networks, though there are currently no efficient algorithms for solving this problem. We consider an approach that exploits a partially known correspondence and show via varied simulations and applications to the Drosophila connectome that in practice this approach can achieve good performance.https://arxiv.org/abs/1803.02423https://arxiv.org/abs/1803.0242
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