Scalable Algorithms for the Analysis of Massive Networks

Die Netzwerkanalyse zielt darauf ab, nicht-triviale Erkenntnisse aus vernetzten Daten zu gewinnen. Beispiele für diese Erkenntnisse sind die Wichtigkeit einer Entität im Verhältnis zu anderen nach bestimmten Kriterien oder das Finden des am besten geeigneten Partners für jeden Teilnehmer eines Netzwerks - bekannt als Maximum Weighted Matching (MWM). Da der Begriff der Wichtigkeit an die zu betrachtende Anwendung gebunden ist, wurden zahlreiche Zentralitätsmaße eingeführt. Diese Maße stammen hierbei aus Jahrzehnten, in denen die Rechenleistung sehr begrenzt war und die Netzwerke im Vergleich zu heute viel kleiner waren. Heute sind massive Netzwerke mit Millionen von Kanten allgegenwärtig und eine triviale Berechnung von Zentralitätsmaßen ist oft zu zeitaufwändig. Darüber hinaus ist die Suche nach der Gruppe von k Knoten mit hoher Zentralität eine noch kostspieligere Aufgabe. Skalierbare Algorithmen zur Identifizierung hochzentraler (Gruppen von) Knoten in großen Graphen sind von großer Bedeutung für eine umfassende Netzwerkanalyse. Heutigen Netzwerke verändern sich zusätzlich im zeitlichen Verlauf und die effiziente Aktualisierung der Ergebnisse nach einer Änderung ist eine Herausforderung. Effiziente dynamische Algorithmen sind daher ein weiterer wesentlicher Bestandteil moderner Analyse-Pipelines. Hauptziel dieser Arbeit ist es, skalierbare algorithmische Lösungen für die zwei oben genannten Probleme zu finden. Die meisten unserer Algorithmen benötigen Sekunden bis einige Minuten, um diese Aufgaben in realen Netzwerken mit bis zu Hunderten Millionen von Kanten zu lösen, was eine deutliche Verbesserung gegenüber dem Stand der Technik darstellt. Außerdem erweitern wir einen modernen Algorithmus für MWM auf dynamische Graphen. Experimente zeigen, dass unser dynamischer MWM-Algorithmus Aktualisierungen in Graphen mit Milliarden von Kanten in Millisekunden bewältigt.Network analysis aims to unveil non-trivial insights from networked data by studying relationship patterns between the entities of a network. Among these insights, a popular one is to quantify the importance of an entity with respect to the others according to some criteria. Another one is to find the most suitable matching partner for each participant of a network knowing the pairwise preferences of the participants to be matched with each other - known as Maximum Weighted Matching (MWM). Since the notion of importance is tied to the application under consideration, numerous centrality measures have been introduced. Many of these measures, however, were conceived in a time when computing power was very limited and networks were much smaller compared to today's, and thus scalability to large datasets was not considered. Today, massive networks with millions of edges are ubiquitous, and a complete exact computation for traditional centrality measures are often too time-consuming. This issue is amplified if our objective is to find the group of k vertices that is the most central as a group. Scalable algorithms to identify highly central (groups of) vertices on massive graphs are thus of pivotal importance for large-scale network analysis. In addition to their size, today's networks often evolve over time, which poses the challenge of efficiently updating results after a change occurs. Hence, efficient dynamic algorithms are essential for modern network analysis pipelines. In this work, we propose scalable algorithms for identifying important vertices in a network, and for efficiently updating them in evolving networks. In real-world graphs with hundreds of millions of edges, most of our algorithms require seconds to a few minutes to perform these tasks. Further, we extend a state-of-the-art algorithm for MWM to dynamic graphs. Experiments show that our dynamic MWM algorithm handles updates in graphs with billion edges in milliseconds

