12,666 research outputs found
Multilevel compression of random walks on networks reveals hierarchical organization in large integrated systems
To comprehend the hierarchical organization of large integrated systems, we
introduce the hierarchical map equation, which reveals multilevel structures in
networks. In this information-theoretic approach, we exploit the duality
between compression and pattern detection; by compressing a description of a
random walker as a proxy for real flow on a network, we find regularities in
the network that induce this system-wide flow. Finding the shortest multilevel
description of the random walker therefore gives us the best hierarchical
clustering of the network, the optimal number of levels and modular partition
at each level, with respect to the dynamics on the network. With a novel search
algorithm, we extract and illustrate the rich multilevel organization of
several large social and biological networks. For example, from the global air
traffic network we uncover countries and continents, and from the pattern of
scientific communication we reveal more than 100 scientific fields organized in
four major disciplines: life sciences, physical sciences, ecology and earth
sciences, and social sciences. In general, we find shallow hierarchical
structures in globally interconnected systems, such as neural networks, and
rich multilevel organizations in systems with highly separated regions, such as
road networks.Comment: 11 pages, 5 figures. For associated code, see
http://www.tp.umu.se/~rosvall/code.htm
Maps of random walks on complex networks reveal community structure
To comprehend the multipartite organization of large-scale biological and
social systems, we introduce a new information theoretic approach that reveals
community structure in weighted and directed networks. The method decomposes a
network into modules by optimally compressing a description of information
flows on the network. The result is a map that both simplifies and highlights
the regularities in the structure and their relationships. We illustrate the
method by making a map of scientific communication as captured in the citation
patterns of more than 6000 journals. We discover a multicentric organization
with fields that vary dramatically in size and degree of integration into the
network of science. Along the backbone of the network -- including physics,
chemistry, molecular biology, and medicine -- information flows
bidirectionally, but the map reveals a directional pattern of citation from the
applied fields to the basic sciences.Comment: 7 pages and 4 figures plus supporting material. For associated source
code, see http://www.tp.umu.se/~rosvall
RDSZ: an approach for lossless RDF stream compression
In many applications (like social or sensor networks) the in-
formation generated can be represented as a continuous stream of RDF
items, where each item describes an application event (social network
post, sensor measurement, etc). In this paper we focus on compressing
RDF streams. In particular, we propose an approach for lossless RDF
stream compression, named RDSZ (RDF Differential Stream compressor
based on Zlib). This approach takes advantage of the structural similarities among items in a stream by combining a differential item encoding
mechanism with the general purpose stream compressor Zlib. Empirical
evaluation using several RDF stream datasets shows that this combi-
nation produces gains in compression ratios with respect to using Zlib
alone
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