Networks of communities and communities of networks in online government

Over the past decade the World Wide Web has become a core platform for the electronic operation of government. Yet the shape and nature of government presence on the Web and the online community in which it resides remains poorly understood and relatively under-theorised. This paper analyses large-scale web crawling data that map the hyperlink network structure between government websites and the broader Web ecology in the UK. In particular, it reports the ‘communities’ of websites within a hyperlink network of over 19,000 websites and over 135,000 hyperlinks derived from 75 key UK government seed sites at national, regional (i.e. Scotland and Wales) and local government levels. These website communities were derived by utilising Infomap, a state-of-the-art community detection algorithm that operate on the principle that flows of information in complex networks reveals community structure. Identifying and analysing online communities in which government websites reside provides insights in how hyperlink communities are arranged, that is, their emergent organizing principal and the importance of government in these online communities. It is hypothesized that online ‘communities’ can occur around different policy topics (such as health, education or policing), or along institutional or jurisdictional boundaries (such as England, Scotland and Wales). Using this novel approach this paper demonstrates that communities emerge on both axes, and that social media and government portals are some of the most significant communities based on information flows. This research provides foundational knowledge about the role of government websites in the World Wide Web, the emergent online associations, and the changing dynamic of state information in the twenty-first century. It points to strategies for developing government Web presence in networks that matter

Networks of Communities and Communities of Networks in Online Government

Over the past decade the World Wide Web has become a core platform for the electronic operation of government. Yet the shape and nature of government presence on the Web and the online community in which it resides remains poorly understood and under-theorised. This paper analyses large-scale web crawling data that map the hyperlink network structure between government websites and the broader Web ecology in the UK. In particular, it reports the ‘communities’ of websites within a hyperlink network of over 19,000 websites and over 135,000 hyperlinks derived from 75 key UK government seed sites at national, regional (i.e. Scotland and Wales) and local government levels. Website communities were derived by utilising Infomap, a state-of-the-art community detection algorithm that operates on the principle that flows of information in complex networks reveals community structure. Identifying and analysing online communities in which government websites reside provides insights in how hyperlink communities are arranged, that is, their emergent organizing principal and the importance of government in these online communities. It is hypothesized that online ‘communities’ can occur around different policy topics (such as health, education or policing), or along institutional or jurisdictional boundaries (such as England, Scotland and Wales). Using this novel approach this paper demonstrates that communities emerge on both axes, and that social media and government portals are some of the most significant communities based on information flows. This research provides foundational knowledge about the role of government websites in the World Wide Web, the emergent online associations, and the changing dynamic of state information in the twenty-first century. It points to strategies for developing government Web presence in networks that matter

Hierarchical mutual information for the comparison of hierarchical community structures in complex networks

The quest for a quantitative characterization of community and modular structure of complex networks produced a variety of methods and algorithms to classify different networks. However, it is not clear if such methods provide consistent, robust and meaningful results when considering hierarchies as a whole. Part of the problem is the lack of a similarity measure for the comparison of hierarchical community structures. In this work we give a contribution by introducing the {\it hierarchical mutual information}, which is a generalization of the traditional mutual information, and allows to compare hierarchical partitions and hierarchical community structures. The {\it normalized} version of the hierarchical mutual information should behave analogously to the traditional normalized mutual information. Here, the correct behavior of the hierarchical mutual information is corroborated on an extensive battery of numerical experiments. The experiments are performed on artificial hierarchies, and on the hierarchical community structure of artificial and empirical networks. Furthermore, the experiments illustrate some of the practical applications of the hierarchical mutual information. Namely, the comparison of different community detection methods, and the study of the consistency, robustness and temporal evolution of the hierarchical modular structure of networks.Comment: 14 pages and 12 figure

Detecting Community Structure in Dynamic Social Networks Using the Concept of Leadership

Detecting community structure in social networks is a fundamental problem empowering us to identify groups of actors with similar interests. There have been extensive works focusing on finding communities in static networks, however, in reality, due to dynamic nature of social networks, they are evolving continuously. Ignoring the dynamic aspect of social networks, neither allows us to capture evolutionary behavior of the network nor to predict the future status of individuals. Aside from being dynamic, another significant characteristic of real-world social networks is the presence of leaders, i.e. nodes with high degree centrality having a high attraction to absorb other members and hence to form a local community. In this paper, we devised an efficient method to incrementally detect communities in highly dynamic social networks using the intuitive idea of importance and persistence of community leaders over time. Our proposed method is able to find new communities based on the previous structure of the network without recomputing them from scratch. This unique feature, enables us to efficiently detect and track communities over time rapidly. Experimental results on the synthetic and real-world social networks demonstrate that our method is both effective and efficient in discovering communities in dynamic social networks

Community Detection in Dynamic Networks via Adaptive Label Propagation

An adaptive label propagation algorithm (ALPA) is proposed to detect and monitor communities in dynamic networks. Unlike the traditional methods by re-computing the whole community decomposition after each modification of the network, ALPA takes into account the information of historical communities and updates its solution according to the network modifications via a local label propagation process, which generally affects only a small portion of the network. This makes it respond to network changes at low computational cost. The effectiveness of ALPA has been tested on both synthetic and real-world networks, which shows that it can successfully identify and track dynamic communities. Moreover, ALPA could detect communities with high quality and accuracy compared to other methods. Therefore, being low-complexity and parameter-free, ALPA is a scalable and promising solution for some real-world applications of community detection in dynamic networks.Comment: 16 pages, 11 figure

Evolutionary Algorithms for Community Detection in Continental-Scale High-Voltage Transmission Grids

Symmetry is a key concept in the study of power systems, not only because the admittance and Jacobian matrices used in power flow analysis are symmetrical, but because some previous studies have shown that in some real-world power grids there are complex symmetries. In order to investigate the topological characteristics of power grids, this paper proposes the use of evolutionary algorithms for community detection using modularity density measures on networks representing supergrids in order to discover densely connected structures. Two evolutionary approaches (generational genetic algorithm, GGA+, and modularity and improved genetic algorithm, MIGA) were applied. The results obtained in two large networks representing supergrids (European grid and North American grid) provide insights on both the structure of the supergrid and the topological differences between different regions. Numerical and graphical results show how these evolutionary approaches clearly outperform to the well-known Louvain modularity method. In particular, the average value of modularity obtained by GGA+ in the European grid was 0.815, while an average of 0.827 was reached in the North American grid. These results outperform those obtained by MIGA and Louvain methods (0.801 and 0.766 in the European grid and 0.813 and 0.798 in the North American grid, respectively)

Overlapping Community Structure in Co-authorship Networks: a Case Study

Community structure is one of the key properties of real-world complex networks. It plays a crucial role in their behaviors and topology. While an important work has been done on the issue of community detection, very little attention has been devoted to the analysis of the community structure. In this paper, we present an extensive investigation of the overlapping community network deduced from a large-scale co-authorship network. The nodes of the overlapping community network represent the functional communities of the co-authorship network, and the links account for the fact that communities share some nodes in the co-authorship network. The comparative evaluation of the topological properties of these two networks shows that they share similar topological properties. These results are very interesting. Indeed, the network of communities seems to be a good representative of the original co-authorship network. With its smaller size, it may be more practical in order to realize various analyses that cannot be performed easily in large-scale real-world networks.Comment: 2014 7th International Conference on u- and e- Service, Science and Technolog