Local Edge Betweenness based Label Propagation for Community Detection in Complex Networks

Nowadays, identification and detection community structures in complex networks is an important factor in extracting useful information from networks. Label propagation algorithm with near linear-time complexity is one of the most popular methods for detecting community structures, yet its uncertainty and randomness is a defective factor. Merging LPA with other community detection metrics would improve its accuracy and reduce instability of LPA. Considering this point, in this paper we tried to use edge betweenness centrality to improve LPA performance. On the other hand, calculating edge betweenness centrality is expensive, so as an alternative metric, we try to use local edge betweenness and present LPA-LEB (Label Propagation Algorithm Local Edge Betweenness). Experimental results on both real-world and benchmark networks show that LPA-LEB possesses higher accuracy and stability than LPA when detecting community structures in networks.Comment: 6 page

Attributed-Based Label Propagation Method for Balanced Modularity and Homogeneity Community Detection

Community Detection is an expanding field of interest in many scopes, e.g., social science, bibliometrics, marketing and recommendations, biology etc. Various community detection tools and methods have been proposed in the last years. This research is to develop an improved Label Propagation algorithm (Attribute-Based Label Propagation ABLP) that considers the nodes’ attributes to achieve a fair Homogeneity value, while maintaining high Modularity measure. It also formulates an adaptive Homogeneity measure, with penalty and weight modulation, that can be utilized in consonance with the user’s requirements. Based on the literature review, a research gap of employing Homogeneity in Community Detection was identified, and accordingly, Homogeneity as a constraint in Modularity based methods is investigated. In addition, a novel dataset constructed on COVID-19 contact tracing in the Kingdom of Bahrain is proposed, to help identify communities of infected persons and study their attributes’ values. The implementation of proposed algorithm performed high Modularity and Homogeneity measures compared with other algorithms

A Node Influence Based Label Propagation Algorithm for Community Detection in Networks

Label propagation algorithm (LPA) is an extremely fast community detection method and is widely used in large scale networks. In spite of the advantages of LPA, the issue of its poor stability has not yet been well addressed. We propose a novel node influence based label propagation algorithm for community detection (NIBLPA), which improves the performance of LPA by improving the node orders of label updating and the mechanism of label choosing when more than one label is contained by the maximum number of nodes. NIBLPA can get more stable results than LPA since it avoids the complete randomness of LPA. The experimental results on both synthetic and real networks demonstrate that NIBLPA maintains the efficiency of the traditional LPA algorithm, and, at the same time, it has a superior performance to some representative methods

Large-Scale Networks: Algorithms, Complexity and Real Applications

Networks have broad applicability to real-world systems, due to their ability to model and represent complex relationships. The discovery and forecasting of insightful patterns from networks are at the core of analytical intelligence in government, industry, and science. Discoveries and forecasts, especially from large-scale networks commonly available in the big-data era, strongly rely on fast and efficient network algorithms. Algorithms for dealing with large-scale networks are the first topic of research we focus on in this thesis. We design, theoretically analyze and implement efficient algorithms and parallel algorithms, rigorously proving their worst-case time and space complexities. Our main contributions in this area are novel, parallel algorithms to detect k-clique communities, special network groups which are widely used to understand complex phenomena. The proposed algorithms have a space complexity which is the square root of that of the current state-of-the-art. Time complexity achieved is optimal, since it is inversely proportional to the number of processing units available. Extensive experiments were conducted to confirm the efficiency of the proposed algorithms, even in comparison to the state-of-the-art. We experimentally measured a linear speedup, substantiating the optimal performances attained. The second focus of this thesis is the application of networks to discover insights from real-world systems. We introduce novel methodologies to capture cross correlations in evolving networks. We instantiate these methodologies to study the Internet, one of the most, if not the most, pervasive modern technological system. We investigate the dynamics of connectivity among Internet companies, those which interconnect to ensure global Internet access. We then combine connectivity dynamics with historical worldwide stock markets data, and produce graphical representations to visually identify high correlations. We find that geographically close Internet companies offering similar services are driven by common economic factors. We also provide evidence on the existence and nature of hidden factors governing the dynamics of Internet connectivity. Finally, we propose network models to effectively study the Internet Domain Name System (DNS) traffic, and leverage these models to obtain rankings of Internet domains as well as to identify malicious activities

Isomorphic effects of managerial and directoral career paths

The extensive array of interlocking directorate research remains near-exclusively cross-sectional or comparative cross-sectional in nature. While this has been fruitful in identifying persistent structures of inter-organisational relationships evidence of the impact of these structures on organisational performance or activity has been more limited. This should not be surprising because, by their nature, relationships have strong longitudinal and dynamic qualities that are likely to be difficult to isolate through cross-sectional approaches. Clearly, managerial practice is inevitably strongly conditioned by the specific contingencies of the time and the information available through networks of colleagues and advisers (particularly at board level) at the time. But managerial and directoral capabilities and mental sets are also developed over time, particularly through previous experiences in these roles and the formation of long-lasting 'strong' and 'weak' relationships. This paper tests the influence of three longitudinal dimensions of managers and directors' relationships on a set of indicators of financial performance, drawing from a large dataset of detailing historic board membership of UK firms. It finds evidence of isomorphic processes through these channels and establishes that the longitudinal design considerably enhances the detection of performance effects from directorate interlocks. More broadly, the research has implications for the conception of collective action and the constitution of 'community'

