5,637 research outputs found
Multilayer Complex Network Descriptors for Color-Texture Characterization
A new method based on complex networks is proposed for color-texture
analysis. The proposal consists on modeling the image as a multilayer complex
network where each color channel is a layer, and each pixel (in each color
channel) is represented as a network vertex. The network dynamic evolution is
accessed using a set of modeling parameters (radii and thresholds), and new
characterization techniques are introduced to capt information regarding within
and between color channel spatial interaction. An automatic and adaptive
approach for threshold selection is also proposed. We conduct classification
experiments on 5 well-known datasets: Vistex, Usptex, Outex13, CURet and MBT.
Results among various literature methods are compared, including deep
convolutional neural networks with pre-trained architectures. The proposed
method presented the highest overall performance over the 5 datasets, with 97.7
of mean accuracy against 97.0 achieved by the ResNet convolutional neural
network with 50 layers.Comment: 20 pages, 7 figures and 4 table
Weighted and unweighted network of amino acids within protein
The information regarding the structure of a single protein is encoded in the
network of interacting amino acids. Considering each protein as a weighted and
unweighted network of amino acids we have analyzed a total of forty nine
protein structures that covers the three branches of life on earth. Our results
show that the probability degree distribution of network connectivity follows
Poisson's distribution; whereas the probability strength distribution does not
follow any known distribution. However, the average strength of amino acid node
depends on its degree (k). For some of the proteins, the strength of a node
increases linearly with k. On the other hand, for a set of other proteins,
although the strength increases linaerly with k for smaller values of k, we
have not obtained any clear functional relationship of strength with degree at
higher values of k. The results also show that the weight of the amino acid
nodes belonging to the highly connected nodes tend to have a higher value. The
result that the average clustering coefficient of weighted network is less than
that of unweighted network implies that the topological clustering is generated
by edges with low weights. The ratio of average clustering coefficients of
protein network to that of the corresponding classical random network varies
linearly with the number (N) of amino acids of a protein; whereas the ratio of
characteristic path lengths varies logarithmically with N. The power law
behaviour of clustering coefficients of weighted and unweighted network as a
function of degree k indicates that the network has a signature of hierarchical
network. It has also been observed that the network is of assortative type
The Architecture of a Novel Weighted Network: Knowledge Network
Networked structure emerged from a wide range of fields such as biological
systems, World Wide Web and technological infrastructure. A deeply insight into
the topological complexity of these networks has been gained. Some works start
to pay attention to the weighted network, like the world-wide airport network
and the collaboration network, where links are not binary, but have
intensities. Here, we construct a novel knowledge network, through which we
take the first step to uncover the topological structure of the knowledge
system. Furthermore, the network is extended to the weighted one by assigning
weights to the edges. Thus, we also investigate the relationship between the
intensity of edges and the topological structure. These results provide a novel
description to understand the hierarchies and organizational principles in
knowledge system, and the interaction between the intensity of edges and
topological structure. This system also provides a good paradigm to study
weighted networks.Comment: 5 figures 11 page
Greedy Strategy Works for k-Center Clustering with Outliers and Coreset Construction
We study the problem of k-center clustering with outliers in arbitrary metrics and Euclidean space. Though a number of methods have been developed in the past decades, it is still quite challenging to design quality guaranteed algorithm with low complexity for this problem. Our idea is inspired by the greedy method, Gonzalez\u27s algorithm, for solving the problem of ordinary k-center clustering. Based on some novel observations, we show that this greedy strategy actually can handle k-center clustering with outliers efficiently, in terms of clustering quality and time complexity. We further show that the greedy approach yields small coreset for the problem in doubling metrics, so as to reduce the time complexity significantly. Our algorithms are easy to implement in practice. We test our method on both synthetic and real datasets. The experimental results suggest that our algorithms can achieve near optimal solutions and yield lower running times comparing with existing methods
Scalable Online Betweenness Centrality in Evolving Graphs
Betweenness centrality is a classic measure that quantifies the importance of
a graph element (vertex or edge) according to the fraction of shortest paths
passing through it. This measure is notoriously expensive to compute, and the
best known algorithm runs in O(nm) time. The problems of efficiency and
scalability are exacerbated in a dynamic setting, where the input is an
evolving graph seen edge by edge, and the goal is to keep the betweenness
centrality up to date. In this paper we propose the first truly scalable
algorithm for online computation of betweenness centrality of both vertices and
edges in an evolving graph where new edges are added and existing edges are
removed. Our algorithm is carefully engineered with out-of-core techniques and
tailored for modern parallel stream processing engines that run on clusters of
shared-nothing commodity hardware. Hence, it is amenable to real-world
deployment. We experiment on graphs that are two orders of magnitude larger
than previous studies. Our method is able to keep the betweenness centrality
measures up to date online, i.e., the time to update the measures is smaller
than the inter-arrival time between two consecutive updates.Comment: 15 pages, 9 Figures, accepted for publication in IEEE Transactions on
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