Real world systems like biological, social, technological, infrastructural and many others can be modeled as networks. The field of network science aims to study these complex networks and understand their structure and dynamics. A common feature of networks across domains is the distribution of the degree of the nodes according to a power-law (scale invariance). As a consequence of this skewness, the high degree nodes dominate the properties of these networks. The rich-club phenomenon is observed when the high degree or the rich nodes of the network prefer to connect amongst themselves. In the first part, the thesis investigates the rich-club phenomenon in higher order neighborhoods of the network by providing an elegant quantification using a geodesic distance based approach. This quantification helped in identifying networks where the trend and intensity of the rich-club phenomenon is significantly different in higher order neighborhoods compared to the immediate neighbors. The thesis also proposes a quantification of the importance of the non-rich nodes in the communication structure of the rich nodes, and broadly classify networks into core-periphery or cellular. Further a lack of universality is noticed in the structure of the networks belonging to a particular domain. It has been observed in the previous literature that the rich club connectivity dominates assortativity, a measure quantifying the mixing patterns in complex networks. Thus, assortativity is biased. To overcome such drawbacks, in the second part of the thesis proposes a novel measure called regularity. The analytical bounds on regularity and formulation of regularity for different network models are provided. Along with this a measure to quantify the mixing patterns of the neighborhood of a node called local regularity is also defined. The analysis on real-world network based on local regularity and degree distribution shows presence of both type of network, uniformly and non-uniformly mixed across different regions. Further normalized regularity is proposed to quantify the extent of preferential mixing in networks discounting the effect of degree distribution.

Das Ziel der kognitiven Neurowissenschaften ist es, Struktur-Funktions-Beziehungen im Gehirn aufzudecken. Durch Fortschritte in der funktionellen Bildgebung und der Graphentheorie konnten zentrale Knotenpunkte im Gehirn identifiziert werden, welche neuronale Informationen integrieren und dementielle Prozesse erklären können. Datenbankgetriebene metaanalytische Methoden wurden genutzt, um tausende Bildgebungsstudien auszuwerten und das funktionelle Profil von Hirnregionen zu dekodieren. Das Systems-Level Decoding zielt darauf ab, eine Seed-Region und mit ihr verbundene kortikale Regionen funktionell zu dekodieren. In Studie 1 wurden thalamische Subregionen und in Studie 2 drei Subregionen des Intraparietalen Sulcus (hIP) als Seed-Regionen verwendet. Studie 3 untersuchte Unterschiede in der Rich-Club-Struktur zwischen Alzheimer-Patienten, Patienten mit milder kognitiver Beeinträchtigung und gesunden Probanden. Es wurden zwei große, Thalamus-zentrierte Systeme identifiziert, die mit autobiographischem Gedächtnis und Nozizeption assoziiert sind; für den hIP wurden neun Systeme, die mit Prozessen wie Arbeitsgedächtnis und numerischem Denken zusammenhängen, gefunden. Studie 3 fand Hinweise, dass periphere Regionen bei Patienten im Vergleich zu gesunden Kontrollen stärker aktiviert sind als Rich-Club-Regionen. Das Systems-Level Decoding lieferte neue Erkenntnisse über die Einbettung des Thalamus und des hIP in kortikale funktionelle Systeme. Allerdings waren die Ergebnisse hinsichtlich der funktionellen Charakterisierung thalamischer Kerne und der Aktivitätsunterschiede in Rich-Club- und peripheren Regionen zwischen Patienten und gesunden Kontrollpersonen begrenzt. Diese Ergebnisse lassen sich möglicherweise durch Limitationen des metaanalytischen Ansatzes erklären. Das Systems-Level Decoding ist insgesamt ein vielversprechender Ansatz für die Formulierung von Hypothesen über Struktur-Funktionsbeziehungen innerhalb der Netzwerkarchitektur des menschlichen Gehirns. / In cognitive neuroscience, there is great interest in unraveling structure-function relationships in the human brain. Advances in functional neuroimaging and graph theory methods have identified key brain nodes relevant for neuronal information integration and functional deficits in degenerative diseases like Alzheimer's. Database-driven meta-analytical methods have also evaluated knowledge from thousands of neuroimaging studies, to decode the functional profile of brain regions. The systems-level decoding aims to identify brain systems that provide insight into the functional characteristics of a seed region and its connected cortical regions. In Study 1, thalamic subregions were used as seed regions. Study 2 applied systems-level decoding to three distinct regions in the intraparietal sulcus (hIP). In Study 3, we attempted to substantiate activation differences in the Rich Club structure between Alzheimer's patients, patients with mild cognitive impairment, and healthy subjects. Two major, thalamus-centered systems associated with autobiographical memory and nociception were identified. Additionally, nine large systems associated with processes such as working memory, numerical cognition, and recognition memory were uncovered for the hIP. Finally, evidence showed that peripheral regions in patients are more activated than central Rich Club regions compared to healthy controls. Systems-level decoding provided significant new insights into the embedding of the thalamus and intraparietal sulcus in functional systems. However, results were limited in the functional characterization of thalamic nuclei and activity differences in Rich Club and peripheral regions between patients and healthy controls. Limitations of the meta-analytical approach may explain these findings. The systems-level decoding represents a promising approach for formulating hypotheses about brain structure-function relationships within the functional network architecture.

Purpose/Aim: To find power structures within the blog network. Material/Method: Using keywords to find all available blog posts about the National Defence Radio Establishment from the blog search engine Twingly, and thereafter using the same blog search engine to find inlinks from other blogs, to those posts. The data is set into the context of the small-world networks models of Duncan J. Watts and rich-club models of Sergi Valverde och Ricard V. Sole. Main results: 5183 unique blogs have written about the subject in 22779 blog posts to which 28128 inlinks from other blogposts are made. Just over one fifth of the blogs are linkted to each other in one big network, where the remaining blogs stand without any ingoing or outgoing links. The first bloggers are the one who continue to write for longer period of time and also the ones who attracts most inlinks. The blogosphere around this subject is highly connected, shows features as one would find in a small-world network, displays a power-law distribution for inlinks and is highly clustered around a few rich- club nodes.

Evolutionary Psychology (EP) tends to be associated with a Massively Modular (MM) cognitive architecture. I argue that EP favors a non-MM cognitive architecture. The main point of dispute is whether central cognition, such as abstract reasoning, exhibits domain-general properties. Partisans of EP argue that domain-specific modules govern central cognition, for it is unclear how the cognitive mind could have evolved domain-generality. In response, I defend a distinction between exogenous and endogenous selection pressures, according to which exogenous pressures tend to select for domain-specificity, whereas the latter, endogenous pressures, select in favor of domain-generality. I draw on models from brain network theory to motivate this distinction, and also to establish that a domain-general, non-MM cognitive architecture is the more parsimonious adaptive solution to endogenous pressures.