Hides and skins have been a resource that has created a wide range of activities such as clothing and footwear of all types, saddlery and riding equipment, travel goods and upholstery were amongst many industries using leather. This dissertation uses historical documentation to investigate the interactions of a small UK company working mostly in the USA that had a pivotal role in the transformation of the network surrounding the production, distribution and use of leather in the late 19th century. As an extended historical analysis it offers a particularly wide perspective on the complex and continuing network outcomes of that networking and the innovations to which it leads. This historical research location also provides an opportunity to examine innovation within the context of network evolution over many decades.

The events of September 11,2001 followed by the oil price hike and the economic crisis of 2008, have lead to a drop in the demand for air travel. Airlines have attempted to return to profitability by cutting service in certain unattractive routes and airports. Simultaneously, delays and excess demand at a few major hubs have lead to airline introducing service at reliever airports. This dissertation attempts to capture the changes in the airline network by utilizing a supply-demand framework. / Ph. D.

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.

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.

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The next generation wireless networks i.e. 5G aim to provide multi-Gbps data traffic, in order to satisfy the increasing demand for high-definition video, among other high data rate services, as well as the exponential growth in mobile subscribers. To achieve this dramatic increase in data rates, current research is focused on improving the capacity of current 4G network standards, based on Long Term Evolution (LTE), before radical changes are exploited which could include acquiring additional/new spectrum. The LTE network has a reuse factor of one; hence neighbouring cells/sectors use the same spectrum, therefore making the cell edge users vulnerable to inter-cell interference. In addition, wireless transmission is commonly hindered by fading and pathloss. In this direction, this thesis focuses on improving the performance of cell edge users in LTE and LTE-Advanced (LTE-A) networks by initially implementing a new Coordinated Multi-Point (CoMP) algorithm to mitigate cell edge user interference. Subsequently Device-to-Device (D2D) communication is investigated as the enabling technology for maximising Resource Block (RB) utilisation in current 4G and emerging 5G networks. It is demonstrated that the application, as an extension to the above, of novel power control algorithms, to reduce the required D2D TX power, and multihop transmission for relaying D2D traffic, can further enhance network performance. To be able to develop the aforementioned technologies and evaluate the performance of new algorithms in emerging network scenarios, a beyond-the-state-of-the-art LTE system-level simulator (SLS) was implemented. The new simulator includes Multiple-Input Multiple-Output (MIMO) antenna functionalities, comprehensive channel models (such as Wireless World initiative New Radio II i.e. WINNER II) and adaptive modulation and coding schemes to accurately emulate the LTE and LTE-A network standards. Additionally, a novel interference modelling scheme using the 'wrap around' technique was proposed and implemented that maintained the topology of flat surfaced maps, allowing for use with cell planning tools while obtaining accurate and timely results in the SLS compared to the few existing platforms. For the proposed CoMP algorithm, the adaptive beamforming technique was employed to reduce interference on the cell edge UEs by applying Coordinated Scheduling (CoSH) between cooperating cells. Simulation results show up to 2-fold improvement in terms of throughput, and also shows SINR gain for the cell edge UEs in the cooperating cells. Furthermore, D2D communication underlaying the LTE network (and future generation of wireless networks) was investigated. The technology exploits the proximity of users in a network to achieve higher data rates with maximum RB utilisation (as the technology reuses the cellular RB simultaneously), while taking some load off the Evolved Node B (eNB) i.e. by direct communication between User Equipment (UE). Simulation results show that the proximity and transmission power of D2D transmission yields high performance gains for a D2D receiver, which was demonstrated to be better than that of cellular UEs with better channel conditions or in close proximity to the eNB in the network. The impact of interference from the simultaneous transmission however impedes the achievable data rates of cellular UEs in the network, especially at the cell edge. Thus, a power control algorithm was proposed to mitigate the impact of interference in the hybrid network (network consisting of both cellular and D2D UEs). It was implemented by setting a minimum SINR threshold so that the cellular UEs achieve a minimum performance, and equally a maximum SINR threshold to establish fairness for the D2D transmission as well. Simulation results show an increase in the cell edge throughput and notable improvement in the overall SINR distribution of UEs in the hybrid network. Additionally, multihop transmission for D2D UEs was investigated in the hybrid network: traditionally, the scheme is implemented to relay cellular traffic in a homogenous network. Contrary to most current studies where D2D UEs are employed to relay cellular traffic, the use of idle nodes to relay D2D traffic was implemented uniquely in this thesis. Simulation results show improvement in D2D receiver throughput with multihop transmission, which was significantly better than that of the same UEs performance with equivalent distance between the D2D pair when using single hop transmission.

