Clustering and Community Detection in Directed Networks: A Survey

Networks (or graphs) appear as dominant structures in diverse domains, including sociology, biology, neuroscience and computer science. In most of the aforementioned cases graphs are directed - in the sense that there is directionality on the edges, making the semantics of the edges non symmetric. An interesting feature that real networks present is the clustering or community structure property, under which the graph topology is organized into modules commonly called communities or clusters. The essence here is that nodes of the same community are highly similar while on the contrary, nodes across communities present low similarity. Revealing the underlying community structure of directed complex networks has become a crucial and interdisciplinary topic with a plethora of applications. Therefore, naturally there is a recent wealth of research production in the area of mining directed graphs - with clustering being the primary method and tool for community detection and evaluation. The goal of this paper is to offer an in-depth review of the methods presented so far for clustering directed networks along with the relevant necessary methodological background and also related applications. The survey commences by offering a concise review of the fundamental concepts and methodological base on which graph clustering algorithms capitalize on. Then we present the relevant work along two orthogonal classifications. The first one is mostly concerned with the methodological principles of the clustering algorithms, while the second one approaches the methods from the viewpoint regarding the properties of a good cluster in a directed network. Further, we present methods and metrics for evaluating graph clustering results, demonstrate interesting application domains and provide promising future research directions.Comment: 86 pages, 17 figures. Physics Reports Journal (To Appear

EUSN 2021 Book of Abstracts, Fifth European Conference on Social Networks

Book of abstract of the fifth European conference on Social Networks EUSN 202

Multidimensional Network analysis

This thesis is focused on the study of multidimensional networks. A multidimensional network is a network in which among the nodes there may be multiple different qualitative and quantitative relations. Traditionally, complex network analysis has focused on networks with only one kind of relation. Even with this constraint, monodimensional networks posed many analytic challenges, being representations of ubiquitous complex systems in nature. However, it is a matter of common experience that the constraint of considering only one single relation at a time limits the set of real world phenomena that can be represented with complex networks. When multiple different relations act at the same time, traditional complex network analysis cannot provide suitable analytic tools. To provide the suitable tools for this scenario is exactly the aim of this thesis: the creation and study of a Multidimensional Network Analysis, to extend the toolbox of complex network analysis and grasp the complexity of real world phenomena. The urgency and need for a multidimensional network analysis is here presented, along with an empirical proof of the ubiquity of this multifaceted reality in different complex networks, and some related works that in the last two years were proposed in this novel setting, yet to be systematically defined. Then, we tackle the foundations of the multidimensional setting at different levels, both by looking at the basic extensions of the known model and by developing novel algorithms and frameworks for well-understood and useful problems, such as community discovery (our main case study), temporal analysis, link prediction and more. We conclude this thesis with two real world scenarios: a monodimensional study of international trade, that may be improved with our proposed multidimensional analysis; and the analysis of literature and bibliography in the field of classical archaeology, used to show how natural and useful the choice of a multidimensional network analysis strategy is in a problem traditionally tackled with different techniques

An Initial Framework Assessing the Safety of Complex Systems

Trabajo presentado en la Conference on Complex Systems, celebrada online del 7 al 11 de diciembre de 2020.Atmospheric blocking events, that is large-scale nearly stationary atmospheric pressure patterns, are often associated with extreme weather in the mid-latitudes, such as heat waves and cold spells which have significant consequences on ecosystems, human health and economy. The high impact of blocking events has motivated numerous studies. However, there is not yet a comprehensive theory explaining their onset, maintenance and decay and their numerical prediction remains a challenge. In recent years, a number of studies have successfully employed complex network descriptions of fluid transport to characterize dynamical patterns in geophysical flows. The aim of the current work is to investigate the potential of so called Lagrangian flow networks for the detection and perhaps forecasting of atmospheric blocking events. The network is constructed by associating nodes to regions of the atmosphere and establishing links based on the flux of material between these nodes during a given time interval. One can then use effective tools and metrics developed in the context of graph theory to explore the atmospheric flow properties. In particular, Ser-Giacomi et al. [1] showed how optimal paths in a Lagrangian flow network highlight distinctive circulation patterns associated with atmospheric blocking events. We extend these results by studying the behavior of selected network measures (such as degree, entropy and harmonic closeness centrality)at the onset of and during blocking situations, demonstrating their ability to trace the spatio-temporal characteristics of these events.This research was conducted as part of the CAFE (Climate Advanced Forecasting of sub-seasonal Extremes) Innovative Training Network which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 813844

