444 research outputs found
Evaluating Local Community Methods in Networks
We present a new benchmarking procedure that is unambiguous and specific to
local community-finding methods, allowing one to compare the accuracy of
various methods. We apply this to new and existing algorithms. A simple class
of synthetic benchmark networks is also developed, capable of testing
properties specific to these local methods.Comment: 8 pages, 9 figures, code included with sourc
Structural Inference of Hierarchies in Networks
One property of networks that has received comparatively little attention is
hierarchy, i.e., the property of having vertices that cluster together in
groups, which then join to form groups of groups, and so forth, up through all
levels of organization in the network. Here, we give a precise definition of
hierarchical structure, give a generic model for generating arbitrary
hierarchical structure in a random graph, and describe a statistically
principled way to learn the set of hierarchical features that most plausibly
explain a particular real-world network. By applying this approach to two
example networks, we demonstrate its advantages for the interpretation of
network data, the annotation of graphs with edge, vertex and community
properties, and the generation of generic null models for further hypothesis
testing.Comment: 8 pages, 8 figure
Robust Detection of Hierarchical Communities from Escherichia coli Gene Expression Data
Determining the functional structure of biological networks is a central goal
of systems biology. One approach is to analyze gene expression data to infer a
network of gene interactions on the basis of their correlated responses to
environmental and genetic perturbations. The inferred network can then be
analyzed to identify functional communities. However, commonly used algorithms
can yield unreliable results due to experimental noise, algorithmic
stochasticity, and the influence of arbitrarily chosen parameter values.
Furthermore, the results obtained typically provide only a simplistic view of
the network partitioned into disjoint communities and provide no information of
the relationship between communities. Here, we present methods to robustly
detect coregulated and functionally enriched gene communities and demonstrate
their application and validity for Escherichia coli gene expression data.
Applying a recently developed community detection algorithm to the network of
interactions identified with the context likelihood of relatedness (CLR)
method, we show that a hierarchy of network communities can be identified.
These communities significantly enrich for gene ontology (GO) terms, consistent
with them representing biologically meaningful groups. Further, analysis of the
most significantly enriched communities identified several candidate new
regulatory interactions. The robustness of our methods is demonstrated by
showing that a core set of functional communities is reliably found when
artificial noise, modeling experimental noise, is added to the data. We find
that noise mainly acts conservatively, increasing the relatedness required for
a network link to be reliably assigned and decreasing the size of the core
communities, rather than causing association of genes into new communities.Comment: Due to appear in PLoS Computational Biology. Supplementary Figure S1
was not uploaded but is available by contacting the author. 27 pages, 5
figures, 15 supplementary file
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.
Multi-objective NSGA-II based community detection using dynamical evolution social network
Community detection is becoming a highly demanded topic in social networking-based applications. It involves finding the maximum intraconnected and minimum inter-connected sub-graphs in given social networks. Many approaches have been developed for communityโs detection and less of them have focused on the dynamical aspect of the social network. The decision of the community has to consider the pattern of changes in the social network and to be smooth enough. This is to enable smooth operation for other community detection dependent application. Unlike dynamical community detection Algorithms, this article presents a non-dominated aware searching Algorithm designated as non-dominated sorting based community detection with dynamical awareness (NDS-CD-DA). The Algorithm uses a non-dominated sorting genetic algorithm NSGA-II with two objectives: modularity and normalized mutual information (NMI). Experimental results on synthetic networks and real-world social network datasets have been compared with classical genetic with a single objective and has been shown to provide superiority in terms of the domination as well as the convergence. NDS-CD-DA has accomplished a domination percentage of 100% over dynamic evolutionary community searching DECS for almost all iterations
Hierarchical community structure in networks
Modular and hierarchical structures are pervasive in real-world complex
systems. A great deal of effort has gone into trying to detect and study these
structures. Important theoretical advances in the detection of modular, or
"community", structures have included identifying fundamental limits of
detectability by formally defining community structure using probabilistic
generative models. Detecting hierarchical community structure introduces
additional challenges alongside those inherited from community detection. Here
we present a theoretical study on hierarchical community structure in networks,
which has thus far not received the same rigorous attention. We address the
following questions: 1)~How should we define a valid hierarchy of communities?