Social media has become an important means of everyday communication. It is a mechanism for “sharing” and “resharing” of information. While social network platforms provide the means to users for resharing/reblogging (aka retweeting), it remains unclear what motivates users to share. Predicting the spread of content is quite important for several purposes such as viral marketing, popular news detection, personalized message recommendation and on-line advertisement. Social content systems store all the information produced in the interactions between users. However, to turn this data into information that allows us to extract patterns, it is important to consider the different phenomena involved in these interactions. In this work, two phenomena that influence the evolution of networks are studied for Twitter: diffusion of information and communication among users. Previous studies have shown that history of interaction among users and properties of the message are good attributes to understand the retweet behavior of users. Factors like content of message and time are less investigated. We propose a prediction model for retweet actions of users. It formulates a function which ranks the users according to how receptive they are to a particular message. The function generates a confidence score for the edges joining the initiator of the message and the followers. Two different pieces of information propagate through different users in the network. We divide the task of calculating confidence score into two parts. The first part is independent of the test tweet. It models transmission rate of the tie between the initiator and the follower. We call this as ‘Pairwise Influence Estimation’. The second part incorporates the tweet properties and user activeness as per time in the ranking function. The proposed model exploits all the dimensions of information dif-fusion process-influence, content and temporal properties. We have captured local aspects of diffusion. It has been observed that users do not read all the messages on their site. This results in shortcomings in the above models. Considering this, we first study the temporal behavior of users’ activities, which directly reflects their availability pertaining to the upcoming post. Also, as it is a continuous task of predicting retweet behavior, we design a user-centric, and temporally localized incremental classification model by considering the fact that users do not read all their tweets. We have tested the effectiveness of this model by using real data from Twitter. We demonstrate that the new proposed model is more accurate in describing the information propagation in microblog compared to the existing methods. Our model works well when we consider different classes of users depending on their activity patterns. In addition, we also investigate the parameters of the model for different classes of users. We report some interesting distinguishing patterns in retweeting behavior of users.

<p>The most important networks from different domains—such as Computing, Organization, Economic, Social, Academic, and Biology—are networks that change over time. For example, in an organization there are email and collaboration networks (e.g., different people or teams working on a document). Apart from the connectivity of the networks changing over time, they can contain attributes such as the topic of an email or message, contents of a document, or the interests of a person in an academic citation or a social network. Analyzing these dynamic networks can be critical in decision-making processes. For instance, in an organization, getting insight into how people from different teams collaborate, provides important information that can be used to optimize workflows.</p> <p><br></p> <p>Network generative models provide a way to study and analyze networks. For example, benchmarking model performance and generalization in tasks like node classification, can be done by evaluating models on synthetic networks generated with varying structure and attribute correlation. In this work, we begin by presenting our systemic study of the impact that graph structure and attribute auto-correlation on the task of node classification using collective inference. This is the first time such an extensive study has been done. We take advantage of a recently developed method that samples attributed networks—although static—with varying network structure jointly with correlated attributes. We find that the graph connectivity that contributes to the network auto-correlation (i.e., the local relationships of nodes) and density have the highest impact on the performance of collective inference methods.</p> <p><br></p> <p>Most of the literature to date has focused on static representations of networks, partially due to the difficulty of finding readily-available datasets of dynamic networks. Dynamic network generative models can bridge this gap by generating synthetic graphs similar to observed real-world networks. Given that motifs have been established as building blocks for the structure of real-world networks, modeling them can help to generate the graph structure seen and capture correlations in node connections and activity. Therefore, we continue with a study of motif evolution in <em>dynamic</em> temporal graphs. Our key insight is that motifs rarely change configurations in fast-changing dynamic networks (e.g. wedges intotriangles, and vice-versa), but rather keep reappearing at different times while keeping the same configuration. This finding motivates the generative process of our proposed models, using temporal motifs as building blocks, that generates dynamic graphs with links that appear and disappear over time.</p> <p><br></p> <p>Our first proposed model generates dynamic networks based on motif-activity and the roles that nodes play in a motif. For example, a wedge is sampled based on the likelihood of one node having the role of hub with the two other nodes being the spokes. Our model learns all parameters from observed data, with the goal of producing synthetic graphs with similar graph structure and node behavior. We find that using motifs and node roles helps our model generate the more complex structures and the temporal node behavior seen in real-world dynamic networks.</p> <p><br></p> <p>After observing that using motif node-roles helps to capture the changing local structure and behavior of nodes, we extend our work to also consider the attributes generated by nodes’ activities. We propose a second generative model for attributed dynamic networks that (i) captures network structure dynamics through temporal motifs, and (ii) extends the structural roles of nodes in motifs to roles that generate content embeddings. Our new proposed model is the first to generate synthetic dynamic networks and sample content embeddings based on motif node roles. To the best of our knowledge, it is the only attributed dynamic network model that can generate <em>new</em> content embeddings—not observed in the input graph, but still similar to that of the input graph. Our results show that modeling the network attributes with higher-order structures (e.g., motifs) improves the quality of the networks generated.</p> <p><br></p> <p>The generative models proposed address the difficulty of finding readily-available datasets of dynamic networks—attributed or not. This work will also allow others to: (i) generate networks that they can share without divulging individual’s private data, (ii) benchmark model performance, and (iii) explore model generalization on a broader range of conditions, among other uses. Finally, the evaluation measures proposed will elucidate models, allowing fellow researchers to push forward in these domains.</p>

The PhD. thesis deals with the morphostructural evolution of the valley network of the Biala Lądecka river, (further refer as BL) during the Late Cenozoic. In this work, the selected methods as geomorphological research (morphostructural analysis, geomorphological mapping), structural-geological research (paleostres analysis) and geophysical survey on selected sites were used, to answer the questions of river basin development and its relationship with predicted tectonic activity within the area, as well as with anticipated or already known paleohydrographic changes. The BL basin is situated within the Rychlebské hory Mts. / Góry Złote (northern and eastern parts of the basin), Králický Sněžník (southern part) and the Krowiarki Mts. (western part) in Poland. The Marginal Sudetic fault zone, which represents one of the most important tectonic zones in the Central Europe, passes in vicinity of the study area as well as the regionally important Bělský fault, which passes directly through the BL basin. The results of the analysis indicate that the BL basin has undergone very complex development due to tectonic movements since Miocene up-to-day. Based on the results of the paleostress analysis, which was performed on the dated volcanites in Lutynia - Lądek Zdrój area, the parameters of the palaostress...