Finding community structure in very large networks.
Reads0
Chats0
TLDR
A hierarchical agglomeration algorithm for detecting community structure which is faster than many competing algorithms: its running time on a network with n vertices and m edges is O (md log n) where d is the depth of the dendrogram describing the community structure.Abstract:
The discovery and analysis of community structure in networks is a topic of considerable recent interest within the physics community, but most methods proposed so far are unsuitable for very large networks because of their computational cost. Here we present a hierarchical agglomeration algorithm for detecting community structure which is faster than many competing algorithms: its running time on a network with n vertices and m edges is O (md log n) where d is the depth of the dendrogram describing the community structure. Many real-world networks are sparse and hierarchical, with m approximately n and d approximately log n, in which case our algorithm runs in essentially linear time, O (n log(2) n). As an example of the application of this algorithm we use it to analyze a network of items for sale on the web site of a large on-line retailer, items in the network being linked if they are frequently purchased by the same buyer. The network has more than 400 000 vertices and 2 x 10(6) edges. We show that our algorithm can extract meaningful communities from this network, revealing large-scale patterns present in the purchasing habits of customers.read more
Citations
More filters
Journal ArticleDOI
Cropping practices manipulate abundance patterns of root and soil microbiome members paving the way to smart farming.
Kyle Hartman,Marcel G. A. van der Heijden,Marcel G. A. van der Heijden,Raphaël Wittwer,Samiran Banerjee,Jean Claude Walser,Klaus Schlaeppi +6 more
TL;DR: It is found that about 10% of variation in microbial communities was explained by the tested cropping practices, which presents the basis towards developing microbiota management strategies for smart farming.
Journal ArticleDOI
Detecting network communities by propagating labels under constraints.
Michael J. Barber,John W. Clark +1 more
TL;DR: This work reformulates the recently proposed label-propagation algorithm (LPA) as an equivalent optimization problem, giving an objective function whose maxima correspond to community solutions, and produces a variety of algorithms that propagate labels subject to constraints.
Journal ArticleDOI
Overlapping community detection using Bayesian non-negative matrix factorization
TL;DR: This work presents a probabilistic approach to community detection that utilizes a Bayesian non-negative matrix factorization model to extract overlapping modules from a network.
Journal ArticleDOI
Graph-based clustering and characterization of repetitive sequences in next-generation sequencing data.
TL;DR: Repetitive regions of plant genomes can be efficiently characterized by the presented graph-based analysis and the graph representation of repeats can be further used to assess the variability and evolutionary divergence of repeat families, discover and characterize novel elements, and aid in subsequent assembly of their consensus sequences.
Journal ArticleDOI
Robustness of community structure in networks.
TL;DR: It is shown that the significance of community structure can be effectively quantified by measuring its robustness to small perturbations in network structure.
References
More filters
Journal ArticleDOI
Collective dynamics of small-world networks
TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
疟原虫var基因转换速率变化导致抗原变异[英]/Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Journal ArticleDOI
Statistical mechanics of complex networks
TL;DR: In this paper, a simple model based on the power-law degree distribution of real networks was proposed, which was able to reproduce the power law degree distribution in real networks and to capture the evolution of networks, not just their static topology.
Journal ArticleDOI
The Structure and Function of Complex Networks
TL;DR: Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.