2)~How should we determine if a hierarchical structure exists in a network? and
3)~how can we detect hierarchical structure efficiently? We approach these
questions by introducing a definition of hierarchy based on the concept of
stochastic externally equitable partitions and their relation to probabilistic
models, such as the popular stochastic block model. We enumerate the challenges
involved in detecting hierarchies and, by studying the spectral properties of
hierarchical structure, present an efficient and principled method for
detecting them.Comment: 22 pages, 12 figure
Passenger Flows in the Metropolitan Seoul Public Transportation: Maximum Spanning Tree and Community Detection
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ผ๋ฌธ (์์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ๋ฌผ๋ฆฌยท์ฒ๋ฌธํ๋ถ(๋ฌผ๋ฆฌํ์ ๊ณต), 2013. 2. ์ต๋ฌด์.๋ณธ ๋
ผ๋ฌธ์์๋ ์์ธ ์๋๊ถ์ ๋์ค๊ตํต ์์คํ
์์์ ์น๊ฐ ํ๋ฆ์ ์ต๋์ ์ฅ๋๋ฌด์ ์ง์ญ์ฌํ ์ฐพ๊ธฐ๋ฅผ ํตํด ์ดํด๋ณด์๋ค. ๋์ค๊ตํต ์๋จ์ธ ๋ฒ์ค์ ์งํ์ฒ ์ ์ฌ๋๋ค์ ์ฃผ๋ ๊ตํต ์๋จ์ผ๋ก ์ ๊ณต๋๋ค. ์ฌ๋๋ค์ ์์ง์์ ํน์ฑ์ ์น๊ฐ ํ๋ฆ ๋ฐ์ดํฐ๋ฅผ ํตํด ๋ถ์ํ ์ ์๋ค. ์ด ๋
ผ๋ฌธ์์๋ ์ญ์ ์ถ๋ฐ์ญ๊ณผ ๋์ฐฉ์ญ์ผ๋ก ๋๋์ด ์น๊ฐํ๋ฆ์ ์ต๋์ ์ฅ๋๋ฌด๋ฅผ ๊ตฌ์ฑํ์๋ค. ์ต๋์ ์ฅ๋๋ฌด์์ ์ถ๋ฐ์ญ๊ณผ ๋์ฐฉ์ญ์ ์ฐ๊ฒฐ ์ ๋ถํฌ๋ ๊ฑฐ๋ญ์ ๊ณฑ ๋ฒ์น์ ๋ฐ๋ฅด๋ฉฐ ์๊ฐ๋์ ๋ฐ๋ผ ์ง์๋ ๋ฌ๋๋ค. ์ด ๋
ผ๋ฌธ์์๋ ๋ํ ์์ธ ์๋๊ถ์ ์ง์ญ์ฌํ ๊ตฌ์กฐ๋ฅผ ์ฐ๊ตฌํ์๋ค. ๋ฒ์ค์ ์งํ์ฒ ์ญ์ ์ฌ๊ฐ ๊ฒฉ์๋ก ๋์ถฉ ๊ฐ๊ธฐ ํ ๋ค ๋ชจ๋๋ด๋ฆฌํฐ ์ต๋ํ๋ฅผ ๋์
ํ์ฌ ์ฌ๊ฐ ์์ญ์ผ๋ก ํํ๋๋ ๋
ธ๋๋ค๋ก ๊ตฌ๋ถ๋ ์ง์ญ์ฌํ๋ฅผ ๊ตฌํ์๋ค. ๋จ์ผ ์ฐ๊ฒฐ ํฉ์น๊ธฐ ๋ฐฉ๋ฒ๊ณผ ๋ถํ ๋ฐ๋ ์ต๋ํ๋ฅผ ํตํด ๊ตฌ๋ถ๋ ๋งํฌ๋ค์ ์ง์ญ์ฌํ๋ฅผ ๊ตฌํ์ฌ ์ค์ฒฉ๋ ์ง์ญ์ฌํ๋ฅผ ๋ฐ๊ฒฌํ์๋ค. ํ ์ง์ญ์ฌํ ์์ ๋
ธ๋ ์์ ๋งํฌ ์์ ๋ถํฌ๋ ์ญ์ ๊ฑฐ๋ญ์ ๊ณฑ ๋ฒ์น์ ๋ฐ๋ฅธ๋ค. ์ด ์ง์ญ์ฌํ๋ค์ ์ฌ๋๋ค์ ์ค์ ์ด๋ ์๋ฃ๋ฅผ ํตํด ์ฐพ์๋ค๋ ์ธก๋ฉด์์ ์ค์ ์์ธ ์๋๊ถ์ ์ง์ญ์ฌํ ํน์ฑ์ ๋ณด์ฌ์ค๋ค๊ณ ์๊ฐํ ์ ์๋ค.In this thesis, passenger flow of Metropolitan Seoul public transportation system is examined through maximum spanning tree and community detection. The public transportation system, consisting bus and subway, provides major transportation modes to the people. The characteristic of movement of people can be analyzed by the passenger flow data of public transportation system. We divide one station by departure station and arrival station and construct maximum spanning tree of the passenger flow. The degree distribution of the departure stations and arrival stations in the maximum spanning tree follows power law with different exponent according to the time zones. We also investigate the community structure of Metropolitan Seoul. The bus and subway stations are coarse grained by square grid and the modularity maximization method using simulated annealing is employed first to find disjoint node(square area) communities. The disjoint link community, using single-linkage agglomerate method and partition density maximization, is also found to reveal overlapped communities. The distribution of number of links and nodes per community in the disjoint link community also follows power law distribution. These communities can be regarded showing real community character of Metropolitan Seoul in the sense of the lexical meaning of community.1 Introduction 1
2 Maximum Spanning Trees of Passenger Flow 4
2.1 Introduction 4
2.2 Method 5
2.3 Results 6
3 Community Detection 14
3.1 Introduction 14
3.2 Method 16
3.2.1 Modularity maximization by simulated annealing 16
3.2.2 Overlapped community detection: Disjoint link community 17
3.3 Results 18
3.3.1 Modularity maximization by simulated annealing 18
3.3.2 Overlapped community detection: Disjoint link community 19
4 Summary 27Maste
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