Sustainable development using urban governance instruments

This thesis examines the relationship between sustainable development and urban governance and the implementation of sustainable development with urban governance instruments in spatial planning and planning law in the United Kingdom and in Germany. The enquiry focuses on social segregation as a challenge for urban development. It is argued that segregation is not a new phenomenon, but a problem that has aggravated over the last years. Segregation and the lack of social cohesion lead to socially perforated cities which suffer from inequalitites between their different neighbourhoods. This leads to the question of effective urban planning process control: Which legal instruments should be used to tackle the problematic implications of urban development, including the segregation and exclusion of whole urban districts? New governance approaches combine area-based policies with an integrated urban development policy. The thesis shows that the most important condition for effective legal process control of urban development however is a guideline for desirable results. It focuses on the concept of sustainable urban development as the guiding principle for contemporary urban development. It is argued that sustainable development focusses on the three pillars of an equal relation between the protection of the environment and a just society by means of a social economic development and good governance. It is also stated that the problem of the concept of sustainable development on the international level, the European level, and on the national levels is that it often lacks mandatory obligations on policy and decision makers with really meaningful consequences. The biggest challenge for sustainable development in the next years will be its operationalisation. An effective implementation of the approach requires a translation of its objectives into specific actions for specific places. Therefore the thesis reviews urban governance as a useful approach to sustainable development. It is shown that the implementation of governance networks can increase both effectiveness by means of problem-solving capacity and the legitimacy of governance in terms of democratic participation and accountability. The relationship between sustainable development and urban governance is illustrated with the examples of the New Deal for Communities programme in the UK and the Social City Programme in Germany. Both instruments supplant traditional top down-polity models with governance instruments when it comes to the implementation of sustainable development in deprived areas

Ratcheting a Way Out of the Respectable: Genealogical Interventions Into Atlanta\u27s Respectability Politics

What do Black Lives Matter and Freaknik have in common? In this paper, I will argue that moments of Black Lives Matter in Atlanta exhibited refusals and undoings of respectability politics through the method of the ratchet. I define the ratchet as moments of non-normative embodiment and political possibility that refuse statist and Eurocentric norms through slippage of the self and the engagement of Black queer sexual politics. Freaknik is foregrounded as a ripe space for excavating such a display of the politically ratchet in Atlanta. I will look at a few different moments in the Black Lives Matter movement in the city of Atlanta and read each for currents of ratchetness and respectability, highlighting the importance of the ratchet in political imagination and possibility

COMPUTATIONAL COMMUNICATION INTELLIGENCE: EXPLORING LINGUISTIC MANIFESTATION AND SOCIAL DYNAMICS IN ONLINE COMMUNICATION

We now live in an age of online communication. As social media becomes an integral part of our life, online communication becomes an essential life skill. In this dissertation, we aim to understand how people effectively communicate online. We research components of success in online communication and present scientific methods to study the skill of effective communication. This research advances the state of art in machine learning and communication studies. For communication studies, we pioneer the study of a communication phenomenon we call Communication Intelligence in online interactions. We create a theory about communication intelligence that measures participants’ ten high-order communication skills, including restraint, self-reflection, perspective taking, and balance. We present a multi-perspective analysis for understanding communication intelligence, including its diverse language, shared linguistic characteristics across people, social dynamics, and the effects of communication modality on communication intelligence. For machine learning, we contribute new computational models and formulations for addressing multi-label and multi-task machine learning problems. We develop a new hierarchical probabilistic model for simultaneously identifying multiple intelligence-embodied communication skills from natural language. The model learns the topic assignment for each sentence and provides a practical and simple way to determine document labels without relying on a threshold function. The model performance increases as the number of labels grows, which makes it a promising approach for large-scale data analysis. We also develop a new multi-task formulation for simultaneously identifying multiple intelligence-embodied communication skills from lexical, discourse, and interaction features. The key merit of this model is that it is a general multi-task formulation that unifies many widely used regularization techniques, including Lasso, group Lasso, sparse-group Lasso, and the Dirty model. This model expands the applicability of multi-task learning by allowing analyzing real-world problems where the degree of task relatedness is uncertain and the true structure of the groups in data is not clear ahead of time. Moreover, it can be applied to streaming data to perform large-scale analysis in real time. Beyond the application of studying communication intelligence, the developed models and formulations can also benefit research in other areas where the problems of simultaneously predicting multiple categories are abundant