Yi-Er-San topics in network science: centrality, bicycle, triplet

Network science studies interactions between different entities. This thesis covers three different topics in network science: higher-order network structures, human mobility and network centralities. Higher-order network is an emerging field in recent years and combinatorial models use multi-body interactions to describe the structures beyond pairwise. In chapter two, I focus on studying the evolution of the links in temporal networks using three nodes motif-- triplets. In specific, I develop a method that use a transition matrix to describe the evolution of triplets in temporal networks. To identify the importance of higher-order interactions in the evolution of networks, we compare both artificial and real-world network data to a model based on pairwise interactions only. The differences between the transition matrix and the calculated matrix from the fitted parameters demonstrate that non-pairwise interactions exist for various real-world systems in space and time. Furthermore, this also reveals that different patterns of higher-order interaction are involved in different real-world situations. To test my approach, I used the transition matrix to design a link prediction method-- Triplet Transition score. I investigate the performance of the methods on four temporal networks, comparing my approach against ten other link prediction methods. My results show that higher-order interactions in both space and time play a crucial role in the evolution of networks as I find Triplet Transaction method, along with two other methods based on non-local interactions, gives the best overall performance. The results also confirm the concept that the higher-order interaction patterns, i.e., triplet dynamics, can help us understand and predict the evolution of different real-world systems. In chapter three, I investigate the behaviours of human mobility in different cities using gravity models. Due to previous technical challenges in collecting data on riding behaviours, there have been only a few studies focusing on patterns and regularities of biking traffic. To extend the research, I use the data from mobike and apply the gravity model to study the mobility of dockless bicycles. I validate the effectiveness of the general gravity model on predicting biking traffic at fine spatial resolutions of locations. I then further study the impacts of spatial scale on the gravity model and reveal that the distance-related parameter grows in a similar way as population-related parameters when the spatial scale of locations increases. The result reveals the emergence of the scaling can be explained by the gravity models. Measuring the importance of nodes in networks via centrality measures is an important task in many network systems. There are many centrality measures available and it is speculated that many encode similar information but the reason behind them is rarely studied. In chapter four, I give an explicit non-linear relationship between two of the most popular measures of node centrality: degree and closeness. Based on a shortest-path tree approximation, I give an analytic derivation that shows the inverse of closeness is linearly dependent on the logarithm of degree. I show that the hypothesis works well for a range of networks produced from stochastic network models and for networks derived from many real-world data sets. I connect our results with previous results for other network distance scales such as average distance. My results imply that measuring closeness is broadly redundant unless our relationship is used to remove the dependence on degree from closeness. The success of our relationship suggests that most networks can be approximated by shortest-path spanning trees which are all statistically similar two or more steps away from their root nodes.Open Acces

Perturbation Centrality and Turbine: A Novel Centrality Measure Obtained Using a Versatile Network Dynamics Tool

Analysis of network dynamics became a focal point to understand and predict changes of complex systems. Here we introduce Turbine, a generic framework enabling fast simulation of any algorithmically definable dynamics on very large networks. Using a perturbation transmission model inspired by communicating vessels, we define a novel centrality measure: perturbation centrality. Hubs and inter-modular nodes proved to be highly efficient in perturbation propagation. High perturbation centrality nodes of the Met-tRNA synthetase protein structure network were identified as amino acids involved in intra-protein communication by earlier studies. Changes in perturbation centralities of yeast interactome nodes upon various stresses well recapitulated the functional changes of stressed yeast cells. The novelty and usefulness of perturbation centrality was validated in several other model, biological and social networks. The Turbine software and the perturbation centrality measure may provide a large variety of novel options to assess signaling, drug action, environmental and social interventions. The Turbine algorithm is available at: http://www.turbine.linkgroup.huComment: 21 pages, 4 figues, 1 table, 58 references + a Supplement of 52 pages, 10 figures, 9 tables and 39 references; Turbine algorithm is available at: http://www.turbine.linkgroup.h

Innovative Affinity Spaces and Their Influence on Network Leadership in Open Innovation: a Dynamic Network Analysis of the Case of China

