110,582 research outputs found
Network Community Detection on Metric Space
Community detection in a complex network is an important problem of much
interest in recent years. In general, a community detection algorithm chooses
an objective function and captures the communities of the network by optimizing
the objective function, and then, one uses various heuristics to solve the
optimization problem to extract the interesting communities for the user. In
this article, we demonstrate the procedure to transform a graph into points of
a metric space and develop the methods of community detection with the help of
a metric defined for a pair of points. We have also studied and analyzed the
community structure of the network therein. The results obtained with our
approach are very competitive with most of the well-known algorithms in the
literature, and this is justified over the large collection of datasets. On the
other hand, it can be observed that time taken by our algorithm is quite less
compared to other methods and justifies the theoretical findings
Predicting protein functions with message passing algorithms
Motivation: In the last few years a growing interest in biology has been
shifting towards the problem of optimal information extraction from the huge
amount of data generated via large scale and high-throughput techniques. One of
the most relevant issues has recently become that of correctly and reliably
predicting the functions of observed but still functionally undetermined
proteins starting from information coming from the network of co-observed
proteins of known functions.
Method: The method proposed in this article is based on a message passing
algorithm known as Belief Propagation, which takes as input the network of
proteins physical interactions and a catalog of known proteins functions, and
returns the probabilities for each unclassified protein of having one chosen
function. The implementation of the algorithm allows for fast on-line analysis,
and can be easily generalized to more complex graph topologies taking into
account hyper-graphs, {\em i.e.} complexes of more than two interacting
proteins.Comment: 12 pages, 9 eps figures, 1 additional html tabl
Joint Centrality Distinguishes Optimal Leaders in Noisy Networks
We study the performance of a network of agents tasked with tracking an
external unknown signal in the presence of stochastic disturbances and under
the condition that only a limited subset of agents, known as leaders, can
measure the signal directly. We investigate the optimal leader selection
problem for a prescribed maximum number of leaders, where the optimal leader
set minimizes total system error defined as steady-state variance about the
external signal. In contrast to previously established greedy algorithms for
optimal leader selection, our results rely on an expression of total system
error in terms of properties of the underlying network graph. We demonstrate
that the performance of any given set of leaders depends on their influence as
determined by a new graph measure of centrality of a set. We define the of a set of nodes in a network graph such that a leader set with
maximal joint centrality is an optimal leader set. In the case of a single
leader, we prove that the optimal leader is the node with maximal information
centrality. In the case of multiple leaders, we show that the nodes in the
optimal leader set balance high information centrality with a coverage of the
graph. For special cases of graphs, we solve explicitly for optimal leader
sets. We illustrate with examples.Comment: Conditionally accepted to IEEE TCN
Model Predictive Control Based Trajectory Generation for Autonomous Vehicles - An Architectural Approach
Research in the field of automated driving has created promising results in
the last years. Some research groups have shown perception systems which are
able to capture even complicated urban scenarios in great detail. Yet, what is
often missing are general-purpose path- or trajectory planners which are not
designed for a specific purpose. In this paper we look at path- and trajectory
planning from an architectural point of view and show how model predictive
frameworks can contribute to generalized path- and trajectory generation
approaches for generating safe trajectories even in cases of system failures.Comment: Presented at IEEE Intelligent Vehicles Symposium 2017, Los Angeles,
CA, US
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