1,901 research outputs found
The impact of graph construction scheme and community detection algorithm on the reliability of community and hub identification in structural brain networks
A critical question in network neuroscience is how nodes cluster together to form communities, to form the mesoscale organisation of the brain. Various algorithms have been proposed for identifying such communities, each identifying different communities within the same network. Here, (using test–retest data from the Human Connectome Project), the repeatability of thirty-three community detection algorithms, each paired with seven different graph construction schemes were assessed. Repeatability of community partition depended heavily on both the community detection algorithm and graph construction scheme. Hard community detection algorithms (in which each node is assigned to only one community) outperformed soft ones (in which each node can belong to more than one community). The highest repeatability was observed for the fast multi-scale community detection algorithm paired with a graph construction scheme that combines nine white matter metrics. This pair also gave the highest similarity between representative group community affiliation and individual community affiliation. Connector hubs had higher repeatability than provincial hubs. Our results provide a workflow for repeatable identification of structural brain networks communities, based on the optimal pairing of community detection algorithm and graph construction scheme
Recommended from our members
Local search: A guide for the information retrieval practitioner
There are a number of combinatorial optimisation problems in information retrieval in which the use of local search methods are worthwhile. The purpose of this paper is to show how local search can be used to solve some well known tasks in information retrieval (IR), how previous research in the field is piecemeal, bereft of a structure and methodologically flawed, and to suggest more rigorous ways of applying local search methods to solve IR problems. We provide a query based taxonomy for analysing the use of local search in IR tasks and an overview of issues such as fitness functions, statistical significance and test collections when conducting experiments on combinatorial optimisation problems. The paper gives a guide on the pitfalls and problems for IR practitioners who wish to use local search to solve their research issues, and gives practical advice on the use of such methods. The query based taxonomy is a novel structure which can be used by the IR practitioner in order to examine the use of local search in IR
A Systematic Analysis of Community Detection in Complex Networks
Numerous techniques have been proposed by researchers to uncover the hidden patterns of real-world complex networks. Finding a hidden community is one of the crucial tasks for community detection in complex networks. Despite the presence of multiple methods for community detection, identification of the best performing method over different complex networks is still an open research question. In this article, we analyzed eight state-of-the-art community detection algorithms on nine complex networks of varying sizes covering various domains including animal, biomedical, terrorist, social, and human contacts. The objective of this article is to identify the best performing algorithm for community detection in real-world complex networks of various sizes and from different domains. The obtained results over 100 iterations demonstrated that the multi-scale method has outperformed the other techniques in terms of accuracy. Multi-scale method achieved 0.458 average value of modularity metric whereas multiple screening resolution, unfolding fast, greedy, multi-resolution, local fitness optimization, sparse Geosocial community detection algorithm, and spectral clustering, respectively obtained the modularity values 0.455, 0.441, 0.436, 0.421, 0.368, 0.341, and 0.340.
Interest communities and flow roles in directed networks: the Twitter network of the UK riots
Directionality is a crucial ingredient in many complex networks in which
information, energy or influence are transmitted. In such directed networks,
analysing flows (and not only the strength of connections) is crucial to reveal
important features of the network that might go undetected if the orientation
of connections is ignored. We showcase here a flow-based approach for community
detection in networks through the study of the network of the most influential
Twitter users during the 2011 riots in England. Firstly, we use directed Markov
Stability to extract descriptions of the network at different levels of
coarseness in terms of interest communities, i.e., groups of nodes within which
flows of information are contained and reinforced. Such interest communities
reveal user groupings according to location, profession, employer, and topic.
The study of flows also allows us to generate an interest distance, which
affords a personalised view of the attention in the network as viewed from the
vantage point of any given user. Secondly, we analyse the profiles of incoming
and outgoing long-range flows with a combined approach of role-based similarity
and the novel relaxed minimum spanning tree algorithm to reveal that the users
in the network can be classified into five roles. These flow roles go beyond
the standard leader/follower dichotomy and differ from classifications based on
regular/structural equivalence. We then show that the interest communities fall
into distinct informational organigrams characterised by a different mix of
user roles reflecting the quality of dialogue within them. Our generic
framework can be used to provide insight into how flows are generated,
distributed, preserved and consumed in directed networks.Comment: 32 pages, 14 figures. Supplementary Spreadsheet available from:
http://www2.imperial.ac.uk/~mbegueri/Docs/riotsCommunities.zip or
http://rsif.royalsocietypublishing.org/content/11/101/20140940/suppl/DC
Reading the news through its structure: new hybrid connectivity based approaches
In this thesis a solution for the problem of identifying the structure of news published
by online newspapers is presented. This problem requires new approaches and algorithms
that are capable of dealing with the massive number of online publications in existence
(and that will grow in the future). The fact that news documents present a high degree of
interconnection makes this an interesting and hard problem to solve. The identification
of the structure of the news is accomplished both by descriptive methods that expose the
dimensionality of the relations between different news, and by clustering the news into
topic groups. To achieve this analysis this integrated whole was studied using different
perspectives and approaches.