The intention in this paper is to present a conceptual framework developed as an evolution of the ‘innovation communities’ concept, called ‘Innovative Affinity Spaces’. This new construct is applied in the context of Chinese firms to explore how it affects network leadership in open innovation projects. Using dynamic network analysis as the methodological tool, the research hypotheses were addressed through cross-checking data from a sample of 68 Chinese networks of companies and research institutions.  Our study yields important conclusions on the notion of network competencies/capabilities as critical elements towards successful network leadership acting within innovative affinity spaces. Keywords: Innovation networks, Network leadership, Network capabilities, Open innovation, Affinity spaces, Dynamic network analysis, Chin

Assessing Subject Areas of Worldwide Information Literacy Research and Practice: A Discipline Co-Occurrence Network Analysis Approach

IL is important due to its potentiality to optimize the use of available information and to transform the novice into self-directed lifelong learners. It has gained ground and much attention in every field of knowledge which is assured by rapid increase in related literature. Since, the IL skills require subject-oriented approach not only to develop standard, guide, framework, tools, etc. but also to evaluate, assess, and impact of IL skills. Thus, measuring of the subject areas of IL publications and it co-occurrence is imperative and the objective of the present study. Based on data from Scopus database, network visualization technique is applied for the measure subject areas co-occurrence and related trends in the IL research articles published during 2001-16. IL publications show linear growth in the study period and trend is also in the same line. IL publications are spread into 26 out of 27 subject areas of Scopus database while there is research gap in Immunology and Microbiology. Social Science is observes as the core subject area while Computer Sciences, Arts and Humanities, Engineering, and Medicine are playing key role in IL research and practices. Social Sciences control the knowledge flow in the network i.e. every new ideas in the network is communicated through this. Highest co-occurrences are observed in Social Sciences and Computer Science followed by Social Sciences--Arts and Humanities; Social Sciences--Business, Management and Accounting; and Social Sciences--Medicine. The findings of the study are proxy of the current status and trend in the subject areas of worldwide IL publications thus provides panoramic view of IL publications in different subjects of world of knowledge

Rumor propagation on random and small world networks

In this work; three specific dynamical systems models, the Basic, Maki-Thompson, and Daley-Kendall, are used to model rumor transmission on social networks. Rumor flow is a measure of the time it takes for the rumor to completely pass through a specified network. Comparisons between random social networks and a small world social networks yield the faster transmission of a rumor over a small world network. Using unique adjacency matrices that define our random networks, observations of some characteristics of the random networks will be made that are specific to this type of graph. Differences in the constructs of the two networks will be illustrated by comparing these properties to those of the small world networks (created by a certain rewiring scheme of a k-regular network). Interesting comparisons are to be made about the networks\u27 defining characteristics include average clustering coefficients, centrality measures, and average path lengths. The flow of a rumor through each type of network reveals the characteristics of the network. A rumor will clearly flow through a small world network faster than in a random network, mainly due to higher density, increased clustering and better defined centrality

Diffusion of a gear-based conservation innovation: adoption patterns and social - ecological outcomes

