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Concurrency and Computation: Practice and Experience

作者： De Salve, Andrea Guidi, Barbara Michienzi, Andrea Consiglio Nazionale delle Ricerche Institute of Applied Sciences and Intelligent Systems Lecce Italy Dipartimento di Informatica Universitã di Pisa Pisa Italy

Online social networks (OSNs) have become one of the most popular platforms where people communicate by sharing contents and personal information. The interactions performed by the users allow to identify the homophily between users and reveal the presence of several communities that could depend on several factors: such as the type of relationships (eg, colleagues and school mates) or to users' preferences (eg, users' interests or hobbies). A very important issue in this scenario is the necessary to characterize such communities by using known real properties or attributes about their members. In this article, we propose an approach that identifies the communities of users by exploiting several community detection algorithms. Afterward, for each user, we exploit decision trees to find a model that describes and distinguishes community affiliations based on known attributes of the members. The evaluation of our approach is derived from a real dataset which consists of the profile information, relationships, and interactions of 95 716 Facebook users. The experimental results show that the proposed approach is able to correctly recognize which attributes of the members properly characterize their corresponding community while ensuring a high level of accuracy (about 85%). © 2020 John Wiley & Sons Ltd.

关键词： Social networking (online) Decision trees community detection algorithms Homophily On line social networks Online social networks (OSNs) Personal information Popular platform Real properties Users' interests

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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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Statistics of a granular cluster ensemble at a liquid-solid-like phase transition

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Physical Review E 2024年第5期109卷 054901-054901页

作者： Enrique Navarro Claudio Falcón Departamento de Física Facultad de Ciencias Físicas y Matemáticas Universidad de Chile Casilla 487-3 Santiago Chile.

We report on the construction of a granular network of particles to study the formation, evolution, and statistical properties of clusters of particles developing at the vicinity of a liquid-solid-like phase transition within a vertically vibrated quasi-two-dimensional granular system. Using the data of particle positions and local order from Castillo et al. [G. Castillo, N. Mujica, and R. Soto, Phys. Rev. Lett. 109, 095701 (2012)], we extract granular clusters taken as communities of the granular network via modularity optimization. Each one of these communities is a patch of particles with a very well defined local orientational order embedded within an array of other patches forming a complex cluster network. The distributions of cluster sizes and lifespans for the cluster network depend on the distance to the liquid-solid-like phase transition of the quasi-two-dimensional granular system. Specifically, the cluster size distribution displays a scale-invariant behavior for at least a decade in cluster sizes, while cluster lifespans grow monotonically with each cluster size. We believe this systematic community analysis for clustering in granular systems can help to study and understand the spatiotemporal evolution of mesoscale structures in systems displaying out-of-equilibrium phase transitions.

关键词： community structure Clusters Dry granular materials Granular materials Vibrofluidized granular materials Cluster models community detection algorithms

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Network homophily via tail inequalities

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Physical Review E 2023年第5期108卷 054130-054130页

作者： Nicola Apollonio Paolo G. Franciosa Daniele Santoni Istituto per le Applicazioni del Calcolo “Mauro Picone” Consiglio Nazionale delle Ricerche Via dei Taurini 19 00185 Rome Italy Dipartimento di Scienze Statistiche Università di Roma “La Sapienza” Piazzale Aldo Moro 5 00185 Rome Italy Istituto di Analisi dei Sistemi ed Informatica “Antonio Ruberti ” Consiglio Nazionale delle Ricerche Via dei Taurini 19 00185 Rome Italy

Homophily is the principle whereby “similarity breeds connections.” We give a quantitative formulation of this principle within networks. Given a network and a labeled partition of its vertices, the vector indexed by each class of the partition, whose entries are the number of edges of the subgraphs induced by the corresponding classes, is viewed as the observed outcome of the random vector described by picking labeled partitions at random among labeled partitions whose classes have the same cardinalities as the given one. This is the recently introduced random coloring model for network homophily. In this perspective, the value of any homophily score Θ, namely, a nondecreasing real-valued function in the sizes of subgraphs induced by the classes of the partition, evaluated at the observed outcome, can be thought of as the observed value of a random variable. Consequently, according to the score Θ, the input network is homophillic at the significance level α whenever the one-sided tail probability of observing a value of Θ at least as extreme as the observed one is smaller than α. Since, as we show, even approximating α is an NP-hard problem, we resort to classical tails inequality to bound α from above. These upper bounds, obtained by specializing Θ, yield a class of quantifiers of network homophily. Computing the upper bounds requires the knowledge of the covariance matrix of the random vector, which was not previously known within the random coloring model. In this paper we close this gap. Interestingly, the matrix depends on the input partition only through the cardinalities of its classes and depends on the network only through its degrees. Furthermore all the covariances have the same sign, and this sign is a graph invariant. Plugging this structure into the bounds yields a meaningful, easy to compute class of indices for measuring network homophily. As demonstrated in real-world network applications, these indices are effective and reliable, and may lead to d

