305,316 research outputs found
A Modified Distance Dynamics Model for Improvement of Community Detection
© 2018 IEEE. Community detection is a key technique for identifying the intrinsic community structures of complex networks. The distance dynamics model has been proven effective in finding communities with arbitrary size and shape and identifying outliers. However, to simulate distance dynamics, the model requires manual parameter specification and is sensitive to the cohesion threshold parameter, which is difficult to determine. Furthermore, it has difficulty handling rough outliers and ignores hubs (nodes that bridge communities). In this paper, we propose a robust distance dynamics model, namely, Attractor++, which uses a dynamic membership degree. In Attractor++, the dynamic membership degree is used to determine the influence of exclusive neighbors on the distance instead of setting the cohesion threshold. In addition, considering its inefficiency and low accuracy in handling outliers and identifying hubs, we design an outlier optimization model that is based on triangle adjacency. By using optimization rules, a postprocessing method further judges whether a singleton node should be merged into the same community as its triangles or regarded as a hub or an outlier. Extensive experiments on both real-world and synthetic networks demonstrate that our algorithm more accurately identifies nodes that have special roles (hubs and outliers) and more effectively identifies community structures
A Comprehensive Analysis of Multilayer Community Detection Algorithms for Application to EEG-Based Brain Networks
Modular organization is an emergent property of brain networks, responsible for shaping communication processes and underpinning brain functioning. Moreover, brain networks are intrinsically multilayer since their attributes can vary across time, subjects, frequency, or other domains. Identifying the modular structure in multilayer brain networks represents a gateway toward a deeper understanding of neural processes underlying cognition. Electroencephalographic (EEG) signals, thanks to their high temporal resolution, can give rise to multilayer networks able to follow the dynamics of brain activity. Despite this potential, the community organization has not yet been thoroughly investigated in brain networks estimated from EEG. Furthermore, at the state of the art, there is still no agreement about which algorithm is the most suitable to detect communities in multilayer brain networks, and a way to test and compare them all under a variety of conditions is lacking. In this work, we perform a comprehensive analysis of three algorithms at the state of the art for multilayer community detection (namely, genLouvain, DynMoga, and FacetNet) as compared with an approach based on the application of a single-layer clustering algorithm to each slice of the multilayer network. We test their ability to identify both steady and dynamic modular structures. We statistically evaluate their performances by means of ad hoc benchmark graphs characterized by properties covering a broad range of conditions in terms of graph density, number of clusters, noise level, and number of layers. The results of this simulation study aim to provide guidelines about the choice of the more appropriate algorithm according to the different properties of the brain network under examination. Finally, as a proof of concept, we show an application of the algorithms to real functional brain networks derived from EEG signals collected at rest with closed and open eyes. The test on real data provided results in agreement with the conclusions of the simulation study and confirmed the feasibility of multilayer analysis of EEG-based brain networks in both steady and dynamic conditions
Bioinformatics tools in predictive ecology: Applications to fisheries
This article is made available throught the Brunel Open Access Publishing Fund - Copygith @ 2012 Tucker et al.There has been a huge effort in the advancement of analytical techniques for molecular biological data over the past decade. This has led to many novel algorithms that are specialized to deal with data associated with biological phenomena, such as gene expression and protein interactions. In contrast, ecological data analysis has remained focused to some degree on off-the-shelf statistical techniques though this is starting to change with the adoption of state-of-the-art methods, where few assumptions can be made about the data and a more explorative approach is required, for example, through the use of Bayesian networks. In this paper, some novel bioinformatics tools for microarray data are discussed along with their ‘crossover potential’ with an application to fisheries data. In particular, a focus is made on the development of models that identify functionally equivalent species in different fish communities with the aim of predicting functional collapse
Fundamental structures of dynamic social networks
Social systems are in a constant state of flux with dynamics spanning from
minute-by-minute changes to patterns present on the timescale of years.
Accurate models of social dynamics are important for understanding spreading of
influence or diseases, formation of friendships, and the productivity of teams.
While there has been much progress on understanding complex networks over the
past decade, little is known about the regularities governing the
micro-dynamics of social networks. Here we explore the dynamic social network
of a densely-connected population of approximately 1000 individuals and their
interactions in the network of real-world person-to-person proximity measured
via Bluetooth, as well as their telecommunication networks, online social media
contacts, geo-location, and demographic data. These high-resolution data allow
us to observe social groups directly, rendering community detection
unnecessary. Starting from 5-minute time slices we uncover dynamic social
structures expressed on multiple timescales. On the hourly timescale, we find
that gatherings are fluid, with members coming and going, but organized via a
stable core of individuals. Each core represents a social context. Cores
exhibit a pattern of recurring meetings across weeks and months, each with
varying degrees of regularity. Taken together, these findings provide a
powerful simplification of the social network, where cores represent
fundamental structures expressed with strong temporal and spatial regularity.
Using this framework, we explore the complex interplay between social and
geospatial behavior, documenting how the formation of cores are preceded by
coordination behavior in the communication networks, and demonstrating that
social behavior can be predicted with high precision.Comment: Main Manuscript: 16 pages, 4 figures. Supplementary Information: 39
pages, 34 figure
Identifying the underlying structure and dynamic interactions in a voting network
We analyse the structure and behaviour of a specific voting network using a
dynamic structure-based methodology which draws on Q-Analysis and social
network theory. Our empirical focus is on the Eurovision Song Contest over a
period of 20 years. For a multicultural contest of this kind, one of the key
questions is how the quality of a song is judged and how voting groups emerge.
We investigate structures that may identify the winner based purely on the
topology of the network. This provides a basic framework to identify what the
characteristics associated with becoming a winner are, and may help to
establish a homogenous criterion for subjective measures such as quality.
Further, we measure the importance of voting cliques, and present a dynamic
model based on a changing multidimensional measure of connectivity in order to
reveal the formation of emerging community structure within the contest.
Finally, we study the dynamic behaviour exhibited by the network in order to
understand the clustering of voting preferences and the relationship between
local and global properties.Comment: 20 pages, 10 figures, 3 tables, submitted to Physica
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