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Open AccessJournal ArticleDOI

Finding community structure in very large networks.

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.

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Citations
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Proceedings ArticleDOI

Frequent Subgraph Based Familial Classification of Android Malware

TL;DR: FalDroid, an automatic system for classifying Android malware according to fregraph, is proposed and developed and it is shown that FalDroid can correctly classify 94.5% malwares into their families using around 4.4s per app.
Journal ArticleDOI

Covariance, correlation matrix, and the multiscale community structure of networks.

TL;DR: This work concludes that the rescaling transformation is crucial to identify the multiscale community structure of network, as well as the translation and rotation transformations.
Proceedings ArticleDOI

Detecting Communities from Bipartite Networks Based on Bipartite Modularities

TL;DR: Experimental results show that the new bipartite modularity proposed by Guimera and Barber allows one-to-many correspondence between communities of different vertex types.
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S-CLONE: Socially-aware data replication for social networks

TL;DR: S-CLONE is proposed, a socially-aware data replication scheme which can significantly improve a social network's efficiency by taking into account social relationships of its data.
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Genome-wide RNAseq study of the molecular mechanisms underlying microglia activation in response to pathological tau perturbation in the rTg4510 tau transgenic animal model

TL;DR: Differentially expressed genes in rTg4510 microglia were enriched in human neurodegenerative disease associated pathways, including Alzheimer’s, Parkinson's, and Huntington's diseases, and highly overlapped with the microglian modules of human brain transcriptional co-expression networks.
References
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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

宁北芳, +1 more
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

Mark Newman
- 01 Jan 2003 - 
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.
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