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
Control range: a controllability-based index for node significance in directed networks
Bingbo Wang,Lin Gao,Yong Gao +2 more
TL;DR: A novel structural index, the control range, motivated by recent studies on the structural controllability of large-scale directed networks is proposed, and used to detect and analyze functional modules in glossary networks and the enzyme-centric network of homo sapiens.
Journal ArticleDOI
EEG analysis of seizure patterns using visibility graphs for detection of generalized seizures
TL;DR: The proposed VGS-based features can help improve seizure detection for ID patients and show significant difference between seizure and non-seizure EEG.
Journal ArticleDOI
Agent-based simulation in management and organizational studies: a survey
TL;DR: The survey shows that ABS provides a robust and rigorous framework to elaborate descriptions, explanations, predictions and theories about organizations and their processes as well as develop tools that support strategic and operational decision making and problem-solving.
Journal ArticleDOI
Exploring the mechanism of TCM formulae in the treatment of different types of coronary heart disease by network pharmacology and machining learning.
TL;DR: This work utilized network pharmacology and machine learning to explore the mechanism of eight classic TCM formulae in the treatment of different types of CHD and designed a rank algorithm, which integrated network topology with biological function, to identify important targets of these formULae.
Journal ArticleDOI
Exploring connectivity with large-scale Granger causality on resting-state functional MRI
TL;DR: Functional network recovery from fMRI data suggests that lsGC gives useful insight into connectivity patterns from resting-state fMRI at a multivariate voxel-resolution.
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.