Understanding the complex dynamics of communities of software developers requires a view of such organizations as a network of interacting agents involving both goals and constraints. Beyond their special features, these systems display some overall patterns of organization not far from the ones seen in other types of organizations, including both natural and artificial entities. By looking at both software developers and social insects as agents interacting in a complex network, we found common statistical patterns of organization. Here, simple self-organizing processes leading to the formation of hierarchies in wasp colonies and open source communities are studied. Our analysis further validates simple models of formation of wasp hierarchies based on individual learning. In the open source community, a few members are clearly distinguished from the rest of the community with different reinforcement mechanisms.
In network science, a group of nodes connected with each other at higher probability than with those outside the group is referred to as a community. From the perspective that individual communities are associated with functional modules constituting complex systems described by networks, discovering communities is primarily important for understanding overall functions of these systems. Much effort has been devoted to developing methods to detect communities in networks since the early days of network science. Nevertheless, the method to reveal key characteristics of communities in real-world…
Organization of networks with tagged nodes and biased links: a priori distinct communities. The case of Intelligent Design Proponents and Darwinian Evolution Defenders
Weaker Coherence, Weaker Reciprocity: Comparing the Semantic and Social Organization of Moltbook and Reddit
Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales（布宜诺斯艾利斯大学，理学院）；Instituto de Física Interdisciplinaria y Aplicada (INFINA), CONICET – Universidad de Buenos Aires（跨学科与应用物理研究所 (INFINA)，阿根廷国家科学研究委员会 - 布宜诺斯艾利斯大学）；Instituto Balseiro, Universidad Nacional de Cuyo y Comisión Nacional de Energía Atómica（巴尔塞罗研究所，门多萨国立大学与阿根廷国家原子能委员会）；Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)（阿根廷国家科学研究委员会 (CONICET)）
Social networks are typical attributed networks with node attributes. Different from traditional attribute community detection problem aiming at obtaining the whole set of communities in the network, we study an application-oriented problem of mining an application-aware community organization with respect to specific concerned attributes. The concerned attributes are designated based on the requirements of any application by a user in advance. The application-aware community organization w.r.t. concerned attributes consists of the communities with feature subspaces containing these concerned …
What defines a group of friends? Rethinking community structure in signed, directed networks
Grupo Interdisciplinar de Sistemas Complejos (GISC), Departamento de Matemáticas, Universidad Carlos III de Madrid（马德里卡洛斯三世大学复杂系统跨学科小组数学系）；Instituto de Biocomputación y Física de Sistemas Complejos (BIFI), Universidad de Zaragoza（萨拉戈萨大学生物计算与复杂系统物理研究所）；Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington（印第安纳大学布鲁明顿分校卢迪信息、计算与工程学院复杂网络与系统研究中心）
The hidden metric space behind complex network topologies is a fervid topic in current network science and the hyperbolic space is one of the most studied, because it seems associated to the structural organization of many real complex systems. The Popularity-Similarity-Optimization (PSO) model simulates how random geometric graphs grow in the hyperbolic space, reproducing strong clustering and scale-free degree distribution, however it misses to reproduce an important feature of real complex networks, which is the community organization. The Geometrical-Preferential-Attachment (GPA) model was…
We present a novel approach to partitioning network nodes into non-overlapping communities - a key step in revealing network modularity and hierarchical organization. Our methodology, applicable to networks with both weighted and unweighted symmetric edges, uses random walks to explore neighboring nodes in the same community. The walk-likelihood algorithm (WLA) produces an optimal partition of network nodes into a given number of communities. The walk-likelihood community finder (WLCF) employs WLA to predict both the optimal number of communities and the corresponding network partition. We hav…
Diffusion-driven pattern formation in an opinion dynamical network model
Helmholtz Institute for Functional Marine Biodiversity (HIFMB)（赫尔姆霍兹功能海洋生物多样性研究所）；Carl-von-Ossietzky University, Institute for Chemistry and Biology of the Marine Environment（卡尔·冯·奥西茨基大学，海洋环境化学与生物学研究所）；Alfred-Wegener Institute (AWI), Helmholtz Center for Polar and Marine Research（阿尔弗雷德·魏格纳研究所（AWI），赫尔姆霍兹极地海洋研究中心）