关键词： Assortativity Combinatorics community detection algorithms Computational complexity Data analysis

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Colloquium: Multiscale modeling of brain network organization

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Reviews of Modern Physics 2022年第3期94卷 031002-031002页

作者： Charley Presigny Fabrizio De Vico Fallani Sorbonne Université Institut du Cerveau—Paris Brain Institute—ICM CNRS Inria Inserm AP-HP Hôpital de la Pitié Salpêtrière F-75013 Paris France

A complete understanding of the brain requires an integrated description of the numerous scales and levels of neural organization. This means studying the interplay of genes and synapses, but also the relation between the structure and dynamics of the whole brain, which ultimately leads to different types of behavior, from perception to action, while asleep or awake. Yet multiscale brain modeling is challenging, in part because of the difficulty to simultaneously access information from multiple scales and levels. While some insight has been gained on the role of specific microcircuits on the generation of macroscale brain activity, a comprehensive characterization of how changes occurring at one scale or level can have an impact on other ones remains poorly understood. Recent efforts to address this gap include the development of new frameworks originating mostly from network science and complex systems theory. These theoretical contributions provide a powerful framework to analyze and model interconnected systems exhibiting interactions within and between different layers of information. Recent advances for the characterization of the multiscale brain organization in terms of structure-function, oscillation frequencies, and temporal evolution are presented. Efforts are reviewed on the multilayer network properties underlying the physics of higher-order organization of neuronal assemblies, as well as on the identification of multimodal network-based biomarkers of brain pathologies such as Alzheimer’s disease. This Colloquium concludes with a perspective discussion of how recent results from multilayer network theory, involving generative modeling, controllability, and machine learning, could be adopted to address new questions in modern physics and neuroscience.

关键词： Network phase transitions Random walks Brain network Coupled oscillators Multilayer & multiplex networks community detection algorithms Neuroimaging Neuronal network models

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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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Hierarchical clustering of bipartite data sets based on the statistical significance of coincidences

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Physical Review E 2020年第4期102卷 042304-042304页

作者： Ignacio Tamarit María Pereda José A. Cuesta Grupo Interdisciplinar de Sistemas Complejos (GISC) Departamento de Matemáticas de la Universidad Carlos III de Madrid Leganés Spain Unidad Mixta Interdisciplinar de Comportamiento y Complejidad Social (UMICCS) Madrid Spain Grupo de Investigación Ingeniería de Organización y Logística (IOL) Escuela Técnica Superior de Ingenieros Industriales Universidad Politécnica de Madrid Madrid Spain Instituto de Biocomputación y Física de Sistemas Complejos (BIFI) Universidad de Zaragoza Zaragoza Spain UC3M-Santander Big Data Institute (IBiDat) Getafe Spain

When some ‘entities’ are related by the ‘features’ they share they are amenable to a bipartite network representation. Plant-pollinator ecological communities, co-authorship of scientific papers, customers and purchases, or answers in a poll, are but a few examples. Analyzing clustering of such entities in the network is a useful tool with applications in many fields, like internet technology, recommender systems, or detection of diseases. The algorithms most widely applied to find clusters in bipartite networks are variants of modularity optimization. Here, we provide a hierarchical clustering algorithm based on a dissimilarity between entities that quantifies the probability that the features shared by two entities are due to mere chance. The algorithm performance is O(n2) when applied to a set of n entities, and its outcome is a dendrogram exhibiting the connections of those entities. Through the introduction of a ‘susceptibility’ measure we can provide an ‘optimal’ choice for the clustering as well as quantify its quality. The dendrogram reveals further useful structural information though—like the existence of subclusters within clusters or of nodes that do not fit in any cluster. We illustrate the algorithm by applying it first to a set of synthetic networks, and then to a selection of examples. We also illustrate how to transform our algorithm into a valid alternative for one-mode networks as well, and show that it performs at least as well as the standard, modularity-based algorithms—with a higher numerical performance. We provide an implementation of the algorithm in python freely accessible from GitHub.