Conservation interventions are only effective if people use them. Thus, identifying motivations and barriers to the uptake of conservation interventions is critical. Yet, analysis of factors that hinder or promote conservation diffusion (spread of conservation interventions) processes has received little attention by conservation practitioners and policy makers. Consequently, many efforts to achieve sustainability fail to reach full potential. Nearly all conservation interventions are characterized by the introduction of new ideas and practices. In line with this recognition, implementation of conservation can therefore benefit from a large body of social science research that explains how new ideas, practises, and technologies, i.e., innovations spread. Central to understanding how innovations spread among social systems, is the diffusion of innovations theory pioneered by Rogers. This thesis uses the diffusion of innovation lens to investigate the introduction of a conservation intervention in coastal Kenya. Diffusion research show that peoples' adoption behaviour is typically influenced by social differentiations in terms of personal attributes, socioeconomic status, and communication behaviour (Rogers 2010). Though personal attributes and socioeconomic status are widely used to analyse adoption processes (Horst et al 2007, Knowler & Bradshaw 2007), there remains very limited empirical work emphasizing the effect of communication behaviour in conservation diffusion literature. In addition, there is a long-standing recognition that proper communication channels are critical in facilitating innovation transfer (Gladwell 2006, Nilakanta & Scamell 1990, Rogers 1995). Yet, no criteria currently exist in the conservation literature to identify characteristics and functions of key intermediaries needed to facilitate conservation transfer. Thirdly, after initial adoption, whether people maintain an innovation is largely determined by the impact it has on their lives. However, conservation diffusion studies rarely examine the impacts of conservation innovations on either people or ecosystems (Weeks et al 2010, Woodhouse & Emiel de Lange 2016). These critical knowledge gaps lend themselves for empirical investigation. This thesis therefore aims to examine how people adopt conservation interventions and determine key social and environmental impacts of doing so. To address these aims, I ask two fundamental research questions: (i) "how does conservation interventions spread through societies?" (ii) "what are the consequences of conservation diffusion on people and environment?" I provide answers to these questions by addressing the following interrelated specific objectives: 1. determine the factors that influence uptake (adoption) and spread (diffusion) of a conservation intervention over time (Chapter 3) 2. identify key stakeholders to facilitate conservation transfer (Chapter 4) 3. investigate impacts of conservation diffusion on people's wellbeing (Chapter 5) 4. examine impacts of conservation diffusion on the ecosystem (Chapter 6) I explore these issues through a case study of a fisheries bycatch (incidental take) reduction initiative introduced in coastal Kenya (see details in chapter 2). Specifically, I study a modified basket trap retrofitted with escape gaps that allows juveniles and narrow-bodied, low value fish species (i.e. bycatch) to exit, while larger, wider-bodied target species are retained (Mbaru & McClanahan 2013). This intervention was introduced with the explicit aim to protect biodiversity by harvesting fish species at sizes that ensure sustainability of the local fishery (McClanahan & Mangi 2004). However, it was expected that improved catches over time will translate to positive sustainability outcomes, e.g., improved income and livelihoods that will continue to accrue over the long term. Aside from the diffusion of innovations theory, this research further draws from a number of social science theories and emerging breakthroughs in functional ecology to provide a rigorous and deeper examination of the study aims highlighted above. Chapter 1 provides a general introduction about the different theoretical foundations and approaches that can be used to analyse conservation diffusion processes in light of the diffusion of innovations theory. Chapter 2 provides an overview of study sites and describes the methods used throughout the thesis, though each chapter will also have additional methods. In chapter 3, I integrate theoretical foundations of the diffusion of innovations theory with novel breakthroughs in network science to offer a clearer understanding of the factors that shape conservation diffusion patterns over time. Unlike the majority of conservation diffusion studies, I explicitly measure communication behaviour via social networks and leverage recent advances in network modelling to simultaneously test the effect of social network structures and social influence on conservation diffusion while accounting for personal attributes and socioeconomic characteristics. I show that network processes contribute considerably to conservation diffusion – particularly in the early adoption stage – even when key socioeconomic factors are accounted for. By showing that communication behaviour is crucial during the early stages of the diffusion process, my results challenge decades of diffusion research suggesting commination behaviour is more important for late adoption. Overall, I demonstrate that harnessing the power and characteristics of social networks can help diffuse conservation