Network analysis is becoming increasingly relevant in the historical investigation of scientific communities and their knowledge circulation process, because it offers the opportunity to explore and visualize connections amog scientific actors on a scale qualitatively different from traditional historical methods. Temporal networks are especially suitable for this task, as they allow to investigate the evolution of scientific communities over time. In this paper we will rely on the analytical tools provided by temporal networks to examine the technical comission on agriculture (1913 - 1947) es…
Community detection algorithms are fundamental tools to understand organizational principles in social networks. With the increasing power of social media platforms, when detecting communities there are two possi- ble sources of information one can use: the structure of social network and node attributes. However structure of social networks and node attributes are often interpreted separately in the research of community detection. When these two sources are interpreted simultaneously, one common as- sumption shared by previous studies is that nodes attributes are correlated with communities.…
Business elites reconfigure their locus of organization over time, from the city level, to the national level, and beyond. We ask what the current level of elite organization is and propose a novel theoretical and empirical approach to answer this question. Building on the universal distinction between local and nonlocal ties we use network analysis and community detection to dissect the global network of interlocking directorates among over five million firms. We find that elite orientation is indeed changing from the national to the transnational plane, but we register a considerable heterog…
Inter-firm organizations, which play a driving role in the economy of a country, can be represented in the form of a customer-supplier network. Such a network exhibits a heavy-tailed degree distribution, disassortative mixing and a prominent community structure. We analyze a large-scale data set of customer-supplier relationships containing data from one million Japanese firms. Using a directed network framework, we show that the production network exhibits the characteristics listed above. We conduct detailed investigations to characterize the communities in the network. The topology within s…
Nestedness has traditionally been used to detect assembly patterns in meta-communities and networks of interacting species. Attempts have also been made to uncover nested structures in international trade, typically represented as bipartite networks in which connections can be established between countries (exporters or importers) and industries. A bipartite representation of trade, however, inevitably neglects transactions between industries. To fully capture the organization of the global value chain, we draw on the World Input-Output Database and construct a multi-layer network in which the…
By mapping the most advanced elements of the contemporary social interactions, the world scientific collaboration network develops an extremely involved and heterogeneous organization. Selected characteristics of this heterogeneity are studied here and identified by focusing on the scientific collaboration community of H. Eugene Stanley - one of the most prolific world scholars at the present time. Based on the Web of Science records as of March 28, 2016, several variants of networks are constructed. It is found that the Stanley #1 network - this in analogy to the Erdős # - develops a largely …
Community Detection with the Map Equation and Infomap: Theory and Applications
IceLab, Department of Physics, Umeå University（于默奥大学物理系冰实验室）；Scientific Computing Laboratory, Center for the Study of Complex Systems, Institute of Physics Belgrade, University of Belgrade（贝尔格莱德大学贝尔格莱德物理研究所复杂系统研究中心科学计算实验室）
Revealing the community structure exhibited by real networks is a fundamental phase towards a comprehensive understanding of complex systems beyond the local organization of their components. Community detection techniques help on providing insights into understanding the local organization of the components of networks. In this study we identify and investigate the overlapping community structure of an interesting and unique case of study: the Estonian network of payments. In order to perform the study, we use the Clique Percolation Method and explore statistical distribution functions of the…
We propose a method to decompose dynamical systems based on the idea that modules constrain the spread of perturbations. We find partitions of system variables that maximize 'perturbation modularity', defined as the autocovariance of coarse-grained perturbed trajectories. The measure effectively separates the fast intramodular from the slow intermodular dynamics of perturbation spreading (in this respect, it is a generalization of the 'Markov stability' method of network community detection). Our approach captures variation of modular organization across different system states, time scales, a…
