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Segmentation of large images based on super-pixels and community detection in graphs

IET IMAGE PROCESSING 2017年第12期11卷 1219-1228页

作者： Linares, Oscar A. C. Botelho, Glenda Michele Rodrigues, Francisco Aparecido Batista Neto, Joao Univ Sao Paulo Inst Ciencias Matemat & Comp Campus Sao CarlosCaixa Postal 668 BR-13560970 Sao Carlos SP Brazil Univ Fed Tocantins Palmas Brazil

Image segmentation has many applications which range from machine learning to medical diagnosis. In this study, the authors propose a framework for the segmentation of images based on super-pixels and algorithms for community identification in graphs. The super-pixel pre-segmentation step reduces the number of nodes in the graph, rendering the method the ability to process large images. Moreover, community detection algorithms provide more accurate segmentation than traditional approaches based on spectral graph partition. The authors also compared their method with two algorithms: (i) the graph-based approach by Felzenszwalb and Huttenlocher and (ii) the contour-based method by Arbelaez. Results have shown that their method provides more precise segmentation and is faster than both of them.

关键词： image segmentation graph theory object detection contour-based method graph-based approach spectral graph partition community detection algorithms super-pixel pre-segmentation step medical diagnosis machine learning image segmentation

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An efficient method for mining the maximal alpha-quasi-clique-community of a given node in complex networks

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SOCIAL NETWORK ANALYSIS AND MINING 2018年第1期8卷 1-18页

作者： Conde-Cespedes, Patricia Ngonmang, Blaise Viennet, Emmanuel ISEP LISITE 10 Rue Vanves F-92130 Issy Les Moulineaux France Univ Paris 13 L2TI 99 Ave Jean Baptiste Clement F-93430 Villetaneuse France

Detecting communities in large complex networks is important to understand their structure and to extract features useful for visualization or prediction of various phenomena like the diffusion of information or the dynamic of the network. A community is defined by a set of strongly interconnected nodes. An alpha-quasi-clique is a group of nodes where each member is connected to more than a proportion alpha of the other nodes. By construction, an alpha-quasi-clique has a density greater than alpha. The size of an alpha-quasi-clique is limited by the degree of its nodes. In complex networks whose degree distribution follows a power law, usually alpha-quasi-cliques are small sets of nodes for high values of alpha. In this paper, we present an efficient method for finding the maximal alpha-quasi-clique of a given node in the network. Therefore, the resulting communities of our method have two main characteristics: they are alpha-quasi-cliques (very dense for high alpha) and they are local to the given node. Detecting the local community of specific nodes is very important for applications dealing with huge networks, when iterating through all nodes would be impractical or when the network is not entirely known. The proposed method, called RANK-NUM-NEIGHS (RNN), is evaluated experimentally on real and computer-generated networks in terms of quality (community size), execution time and stability. We also provide an upper bound on the optimal solution.

关键词： community detection algorithms Maximal alpha-quasi-clique Local community detection Complex networks Density

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Revealing in-block nestedness: detection and benchmarking

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Physical Review E 2018年第6期97卷 062302-062302页

作者： Albert Solé-Ribalta Claudio J. Tessone Manuel S. Mariani Javier Borge-Holthoefer Internet Interdisciplinary Institute (IN3) Universitat Oberta de Catalunya 08860 Barcelona Catalonia Spain and Institute for Biocomputation and Physics of Complex Systems (BIFI) Universidad de Zaragoza 50018 Zaragoza Spain

As new instances of nested organization—beyond ecological networks—are discovered, scholars are debating the coexistence of two apparently incompatible macroscale architectures: nestedness and modularity. The discussion is far from being solved, mainly for two reasons. First, nestedness and modularity appear to emerge from two contradictory dynamics, cooperation and competition. Second, existing methods to assess the presence of nestedness and modularity are flawed when it comes to the evaluation of concurrently nested and modular structures. In this work, we tackle the latter problem, presenting the concept of in-block nestedness, a structural property determining to what extent a network is composed of blocks whose internal connectivity exhibits nestedness. We then put forward a set of optimization methods that allow us to identify such organization successfully, in synthetic and in a large number of real networks. These findings challenge our understanding of the topology of ecological and social systems, calling for new models to explain how such patterns emerge.

关键词： community structure Network structure Patterns in complex systems Social systems Topological tools Bipartite graphs Ecological networks Real world networks Social networks community detection algorithms Network Models Networks Analysis Tools Non-overlapping communities

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Algorithmic detectability threshold of the stochastic block model

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Physical Review E 2018年第3期97卷 032301-032301页

作者： Tatsuro Kawamoto Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology 2-3-26 Aomi Koto-ku Tokyo Japan

The assumption that the values of model parameters are known or correctly learned, i.e., the Nishimori condition, is one of the requirements for the detectability analysis of the stochastic block model in statistical inference. In practice, however, there is no example demonstrating that we can know the model parameters beforehand, and there is no guarantee that the model parameters can be learned accurately. In this study, we consider the expectation–maximization (EM) algorithm with belief propagation (BP) and derive its algorithmic detectability threshold. Our analysis is not restricted to the community structure but includes general modular structures. Because the algorithm cannot always learn the planted model parameters correctly, the algorithmic detectability threshold is qualitatively different from the one with the Nishimori condition.