关键词： community structure Bipartite graphs Real world networks community detection algorithms Data analysis

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Large deviations of semisupervised learning in the stochastic block model

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Physical Review E 2022年第3期105卷 034108-034108页

作者： Hugo Cui Luca Saglietti Lenka Zdeborová SPOC Laboratory Physics Department École Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland

In semisupervised community detection, the membership of a set of revealed nodes is known in addition to the graph structure and can be leveraged to achieve better inference accuracies. While previous works investigated the case where the revealed nodes are selected at random, this paper focuses on correlated subsets leading to atypically high accuracies. In the framework of the dense stochastic block model, we employ statistical physics methods to derive a large deviation analysis of the number of these rare subsets, as characterized by their free energy. We find theoretical evidence of a nonmonotonic relationship between reconstruction accuracy and the free energy associated to the posterior measure of the inference problem. We further discuss possible implications for active learning applications in community detection.

关键词： Disordered systems community detection algorithms

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Bayan algorithm: Detecting communities in networks through exact and approximate optimization of modularity

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Physical Review E 2024年第4期110卷 044315页

作者： Samin Aref Mahdi Mostajabdaveh Hriday Chheda Department of Mechanical and Industrial Engineering Department of Mathematical and Industrial Engineering

community detection is a classic network problem with extensive applications in various fields. Its most common method is using modularity maximization heuristics which rarely return an optimal partition or anything similar. Partitions with globally optimal modularity are difficult to compute, and therefore have been underexplored. Using structurally diverse networks, we compare 30 community detection methods including our proposed algorithm that offers optimality and approximation guarantees: the Bayan algorithm. Unlike existing methods, Bayan globally maximizes modularity or approximates it within a factor. Our results show the distinctive accuracy and stability of maximum-modularity partitions in retrieving planted partitions at rates higher than most alternatives for a wide range of parameter settings in two standard benchmarks. Compared to the partitions from 29 other algorithms, maximum-modularity partitions have the best medians for description length, coverage, performance, average conductance, and well clusteredness. These advantages come at the cost of additional computations which Bayan makes possible for small networks (networks that have up to 3000 edges in their largest connected component). Bayan is several times faster than using open-source and commercial solvers for modularity maximization, making it capable of finding optimal partitions for instances that cannot be optimized by any other existing method. Our results point to a few well-performing algorithms, among which Bayan stands out as the most reliable method for small networks. A python implementation of the Bayan algorithm (bayanpy) is publicly available through the package installer for python.

关键词： Clustering community structure Network optimization Network structure community detection algorithms Non-overlapping communities

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Mutual information and the encoding of contingency tables

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Physical Review E 2024年第6期110卷 064306-064306页

作者： Maximilian Jerdee Alec Kirkley M. E. J. Newman Department of Physics University of Michigan Ann Arbor Michigan 48109 USA Institute of Data Science University of Hong Kong Hong Kong Department of Urban Planning and Design University of Hong Kong Hong Kong Urban Systems Institute University of Hong Kong Hong Kong Center for the Study of Complex Systems University of Michigan Ann Arbor Michigan 48109 USA

Mutual information is commonly used as a measure of similarity between competing labelings of a given set of objects, for example to quantify performance in classification and community detection tasks. As argued recently, however, the mutual information as conventionally defined can return biased results because it neglects the information cost of the so-called contingency table, a crucial component of the similarity calculation. In principle the bias can be rectified by subtracting the appropriate information cost, leading to the modified measure known as the reduced mutual information, but in practice one can only ever compute an upper bound on this information cost, and the value of the reduced mutual information depends crucially on how good a bound is established. In this paper we describe an improved method for encoding contingency tables that gives a substantially better bound in typical use cases and approaches the ideal value in the common case where the labelings are closely similar, as we demonstrate with extensive numerical results.

关键词： community structure community detection algorithms Information theory

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