SIS 2017. Statistics and Data Science: new challenges, new generations

The 2017 SIS Conference aims to highlight the crucial role of the Statistics in Data Science. In this new domain of ‘meaning’ extracted from the data, the increasing amount of produced and available data in databases, nowadays, has brought new challenges. That involves different fields of statistics, machine learning, information and computer science, optimization, pattern recognition. These afford together a considerable contribute in the analysis of ‘Big data’, open data, relational and complex data, structured and no-structured. The interest is to collect the contributes which provide from the different domains of Statistics, in the high dimensional data quality validation, sampling extraction, dimensional reduction, pattern selection, data modelling, testing hypotheses and confirming conclusions drawn from the data

Advances in Computational Intelligence Applications in the Mining Industry

This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners

A dynamic view of network structure and governance mechanisms : the case of a coffee sector sustainable sourcing network

In the context of sustainable supply networks, this research analyzes the evolution of governance mechanisms and network structure, including the interplay between network conditions, context factors, positional power and managerial actions. The study reports on a longitudinal empirical research on a multi-stakeholder sustainable sourcing network established by Nespresso, Nestlé’s specialty coffee subsidiary. The research analyzes both dyadic and multi-actor network dynamics and proposes a framework to study network evolution. Social network analysis techniques are also used to measure evolution of the network's structure and complexity as well as positional power opportunities. The research shows that in the initial start-up phase, in a context marked by uncertainty, pre-existing commercial and personal relationships were favoured in the choice of partners. These pre-existing relationships were also influential in defining the initial network structure and supporting an initial phase of exploration. Governance mechanisms initially relied mostly on informal mechanisms, while formal mechanisms were incorporated over time to enable the supply chain network to grow and to provide clarity to all actors. As the sustainability programme network expanded in size and complexity, Nespresso, the lead organization, also acted on the network's structure by introducing regional offices, thus increasing network centralization and reducing complexity. Power derived by actors occupying central or brokerage positions in multiplex networks also influenced power relationships in the sustainability network by moderating or expanding the power opportunities available to central actors. The research has implications for both the Inter-organizational Relationship and the Social Network Theory literatures. In contrast with prior literature, the research proposes that in conditions of uncertainty, the use of informal governance mechanisms can facilitate a search and experimentation process. Formalization of governance mechanisms can be used, not as a repair mechanism, but rather as an enabler for further growth and efficiency. The research also extends the concept of network complexity and proposes that network managers can reduce this complexity by introducing or managing nodes that in turn contribute to the re-centralization of relationships towards specific nodes. Lastly, the research has implications for managers and proposes mapping of existing commercial and personal relationships as a potentially valuable tool in the creation and management of networks, adapting coordination mechanisms to the objectives of the relationship and actively managing the network's structure as a mechanism to enable network growth and efficiency.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Social Norms Marketing, Social Networks and Alcohol Consumption: A Collegiate Context. Investigating Feasability in Ireland.

The current Irish policies have not been adequately effective in reducing alcohol consumption. There is a need to consider alternative strategies, such as the increasingly popular SN marketing campaigns, which have been applied successfully in the US college system. However, the potential of these campaigns has not been evaluated in Ireland. It is also not clear from the literature if descriptive or injunctive norm types will be more likely to induce behaviour change. Further, while SN interventions tend to provide ‘friends’ or ‘typical student’ as referent groups, little is understood about how individuals visualize these groups and how salient these peers are. The present study addressed these issues by combining web based survey methods with social network analysis. Hierarchical multiple regression was used to analyse a web survey of 1700 DIT students. Further, 26 ego networks generated via in depth interviews were examined using network techniques combined with a qualitative analysis to understand norm salience. The study provides evidence of overestimations of the campus drinking norm at DIT. It shows that perceived norms impact personal consumption and that social distance is a key consideration in this regards. Further, the findings demonstrate that descriptive norms are stronger predictors of personal consumption than injunctive norms. Most importantly, the study provides evidence that individuals’ social networks are key determinants of their drinking behaviours and that the most salient peers for DIT students are embedded in cohesive sub groups outside college. The study does not support using SN campaigns to reduce alcohol consumption in DIT. It urges policy makers to address norm salience in intervention work as it is critical for the applicability, planning and success of SN campaigns