8,442 research outputs found
Compressing networks with super nodes
Community detection is a commonly used technique for identifying groups in a
network based on similarities in connectivity patterns. To facilitate community
detection in large networks, we recast the network to be partitioned into a
smaller network of 'super nodes', each super node comprising one or more nodes
in the original network. To define the seeds of our super nodes, we apply the
'CoreHD' ranking from dismantling and decycling. We test our approach through
the analysis of two common methods for community detection: modularity
maximization with the Louvain algorithm and maximum likelihood optimization for
fitting a stochastic block model. Our results highlight that applying community
detection to the compressed network of super nodes is significantly faster
while successfully producing partitions that are more aligned with the local
network connectivity, more stable across multiple (stochastic) runs within and
between community detection algorithms, and overlap well with the results
obtained using the full network
A perceptual hash function to store and retrieve large scale DNA sequences
This paper proposes a novel approach for storing and retrieving massive DNA
sequences.. The method is based on a perceptual hash function, commonly used to
determine the similarity between digital images, that we adapted for DNA
sequences. Perceptual hash function presented here is based on a Discrete
Cosine Transform Sign Only (DCT-SO). Each nucleotide is encoded as a fixed gray
level intensity pixel and the hash is calculated from its significant frequency
characteristics. This results to a drastic data reduction between the sequence
and the perceptual hash. Unlike cryptographic hash functions, perceptual hashes
are not affected by "avalanche effect" and thus can be compared. The similarity
distance between two hashes is estimated with the Hamming Distance, which is
used to retrieve DNA sequences. Experiments that we conducted show that our
approach is relevant for storing massive DNA sequences, and retrieving them
Clustering and Community Detection in Directed Networks: A Survey
Networks (or graphs) appear as dominant structures in diverse domains,
including sociology, biology, neuroscience and computer science. In most of the
aforementioned cases graphs are directed - in the sense that there is
directionality on the edges, making the semantics of the edges non symmetric.
An interesting feature that real networks present is the clustering or
community structure property, under which the graph topology is organized into
modules commonly called communities or clusters. The essence here is that nodes
of the same community are highly similar while on the contrary, nodes across
communities present low similarity. Revealing the underlying community
structure of directed complex networks has become a crucial and
interdisciplinary topic with a plethora of applications. Therefore, naturally
there is a recent wealth of research production in the area of mining directed
graphs - with clustering being the primary method and tool for community
detection and evaluation. The goal of this paper is to offer an in-depth review
of the methods presented so far for clustering directed networks along with the
relevant necessary methodological background and also related applications. The
survey commences by offering a concise review of the fundamental concepts and
methodological base on which graph clustering algorithms capitalize on. Then we
present the relevant work along two orthogonal classifications. The first one
is mostly concerned with the methodological principles of the clustering
algorithms, while the second one approaches the methods from the viewpoint
regarding the properties of a good cluster in a directed network. Further, we
present methods and metrics for evaluating graph clustering results,
demonstrate interesting application domains and provide promising future
research directions.Comment: 86 pages, 17 figures. Physics Reports Journal (To Appear
The Myth of Global Science Collaboration - Collaboration patterns in epistemic communities
Scientific collaboration is often perceived as a joint global process that
involves researchers worldwide, regardless of their place of work and
residence. Globalization of science, in this respect, implies that
collaboration among scientists takes place along the lines of common topics and
irrespective of the spatial distances between the collaborators. The networks
of collaborators, termed 'epistemic communities', should thus have a
space-independent structure. This paper shows that such a notion of globalized
scientific collaboration is not supported by empirical data. It introduces a
novel approach of analyzing distance-dependent probabilities of collaboration.
The results of the analysis of six distinct scientific fields reveal that
intra-country collaboration is about 10-50 times more likely to occur than
international collaboration. Moreover, strong dependencies exist between
collaboration activity (measured in co-authorships) and spatial distance when
confined to national borders. However, the fact that distance becomes
irrelevant once collaboration is taken to the international scale suggests a
globalized science system that is strongly influenced by the gravity of local
science clusters. The similarity of the probability functions of the six
science fields analyzed suggests a universal mode of spatial governance that is
independent from the mode of knowledge creation in science.Comment: 13 pages, 3 figures, 1 tabl
Comparing biological networks via graph compression
<p>Abstract</p> <p>Background</p> <p>Comparison of various kinds of biological data is one of the main problems in bioinformatics and systems biology. Data compression methods have been applied to comparison of large sequence data and protein structure data. Since it is still difficult to compare global structures of large biological networks, it is reasonable to try to apply data compression methods to comparison of biological networks. In existing compression methods, the uniqueness of compression results is not guaranteed because there is some ambiguity in selection of overlapping edges.</p> <p>Results</p> <p>This paper proposes novel efficient methods, CompressEdge and CompressVertices, for comparing large biological networks. In the proposed methods, an original network structure is compressed by iteratively contracting identical edges and sets of connected edges. Then, the similarity of two networks is measured by a compression ratio of the concatenated networks. The proposed methods are applied to comparison of metabolic networks of several organisms, <it>H. sapiens, M. musculus, A. thaliana, D. melanogaster, C. elegans, E. coli, S. cerevisiae,</it> and <it>B. subtilis,</it> and are compared with an existing method. These results suggest that our methods can efficiently measure the similarities between metabolic networks.</p> <p>Conclusions</p> <p>Our proposed algorithms, which compress node-labeled networks, are useful for measuring the similarity of large biological networks.</p
Multi-Target Prediction: A Unifying View on Problems and Methods
Multi-target prediction (MTP) is concerned with the simultaneous prediction
of multiple target variables of diverse type. Due to its enormous application
potential, it has developed into an active and rapidly expanding research field
that combines several subfields of machine learning, including multivariate
regression, multi-label classification, multi-task learning, dyadic prediction,
zero-shot learning, network inference, and matrix completion. In this paper, we
present a unifying view on MTP problems and methods. First, we formally discuss
commonalities and differences between existing MTP problems. To this end, we
introduce a general framework that covers the above subfields as special cases.
As a second contribution, we provide a structured overview of MTP methods. This
is accomplished by identifying a number of key properties, which distinguish
such methods and determine their suitability for different types of problems.
Finally, we also discuss a few challenges for future research
How round is a protein? Exploring protein structures for globularity using conformal mapping.
We present a new algorithm that automatically computes a measure of the geometric difference between the surface of a protein and a round sphere. The algorithm takes as input two triangulated genus zero surfaces representing the protein and the round sphere, respectively, and constructs a discrete conformal map f between these surfaces. The conformal map is chosen to minimize a symmetric elastic energy E S (f) that measures the distance of f from an isometry. We illustrate our approach on a set of basic sample problems and then on a dataset of diverse protein structures. We show first that E S (f) is able to quantify the roundness of the Platonic solids and that for these surfaces it replicates well traditional measures of roundness such as the sphericity. We then demonstrate that the symmetric elastic energy E S (f) captures both global and local differences between two surfaces, showing that our method identifies the presence of protruding regions in protein structures and quantifies how these regions make the shape of a protein deviate from globularity. Based on these results, we show that E S (f) serves as a probe of the limits of the application of conformal mapping to parametrize protein shapes. We identify limitations of the method and discuss its extension to achieving automatic registration of protein structures based on their surface geometry
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