The Network of Counterparty Risk: Analysing Correlations in OTC Derivatives

Counterparty risk denotes the risk that a party defaults in a bilateral contract. This risk not only depends on the two parties involved, but also on the risk from various other contracts each of these parties holds. In rather informal markets, such as the OTC (over-the-counter) derivative market, institutions only report their aggregated quarterly risk exposure, but no details about their counterparties. Hence, little is known about the diversification of counterparty risk. In this paper, we reconstruct the weighted and time-dependent network of counterparty risk in the OTC derivatives market of the United States between 1998 and 2012. To proxy unknown bilateral exposures, we first study the co-occurrence patterns of institutions based on their quarterly activity and ranking in the official report. The network obtained this way is further analysed by a weighted k-core decomposition, to reveal a core-periphery structure. This allows us to compare the activity-based ranking with a topology-based ranking, to identify the most important institutions and their mutual dependencies. We also analyse correlations in these activities, to show strong similarities in the behavior of the core institutions. Our analysis clearly demonstrates the clustering of counterparty risk in a small set of about a dozen US banks. This not only increases the default risk of the central institutions, but also the default risk of peripheral institutions which have contracts with the central ones. Hence, all institutions indirectly have to bear (part of) the counterparty risk of all others, which needs to be better reflected in the price of OTC derivatives.Comment: 36 pages, 18 figures, 2 table

Quantifying Triadic Closure in Multi-Edge Social Networks

Multi-edge networks capture repeated interactions between individuals. In social networks, such edges often form closed triangles, or triads. Standard approaches to measure this triadic closure, however, fail for multi-edge networks, because they do not consider that triads can be formed by edges of different multiplicity. We propose a novel measure of triadic closure for multi-edge networks of social interactions based on a shared partner statistic. We demonstrate that our operalization is able to detect meaningful closure in synthetic and empirical multi-edge networks, where common approaches fail. This is a cornerstone in driving inferential network analyses from the analysis of binary networks towards the analyses of multi-edge and weighted networks, which offer a more realistic representation of social interactions and relations.Comment: 19 pages, 5 figures, 6 table

Citations Driven by Social Connections? A Multi-Layer Representation of Coauthorship Networks

To what extent is the citation rate of new papers influenced by the past social relations of their authors? To answer this question, we present a data-driven analysis of nine different physics journals. Our analysis is based on a two-layer network representation constructed from two large-scale data sets, INSPIREHEP and APS. The social layer contains authors as nodes and coauthorship relations as links. This allows us to quantify the social relations of each author, prior to the publication of a new paper. The publication layer contains papers as nodes and citations between papers as links. This layer allows us to quantify scientific attention as measured by the change of the citation rate over time. We particularly study how this change depends on the social relations of their authors, prior to publication. We find that on average the maximum value of the citation rate is reached sooner for authors who either published more papers, or who had more coauthors in previous papers. We also find that for these authors the decay in the citation rate is faster, meaning that their papers are forgotten sooner

HYPA: Efficient Detection of Path Anomalies in Time Series Data on Networks

The unsupervised detection of anomalies in time series data has important applications in user behavioral modeling, fraud detection, and cybersecurity. Anomaly detection has, in fact, been extensively studied in categorical sequences. However, we often have access to time series data that represent paths through networks. Examples include transaction sequences in financial networks, click streams of users in networks of cross-referenced documents, or travel itineraries in transportation networks. To reliably detect anomalies, we must account for the fact that such data contain a large number of independent observations of paths constrained by a graph topology. Moreover, the heterogeneity of real systems rules out frequency-based anomaly detection techniques, which do not account for highly skewed edge and degree statistics. To address this problem, we introduce HYPA, a novel framework for the unsupervised detection of anomalies in large corpora of variable-length temporal paths in a graph. HYPA provides an efficient analytical method to detect paths with anomalous frequencies that result from nodes being traversed in unexpected chronological order.Comment: 11 pages with 8 figures and supplementary material. To appear at SIAM Data Mining (SDM 2020

Structure and Dynamics of Collaborative Knowledge Networks

A major means to encode and share scientific knowledge are publications, which cite each other and which are authored by one or more scientists. Citation networks of publications are commonly used to proxy the structure of scientific knowledge. Coauthorship networks are used to represent the social network between collaborating scientists. Yet, these two networks are rarely considered together even though they are interconnected. The multilayer collaborative knowledge network that results from combining the two allows us to study how the social relations among authors affect the structure and dynamics of the citation layer. To address this issue, we apply network theory. In the first part, we analyse the structure of collaborative knowledge networks. Our goal is to study dyadic interactions between individual pairs of authors in the context of the whole network. The ability to perform such a study will allow investigating individual citation behaviours of authors, as well as their deviations from community standards. For this, we develop a novel statistical method to extract how much authors' citations to each other deviate from a certain expectation. It builds on three methodological contributions. The first one is a flexible probabilistic model for complex networks that can encode heterogeneity in dyadic interactions. The second one is a procedure to formulate statistical null models for networks that respect temporal ordering of nodes and community structures. The third contribution is a new nonparametric probabilistic measure to quantify the deviation of an observed value from a distribution. With this method at hand, we present the deviations of authors' citations from the expectation formed based on the behaviour of the community at large. We also show how to use these deviations to highlight the intricate sub-community structures within the larger communities. In the second part, we study the evolution of collaborative knowledge networks. We show that the often neglected social layer has a significant effect on the citation layer. Particularly, we find that the overall likelihood of a publication to be cited scales with the number of previous publications by its authors, as well as with the number of their previous collaborators. To obtain this finding, we develop a method to fit and compare probabilistic growth models of multilayer networks. We further look into how the citations are distributed over time for a given publication and we find that citations arrive faster for the authors with more collaborators and more publications. The scientific contribution of this thesis is twofold. First, we develop novel statistical methods to study evolving multilayer complex networks. These methods can be applied in various fields. Second, we apply these methods to study citation and collaboration networks from the unified viewpoint of a multilayer network, which leads us to findings that could not be reached by merely considering the two layers in isolation