34,836 research outputs found
Grouping complex systems: a weighted network comparative analysis
In this study, the authors compare two inter-municipal commuting networks (MCN) pertaining to the Italian islands of Sardinia and Sicily, by approaching their characterization through a weighted network analysis. They develop on
the results obtained for the MCN of Sardinia (De Montis et al. 2007) and attempt to use network analysis as a mean of detection of similarities or dissimilarities between the systems at hand
Clustering of tag-induced sub-graphs in complex networks
We study the behavior of the clustering coefficient in tagged networks. The
rich variety of tags associated with the nodes in the studied systems provide
additional information about the entities represented by the nodes which can be
important for practical applications like searching in the networks. Here we
examine how the clustering coefficient changes when narrowing the network to a
sub-graph marked by a given tag, and how does it correlate with various other
properties of the sub-graph. Another interesting question addressed in the
paper is how the clustering coefficient of the individual nodes is affected by
the tags on the node. We believe these sort of analysis help acquiring a more
complete description of the structure of large complex systems
Folksonomies and clustering in the collaborative system CiteULike
We analyze CiteULike, an online collaborative tagging system where users
bookmark and annotate scientific papers. Such a system can be naturally
represented as a tripartite graph whose nodes represent papers, users and tags
connected by individual tag assignments. The semantics of tags is studied here,
in order to uncover the hidden relationships between tags. We find that the
clustering coefficient reflects the semantical patterns among tags, providing
useful ideas for the designing of more efficient methods of data classification
and spam detection.Comment: 9 pages, 5 figures, iop style; corrected typo
Global Network Alignment
Motivation: High-throughput methods for detecting molecular interactions have lead to a plethora of biological network data with much more yet to come, stimulating the development of techniques for biological network alignment. Analogous to sequence alignment, efficient and reliable network alignment methods will improve our understanding of biological systems. Network alignment is computationally hard. Hence, devising efficient network alignment heuristics is currently one of the foremost challenges in computational biology. 

Results: We present a superior heuristic network alignment algorithm, called Matching-based GRAph ALigner (M-GRAAL), which can process and integrate any number and type of similarity measures between network nodes (e.g., proteins), including, but not limited to, any topological network similarity measure, sequence similarity, functional similarity, and structural similarity. This is efficient in resolving ties in similarity measures and in finding a combination of similarity measures yielding the largest biologically sound alignments. When used to align protein-protein interaction (PPI) networks of various species, M-GRAAL exposes the largest known functional and contiguous regions of network similarity. Hence, we use M-GRAAL’s alignments to predict functions of un-annotated proteins in yeast, human, and bacteria _C. jejuni_ and _E. coli_. Furthermore, using M-GRAAL to compare PPI networks of different herpes viruses, we reconstruct their phylogenetic relationship and our phylogenetic tree is the same as sequenced-based one
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