In the identification of news clusters and structure, and after a preparatory data collection
phase, where several online newspapers from different parts of the globe were
collected, two newspapers were chosen in particular: the Portuguese daily newspaper
Público and the British newspaper The Guardian.
In the first case, it was shown how information theory (namely variation of information)
combined with adaptive networks was able to identify topic clusters in the news published
by the Portuguese online newspaper Público.
In the second case, the structure of news published by the British newspaper The
Guardian is revealed through the construction of time series of news clustered by a kmeans
process. After this approach an unsupervised algorithm, that filters out irrelevant
news published online by taking into consideration the connectivity of the news labels
entered by the journalists, was developed. This novel hybrid technique is based on Qanalysis
for the construction of the filtered network followed by a clustering technique to
identify the topical clusters. Presently this work uses a modularity optimisation clustering technique but this step is general enough that other hybrid approaches can be used without
losing generality.
A novel second order swarm intelligence algorithm based on Ant Colony Systems
was developed for the travelling salesman problem that is consistently better than the
traditional benchmarks. This algorithm is used to construct Hamiltonian paths over the
news published using the eccentricity of the different documents as a measure of distance.
This approach allows for an easy navigation between published stories that is dependent
on the connectivity of the underlying structure.
The results presented in this work show the importance of taking topic detection in
large corpora as a multitude of relations and connectivities that are not in a static state.
They also influence the way of looking at multi-dimensional ensembles, by showing that
the inclusion of the high dimension connectivities gives better results to solving a particular
problem as was the case in the clustering problem of the news published online.Neste trabalho resolvemos o problema da identificação da estrutura das notícias publicadas
em linha por jornais e agências noticiosas. Este problema requer novas abordagens e
algoritmos que sejam capazes de lidar com o número crescente de publicações em linha
(e que se espera continuam a crescer no futuro). Este facto, juntamente com o elevado
grau de interconexão que as notícias apresentam tornam este problema num problema
interessante e de difícil resolução. A identificação da estrutura do sistema de notícias foi
conseguido quer através da utilização de métodos descritivos que expõem a dimensão das
relações existentes entre as diferentes notícias, quer através de algoritmos de agrupamento
das mesmas em tópicos. Para atingir este objetivo foi necessário proceder a ao estudo deste
sistema complexo sob diferentes perspectivas e abordagens.
Após uma fase preparatória do corpo de dados, onde foram recolhidos diversos jornais
publicados online optou-se por dois jornais em particular: O Público e o The Guardian.
A escolha de jornais em línguas diferentes deve-se à vontade de encontrar estratégias de
análise que sejam independentes do conhecimento prévio que se tem sobre estes sistemas.
Numa primeira análise é empregada uma abordagem baseada em redes adaptativas
e teoria de informação (nomeadamente variação de informação) para identificar tópicos
noticiosos que são publicados no jornal português Público.
Numa segunda abordagem analisamos a estrutura das notícias publicadas pelo jornal
Britânico The Guardian através da construção de séries temporais de notícias. Estas foram
seguidamente agrupadas através de um processo de k-means. Para além disso desenvolveuse
um algoritmo que permite filtrar de forma não supervisionada notícias irrelevantes que
apresentam baixa conectividade às restantes notícias através da utilização de Q-analysis
seguida de um processo de clustering. Presentemente este método utiliza otimização de modularidade, mas a técnica é suficientemente geral para que outras abordagens híbridas
possam ser utilizadas sem perda de generalidade do método.
Desenvolveu-se ainda um novo algoritmo baseado em sistemas de colónias de formigas
para solução do problema do caixeiro viajante que consistentemente apresenta resultados
melhores que os tradicionais bancos de testes. Este algoritmo foi aplicado na construção
de caminhos Hamiltonianos das notícias publicadas utilizando a excentricidade obtida a
partir da conectividade do sistema estudado como medida da distância entre notícias. Esta
abordagem permitiu construir um sistema de navegação entre as notícias publicadas que é
dependente da conectividade observada na estrutura de notícias encontrada.
Os resultados apresentados neste trabalho mostram a importância de analisar sistemas
complexos na sua multitude de relações e conectividades que não são estáticas e que
influenciam a forma como tradicionalmente se olha para sistema multi-dimensionais.
Mostra-se que a inclusão desta dimensões extra produzem melhores resultados na resolução
do problema de identificar a estrutura subjacente a este problema da publicação de notícias em linha
- …