166 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
On robust network coding subgraph construction under uncertainty
We consider the problem of network coding subgraph
construction in networks where there is uncertainty about
link loss rates. For a given set of scenarios specified by an uncertainty
set of link loss rates, we provide a robust optimization-based
formulation to construct a single subgraph that would work
relatively well across all scenarios. We show that this problem
is coNP-hard in general for both objectives: minimizing cost
of subgraph construction and maximizing throughput given a
cost constraint. To solve the problem tractably, we approximate
the problem by introducing path constraints, which results
in polynomial time-solvable solution in terms of the problem
size. The simulation results show that the robust optimization
solution is better and more stable than the deterministic solution
in terms of worst-case performance. From these results, we
compare the tractability of robust network design problems with
different uncertain network components and different problem
formulations
Towards an Efficient Discovery of the Topological Representative Subgraphs
With the emergence of graph databases, the task of frequent subgraph
discovery has been extensively addressed. Although the proposed approaches in
the literature have made this task feasible, the number of discovered frequent
subgraphs is still very high to be efficiently used in any further exploration.
Feature selection for graph data is a way to reduce the high number of frequent
subgraphs based on exact or approximate structural similarity. However, current
structural similarity strategies are not efficient enough in many real-world
applications, besides, the combinatorial nature of graphs makes it
computationally very costly. In order to select a smaller yet structurally
irredundant set of subgraphs, we propose a novel approach that mines the top-k
topological representative subgraphs among the frequent ones. Our approach
allows detecting hidden structural similarities that existing approaches are
unable to detect such as the density or the diameter of the subgraph. In
addition, it can be easily extended using any user defined structural or
topological attributes depending on the sought properties. Empirical studies on
real and synthetic graph datasets show that our approach is fast and scalable
Mining Representative Unsubstituted Graph Patterns Using Prior Similarity Matrix
One of the most powerful techniques to study protein structures is to look
for recurrent fragments (also called substructures or spatial motifs), then use
them as patterns to characterize the proteins under study. An emergent trend
consists in parsing proteins three-dimensional (3D) structures into graphs of
amino acids. Hence, the search of recurrent spatial motifs is formulated as a
process of frequent subgraph discovery where each subgraph represents a spatial
motif. In this scope, several efficient approaches for frequent subgraph
discovery have been proposed in the literature. However, the set of discovered
frequent subgraphs is too large to be efficiently analyzed and explored in any
further process. In this paper, we propose a novel pattern selection approach
that shrinks the large number of discovered frequent subgraphs by selecting the
representative ones. Existing pattern selection approaches do not exploit the
domain knowledge. Yet, in our approach we incorporate the evolutionary
information of amino acids defined in the substitution matrices in order to
select the representative subgraphs. We show the effectiveness of our approach
on a number of real datasets. The results issued from our experiments show that
our approach is able to considerably decrease the number of motifs while
enhancing their interestingness
Heuristics for Network Coding in Wireless Networks
Multicast is a central challenge for emerging multi-hop wireless
architectures such as wireless mesh networks, because of its substantial cost
in terms of bandwidth. In this report, we study one specific case of multicast:
broadcasting, sending data from one source to all nodes, in a multi-hop
wireless network. The broadcast we focus on is based on network coding, a
promising avenue for reducing cost; previous work of ours showed that the
performance of network coding with simple heuristics is asymptotically optimal:
each transmission is beneficial to nearly every receiver. This is for
homogenous and large networks of the plan. But for small, sparse or for
inhomogeneous networks, some additional heuristics are required. This report
proposes such additional new heuristics (for selecting rates) for broadcasting
with network coding. Our heuristics are intended to use only simple local
topology information. We detail the logic of the heuristics, and with
experimental results, we illustrate the behavior of the heuristics, and
demonstrate their excellent performance
A Note on the Practicality of Maximal Planar Subgraph Algorithms
Given a graph , the NP-hard Maximum Planar Subgraph problem (MPS) asks for
a planar subgraph of with the maximum number of edges. There are several
heuristic, approximative, and exact algorithms to tackle the problem, but---to
the best of our knowledge---they have never been compared competitively in
practice. We report on an exploratory study on the relative merits of the
diverse approaches, focusing on practical runtime, solution quality, and
implementation complexity. Surprisingly, a seemingly only theoretically strong
approximation forms the building block of the strongest choice.Comment: Appears in the Proceedings of the 24th International Symposium on
Graph Drawing and Network Visualization (GD 2016
DSL: Discriminative Subgraph Learning via Sparse Self-Representation
The goal in network state prediction (NSP) is to classify the global state
(label) associated with features embedded in a graph. This graph structure
encoding feature relationships is the key distinctive aspect of NSP compared to
classical supervised learning. NSP arises in various applications: gene
expression samples embedded in a protein-protein interaction (PPI) network,
temporal snapshots of infrastructure or sensor networks, and fMRI coherence
network samples from multiple subjects to name a few. Instances from these
domains are typically ``wide'' (more features than samples), and thus, feature
sub-selection is required for robust and generalizable prediction. How to best
employ the network structure in order to learn succinct connected subgraphs
encompassing the most discriminative features becomes a central challenge in
NSP. Prior work employs connected subgraph sampling or graph smoothing within
optimization frameworks, resulting in either large variance of quality or weak
control over the connectivity of selected subgraphs.
In this work we propose an optimization framework for discriminative subgraph
learning (DSL) which simultaneously enforces (i) sparsity, (ii) connectivity
and (iii) high discriminative power of the resulting subgraphs of features. Our
optimization algorithm is a single-step solution for the NSP and the associated
feature selection problem. It is rooted in the rich literature on
maximal-margin optimization, spectral graph methods and sparse subspace
self-representation. DSL simultaneously ensures solution interpretability and
superior predictive power (up to 16% improvement in challenging instances
compared to baselines), with execution times up to an hour for large instances.Comment: 9 page
- âŠ