interventions through target populations. In chapter 4, I draw on social network theory and methods to develop specific criteria for selecting stakeholders who are best placed in social networks (i.e., key players) to facilitate four key conservation objectives: (1) rapid diffusion of conservation information, (2) diffusion between disconnected groups, (3) rapid diffusion of complex knowledge or initiatives, or (4) widespread diffusion of conservation information or initiatives over a longer time period. After identifying the key players for the four distinct diffusion related conservation objectives, I then test whether the socioeconomic attributes of the key players I identified match the ones typically selected by conservation NGOs and other resource management agencies to facilitate conservation diffusion (i.e., current players). Results show clear discrepancies between current players and key players, highlighting missed opportunities for progressing more effective conservation diffusion. The chapter concludes with a novel, practical, and nuance approach to identify a set of ‗key players' better positioned to facilitate diffusion related conservation objectives, thereby helping to mitigate the problem of stakeholder identification in conservation diffusion processes. The focus of chapter 5 is to investigate the effects of adoption or non-adoption of the conservation intervention on people's wellbeing, i.e., an umbrella term that encompasses good social relations, freedom of choice, and basic materials for a good life (MEA 2005). Here, I use the wellbeing framework (Gough & McGregor 2007) to capture how the conservation innovation may impact multiple dimensions (material, relational, subjective) of people's wellbeing. I use panel data (i.e., follow the same individuals over time) to study these three dimensions of wellbeing before the intervention, during the short term (i.e., one year after the introduction), and in the medium term (i.e., about two years after the introduction) for those that adopt the innovation (adopters), those that don't adopt (nonadopters), and in control villages, where the intervention was not introduced. Overall, my findings indicate that adoption of the conservation intervention did no harm to the associated human communities. Indeed, I show modest improvements in material and subjective livelihood wellbeing for adopters relative to controls over time. However, the variations I find in wellbeing experiences (in terms of magnitude of change) among adopters, nonadopters, and controls across the different domains over time affirm the dynamic and social nature of wellbeing. Findings emphasize the need for environmental policy to use multiple indicators of wellbeing in addition to baselines in future evaluation research. The focus of chapter 6 is to assess the impact of the conservation intervention on environment. Previous attempts have been made to understand the effects of escape slot trap fishing on the marine environment (Condy et al 2015). However, most of this work tends to focus on species abundances and catch composition (Gomes et al 2014). Yet, the growing interest in an ecosystem-based approach has stressed maintaining and sustaining ecological functions (Henriques et al 2014). Moreover, in multi-species coral reef fisheries fishing gears are known to exhibit some degree of overlap in the species they capture (McClanahan & Mangi 2001). Depending on the level and type of overlap, these interactions can potentially retard critical pathways associated with gear-based conservation interventions (McClanahan & Kosgei 2018). Against this background, I employ a trait-based approach to assess functional selectivity of the escape slot trap. In addition, I quantify overlaps in catch composition between escape slot traps and other gear types that operate concurrently in the same reefs. These are hook and line, speargun, gillnet, beach seine, basket trap, and a combination of other nets. Overall, I show that using escape slot traps has the potential to lead to environmental improvements. Fish assemblages in escape slot traps are more functionally redundant (tendency of species to perform similar functions) and a vast majority constitute the least breadth of functional diversity. However, I find that two-thirds of the catch released by escape slot traps is targeted by other gear types. Thus, given the extent of overlaps in species selectivity between gears, switching to escape slot traps may not achieve conservation targets in the Kenyan multi-species coral reef fishery unless other gear types are also simultaneously excluded. These results call for caution when assessing ecological implications of gear-based conservation innovations particularly in gear-diverse coral reef fisheries where competitive interactions between gears are eminent. Together, this body of work advances the current state of knowledge about analysing patterns and outcomes of conservation diffusion over time. The stakeholder selection criteria developed in chapter 4 can be applied to facilitate widespread adoption and diffusion of simple initiatives such as rapid environmental awareness campaigns as well as more complex initiatives that seek to implement behaviour change to improve conservation outcomes. This work further provides a more comprehensive way to look at conservation outcomes and can help draw policy attention to the nonmaterial impacts of conservation. Trait-based approaches can provide a concrete platform for ecosystem-based management approaches in tropical multi-species fisheries