102,679 research outputs found
Parallel Community Detection Based on Distance Dynamics for Large-Scale Network
© 2013 IEEE. Data mining task is a challenge on finding a high-quality community structure from large-scale networks. The distance dynamics model was proved to be active on regular-size network community, but it is difficult to discover the community structure effectively from the large-scale network (0.1-1 billion edges), due to the limit of machine hardware and high time complexity. In this paper, we proposed a parallel community detection algorithm based on the distance dynamics model called P-Attractor, which is capable of handling the detection problem of large networks community. Our algorithm first developed a graph partitioning method to divide large network into lots of sub-networks, yet maintaining the complete neighbor structure of the original network. Then, the traditional distance dynamics model was improved by the dynamic interaction process to simulate the distance evolution of each sub-network. Finally, we discovered the real community structure by removing all external edges after evolution process. In our extensive experiments on multiple synthetic networks and real-world networks, the results showed the effectiveness and efficiency of P-Attractor, and the execution time on 4 threads and 32 threads are around 10 and 2 h, respectively. Our proposed algorithm is potential to discover community from a billion-scale network, such as Uk-2007
Revisiting Interval Graphs for Network Science
The vertices of an interval graph represent intervals over a real line where
overlapping intervals denote that their corresponding vertices are adjacent.
This implies that the vertices are measurable by a metric and there exists a
linear structure in the system. The generalization is an embedding of a graph
onto a multi-dimensional Euclidean space and it was used by scientists to study
the multi-relational complexity of ecology. However the research went out of
fashion in the 1980s and was not revisited when Network Science recently
expressed interests with multi-relational networks known as multiplexes. This
paper studies interval graphs from the perspective of Network Science
Extraction and Analysis of Facebook Friendship Relations
Online Social Networks (OSNs) are a unique Web and social phenomenon, affecting tastes and behaviors of their users and helping them to maintain/create friendships. It is interesting to analyze the growth and evolution of Online Social Networks both from the point of view of marketing and other of new services and from a scientific viewpoint, since their structure and evolution may share similarities with real-life social networks. In social sciences, several techniques for analyzing (online) social networks have been developed, to evaluate quantitative properties (e.g., defining metrics and measures of structural characteristics of the networks) or qualitative aspects (e.g., studying the attachment model for the network evolution, the binary trust relationships, and the link prediction problem).\ud
However, OSN analysis poses novel challenges both to Computer and Social scientists. We present our long-term research effort in analyzing Facebook, the largest and arguably most successful OSN today: it gathers more than 500 million users. Access to data about Facebook users and their friendship relations, is restricted; thus, we acquired the necessary information directly from the front-end of the Web site, in order to reconstruct a sub-graph representing anonymous interconnections among a significant subset of users. We describe our ad-hoc, privacy-compliant crawler for Facebook data extraction. To minimize bias, we adopt two different graph mining techniques: breadth-first search (BFS) and rejection sampling. To analyze the structural properties of samples consisting of millions of nodes, we developed a specific tool for analyzing quantitative and qualitative properties of social networks, adopting and improving existing Social Network Analysis (SNA) techniques and algorithms
Generating realistic scaled complex networks
Research on generative models is a central project in the emerging field of
network science, and it studies how statistical patterns found in real networks
could be generated by formal rules. Output from these generative models is then
the basis for designing and evaluating computational methods on networks, and
for verification and simulation studies. During the last two decades, a variety
of models has been proposed with an ultimate goal of achieving comprehensive
realism for the generated networks. In this study, we (a) introduce a new
generator, termed ReCoN; (b) explore how ReCoN and some existing models can be
fitted to an original network to produce a structurally similar replica, (c)
use ReCoN to produce networks much larger than the original exemplar, and
finally (d) discuss open problems and promising research directions. In a
comparative experimental study, we find that ReCoN is often superior to many
other state-of-the-art network generation methods. We argue that ReCoN is a
scalable and effective tool for modeling a given network while preserving
important properties at both micro- and macroscopic scales, and for scaling the
exemplar data by orders of magnitude in size.Comment: 26 pages, 13 figures, extended version, a preliminary version of the
paper was presented at the 5th International Workshop on Complex Networks and
their Application
Synchronization in complex networks
Synchronization processes in populations of locally interacting elements are
in the focus of intense research in physical, biological, chemical,
technological and social systems. The many efforts devoted to understand
synchronization phenomena in natural systems take now advantage of the recent
theory of complex networks. In this review, we report the advances in the
comprehension of synchronization phenomena when oscillating elements are
constrained to interact in a complex network topology. We also overview the new
emergent features coming out from the interplay between the structure and the
function of the underlying pattern of connections. Extensive numerical work as
well as analytical approaches to the problem are presented. Finally, we review
several applications of synchronization in complex networks to different
disciplines: biological systems and neuroscience, engineering and computer
science, and economy and social sciences.Comment: Final version published in Physics Reports. More information
available at http://synchronets.googlepages.com
The anatomy of urban social networks and its implications in the searchability problem
The appearance of large geolocated communication datasets has recently
increased our understanding of how social networks relate to their physical
space. However, many recurrently reported properties, such as the spatial
clustering of network communities, have not yet been systematically tested at
different scales. In this work we analyze the social network structure of over
25 million phone users from three countries at three different scales: country,
provinces and cities. We consistently find that this last urban scenario
presents significant differences to common knowledge about social networks.
First, the emergence of a giant component in the network seems to be controlled
by whether or not the network spans over the entire urban border, almost
independently of the population or geographic extension of the city. Second,
urban communities are much less geographically clustered than expected. These
two findings shed new light on the widely-studied searchability in
self-organized networks. By exhaustive simulation of decentralized search
strategies we conclude that urban networks are searchable not through
geographical proximity as their country-wide counterparts, but through an
homophily-driven community structure
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