关键词： community structure Block models community detection algorithms

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Magnetic eigenmaps for community detection in directed networks

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Physical Review E 2017年第2期95卷 022302-022302页

作者： Michaël Fanuel Carlos M. Alaíz Johan A. K. Suykens Department of Electrical Engineering (ESAT) and STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Kasteelpark Arenberg 10 B-3001 Leuven Belgium

Communities in directed networks have often been characterized as regions with a high density of links, or as sets of nodes with certain patterns of connection. Our approach for community detection combines the optimization of a quality function and a spectral clustering of a deformation of the combinatorial Laplacian, the so-called magnetic Laplacian. The eigenfunctions of the magnetic Laplacian, which we call magnetic eigenmaps, incorporate structural information. Hence, using the magnetic eigenmaps, dense communities including directed cycles can be revealed as well as “role” communities in networks with a running flow, usually discovered thanks to mixture models. Furthermore, in the spirit of the Markov stability method, an approach for studying communities at different energy levels in the network is put forward, based on a quantum mechanical system at finite temperature.

关键词： Deterministic networks Directed networks Real world networks community detection algorithms Cycles Diffusion & random walks

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Stochastic graph Voronoi tessellation reveals community structure

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Physical Review E 2017年第2期95卷 022306-022306页

作者： Zsolt I. Lázár István Papp Levente Varga Ferenc Járai-Szabó Dávid Deritei Mária Ercsey-Ravasz Faculty of Physics Babeş-Bolyai University Cluj-Napoca Romania Department of Network Science Central European University Hungary Romanian Institute of Science and Technology Cluj-Napoca Romania

Given a network, the statistical ensemble of its graph-Voronoi diagrams with randomly chosen cell centers exhibits properties convertible into information on the network's large scale structures. We define a node-pair level measure called Voronoi cohesion which describes the probability for sharing the same Voronoi cell, when randomly choosing g centers in the network. This measure provides information based on the global context (the network in its entirety), a type of information that is not carried by other similarity measures. We explore the mathematical background of this phenomenon and several of its potential applications. A special focus is laid on the possibilities and limitations pertaining to the exploitation of the phenomenon for community detection purposes.

关键词： community structure Stochastic processes community detection algorithms

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Efficient community detection of network flows for varying Markov times and bipartite networks

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Physical Review E 2016年第3期93卷 032309-032309页

作者： Masoumeh Kheirkhahzadeh Andrea Lancichinetti Martin Rosvall Department of IT and Computer Engineering Iran University of Science and Technology Teheran Iran Integrated Science Lab Department of Physics Umeå University SE-901 87 Umeå Sweden

community detection of network flows conventionally assumes one-step dynamics on the links. For sparse networks and interest in large-scale structures, longer timescales may be more appropriate. Oppositely, for large networks and interest in small-scale structures, shorter timescales may be better. However, current methods for analyzing networks at different timescales require expensive and often infeasible network reconstructions. To overcome this problem, we introduce a method that takes advantage of the inner workings of the map equation and evades the reconstruction step. This makes it possible to efficiently analyze large networks at different Markov times with no extra overhead cost. The method also evades the costly unipartite projection for identifying flow modules in bipartite networks.

关键词： Network flow community detection algorithms

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community detection in networks with unequal groups

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Physical Review E 2016年第1期93卷 012303-012303页

作者： Pan Zhang Cristopher Moore M. E. J. Newman Santa Fe Institute Santa Fe New Mexico 87501 USA State Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing 100190 China Physics Department and the Center for the Study of Complex Systems University of Michigan Ann Arbor Michigan 48109 USA

Recently, a phase transition has been discovered in the network community detection problem below which no algorithm can tell which nodes belong to which communities with success any better than a random guess. This result has, however, so far been limited to the case where the communities have the same size or the same average degree. Here we consider the case where the sizes or average degrees differ. This asymmetry allows us to assign nodes to communities with better-than-random success by examining their local neighborhoods. Using the cavity method, we show that this removes the detectability transition completely for networks with four groups or fewer, while for more than four groups the transition persists up to a critical amount of asymmetry but not beyond. The critical point in the latter case coincides with the point at which local information percolates, causing a global transition from a less-accurate solution to a more-accurate one.

关键词： Network phase transitions Cavity methods community detection algorithms

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Overlapping community detection using neighborhood ratio matrix

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2015年 421卷 510-521页

作者： Eustace, Justine Wang, Xingyuan Cui, Yaozu Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian 116024 Peoples R China

The participation of a node in more than one community is a common phenomenon in complex networks. However most existing methods, fail to identify nodes with multiple community affiliation, correctly. In this paper, a unique method to define overlapping community in complex networks is proposed, using the overlapping neighborhood ratio to represent relations between nodes. Matrix factorization is then utilized to assign nodes into their corresponding community structures. Moreover, the proposed method demonstrates the use of Perron clusters to estimate the number of overlapping communities in a network. Experimental results in real and artificial networks show, with great accuracy, that the proposed method succeeds to recover most of the overlapping communities existing in the network. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Neighboring ratio Complex networks community detection algorithms Data mining Peron clusters

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Semantic Divergence based Evaluation of Web Service Communities

Semantic Divergence based Evaluation of Web Service Communit...

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13th IEEE International Conference on Services Computing (SCC)

作者： Naim, Hafida Aznag, Mustapha Quafafou, Mohamed Durand, Nicolas Aix Marseille Univ CNRS UMR 7296 LSIS F-13397 Marseille France

ISBN: (纸本)9781509026289

The number of community detection algorithms is growing continuously adopting a topological based approach to discover optimal subgraphs or communities. In this paper, we propose a new method combining both topology and semantic to evaluate and rank community detection algorithms. To achieve this goal we consider a probabilistic topic based approach to define a new measure called semantic divergence of communities. Combining this measure with others related to prior knowledge, we compute a score for each algorithm to evaluate the effectiveness of its communities and propose a ranking method. We have evaluated our approach considering communities of real web services.

关键词： community detection algorithms Web services Service communities Topic models Semantic divergence Dependency network

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