Archive for the ‘Networks’ Category
Over the last decades, the idea that communication constitutes organizations (CCO) has been gaining considerable momentum in organization studies. The CCO perspective provides new insights into key organizational issues, such as the relation between stability and change, between micro-level and macro-level phenomena, or between emergence and control. However, despite various theoretical advancements, the CCO perspective’s range of methodologies is still limited to analyzing local communication episodes, rather than studying organizations as broader networks of communication episodes. In this paper, we present a new methodological approach to the study of the relation between organization and communication, based on network analysis. Following a discussion of existing network approaches, we incorporate the fundamental assumptions of the CCO perspective into a methodology that places communication at the center of network analysis by turning the prevalent network perspective inside out, so that the vertices of the network represent communication episodes and the edges represent individuals. We illustrate our methodology with an empirical case study, in which we examine the structures and dynamics of an actual organization as a network of communication episodes.
This paper reviews the general philosophy underlying the transdisciplinary research in the Evolution, Complexity and Cognition (ECCO) group. The ECCO conceptual framework is based on an ontology of action: the fundamental constituents of reality are seen as actions and the agents that produce them. More complex phenomena are conceived as self-organizing networks of interacting agents that evolve to become increasingly complex, adaptive and intelligent systems. The resulting worldview allows us to address the most fundamental issues of philosophy, including metaphysics, epistemology, ethics, futurology and praxeology. It in particular tackles the recurrent issues surrounding the matter-mind duality, including the origins of purposefulness and of subjective experience, and the relation between first-person and third-person perspectives. It achieves this by extending the intentional stance down to the simplest agents, elementary particles. This action-based view moreover supports a variety of practical applications, including the design of self-organizing technological systems, of systems that mobilize people to work in a motivated and coordinated manner, and of systems that support the collaborative development and dissemination of knowledge networks. The appendix of the paper, which is structured as a glossary, systematically defines and surveys the fundamental concepts of the ECCO framework.
Change is hard, especially in a large organization. Numerous studies have shown that employees tend instinctively to oppose change initiatives because they disrupt established power structures and ways of getting things done. However, some leaders do succeed—often spectacularly—at transforming their workplaces. What makes them able to exert this sort of influence when the vast majority can’t? All of our findings underscore the importance of networks in influencing change. First, formal authority may give you the illusion of power, but informal networks always matter, whether you are the boss or a middle manager. Second, think about what kind of network you have—or your appointed change agent has—and make sure it matches the type of change you’re after. A bridging network helps drive divergent change; a cohesive network is preferable for nondivergent change. Third, always identify and cultivate fence-sitters, but handle resisters on a case by case basis. We saw clear evidence that these three network factors dramatically improved managers’ odds of successfully implementing all kinds of reforms. We believe they can do the same for change agents in a wide variety of organizations.
Read also: The Secrets of Great Change Agents
Community structure is one of the key properties of complex networks and plays a crucial role in their topology and function. While an impressive amount of work has been done on the issue of community detection, very little attention has been so far devoted to the investigation of communities in real networks. We present a systematic empirical analysis of the statistical properties of communities in large information, communication, technological, biological, and social networks. We find that the mesoscopic organization of networks of the same category is remarkably similar. This is reflected in several characteristics of community structure, which can be used as “fingerprints” of specific network categories. While community size distributions are always broad, certain categories of networks consist mainly of tree-like communities, while others have denser modules. Average path lengths within communities initially grow logarithmically with community size, but the growth saturates or slows down for communities larger than a characteristic size. This behaviour is related to the presence of hubs within communities, whose roles differ across categories. Also the community embeddedness of nodes, measured in terms of the fraction of links within their communities, has a characteristic distribution for each category. Our findings, verified by the use of two fundamentally different community detection methods, allow for a classification of real networks and pave the way to a realistic modelling of networks’ evolution.
Many real networks in nature and society share two generic properties: they are scale-free and they display a high degree of clustering. We show that these two features are the consequence of a hierarchical organization, implying that small groups of nodes organize in a hierarchical manner into increasingly large groups, while maintaining a scale-free topology. In hierarchical networks, the degree of clustering characterizing the different groups follows a strict scaling law, which can be used to identify the presence of a hierarchical organization in real networks. We ﬁnd that several real networks, such as the WorldWideWeb, actor network, the Internet at the domain level, and the semantic web obey this scaling law, indicating that hierarchy is a fundamental characteristic of many complex systems.
The investigation of community structures in networks is an important issue in many domains and disciplines. This problem is relevant for social tasks (objective analysis of relationships on the web), biological inquiries (functional studies in metabolic and protein networks), or technological problems (optimization of large infrastructures). Several types of algorithms exist for revealing the community structure in networks, but a general and quantitative deﬁnition of community is not implemented in the algorithms, leading to an intrinsic difﬁculty in the interpretation of the results without any additional non-topological information. In this article we deal with this problem by showing how quantitative deﬁnitions of community are implemented in practice in the existing algorithms. In this way the algorithms for the identiﬁcation of the community structure become fully self-contained. Furthermore, we propose a local algorithm to detect communities which outperforms the existing algorithms with respect to computational cost, keeping the same level of reliability. The algorithm is tested on artiﬁcial and real-world graphs. In particular, we show how the algorithm applies to a network of scientiﬁc collaborations, which, for its size, cannot be attacked with the usual methods. This type of local algorithm could open the way to applications to large-scale technological and biological systems.
Nature, technology and society are full of complexity arising from the intricate web of the interactions among the units of the related systems (e.g., proteins, computers, people). Consequently, one of the most successful recent approaches to capturing the fundamental features of the structure and dynamics of complex systems has been the investigation of the networks associated with the above units (nodes) together with their relations (edges). Most complex systems have an inherently hierarchical organization and, correspondingly, the networks behind them also exhibit hierarchical features. Indeed, several papers have been devoted to describing this essential aspect of networks, however, without resulting in a widely accepted, converging concept concerning the quantitative characterization of the level of their hierarchy. Here we develop an approach and propose a quantity (measure) which is simple enough to be widely applicable, reveals a number of universal features of the organization of real-world networks and, as we demonstrate, is capable of capturing the essential features of the structure and the degree of hierarchy in a complex network.
Hierarchy seems to pervade complexity in both living and artificial systems. Despite its relevance, no general theory that captures all features of hierarchy and its origins has been proposed yet. Here we present a formal approach resulting from the convergence of theoretical morphology and network theory that allows constructing a 3D morphospace of hierarchies and hence comparing the hierarchical organization of ecological, cellular, technological, and social networks. Embedded within large voids in the morphospace of all possible hierarchies, four major groups are identified. Two of them match the expected from random networks with similar connectivity, thus suggesting that nonadaptive factors are at work. Ecological and gene networks define the other two, indicating that their topological order is the result of functional constraints. These results are consistent with an exploration of the morphospace, using in silico evolved networks.
The purpose of this article is to illustrate, through the example of human dynamics, that a thorough understanding of complex systems requires an understanding of network dynamics as well as network topology and architecture. After an overview of the topology of complex networks, such as the Internet and the WWW, data-driven models for human dynamics are given. These models motivate the study of network dynamics and suggest that complexity theory must incorporate the interactions between dynamics and structure. The article also advances the notion that an understanding of network dynamics is facilitated by the availability of large data sets and analysis tools gained from the study of network structure.