Network theory

About: Network theory is a research topic. Over the lifetime, 2257 publications have been published within this topic receiving 109864 citations.

Towards a Network Description of Interbank Payment Flows

Abstract: We present the application of network theory to the Dutch payment system with specific attention to systemic stability. The network nodes comprise of domestic banks, large international banks and TARGET countries, the links are established by payments between the nodes. Traditional measures (transactions, values) first show payments are relatively well behaved through time and that the system does not contain a group of significant structural net receivers or payers among the participant institutions. Structural circular flows do, however, exist in the system, most prominently a large circular net flow between TARGET countries. Analysis of the properties of prominent network measures over time shows that fast network development takes place in the early phase of network formation of about one hour and slower development afterwards. The payment network is small (in actual nodes and links), compact (in path length and eccentricity) and sparse (in connectivity) for all time periods. In the long run, a mere 12% of the possible number of interbank connections is ever used and banks are on average only 2 steps apart. Relations in the network tend to be reciprocal. Our results also indicate that the network is susceptible to directed attacks. In a final section we show that the recent sub prime' turmoil in credit markets has not materially affected the network structure.

The formation of economic networks: a proximity approach

Abstract: Over the last two decades, scholars from different scientific fields have increasingly acknowledged that network structures play a crucial role in economic activities (Granovetter, 1985; Powell et al., 2005; Cowan et al., 2007; Jackson, 2008). Network structures refer to the particular way relations are organized, which is crucial for the exchange of resources that do not circulate easily through the market, like strategic information, tacit knowledge and trust. Therefore, considerable attention has been given to the analysis of structural properties of networks that favour entrepreneurship, innovation processes, technological change or employment dynamics. A major research concern is to understand how these structures are formed. Since networks are a crucial determinant of economic performance, it is important to understand where this set of relations comes from. This means there is a need to investigate the underlying mechanisms of network formation, i.e. the driving forces behind network structures. Two different, although complementary, perspectives can be adopted to explain relational dynamics. The first driver operates at a 'structural' level and refers to the endogenous mechanisms of network formation (Gluckler, 2007; Rivera et al., 2010). Network theory explains how the organization of relationships influences the creation of further relations. The second driver focuses on the 'individual' level and analyses the unequal embeddedness of actors in networks. In this view, it is argued that the tendency to create relations is related to individual characteristics of actors (Cassiman and Veugelers, 2002).

Computing Top-k Closeness Centrality Faster in Unweighted Graphs

Abstract: Given a connected graph G = (V,E), the closeness centrality of a vertex v is defined as (n-1 / \Sigma_{w \in V} d(v,w). This measure is widely used in the analysis of real-world complex networks, and the problem of selecting the k most central vertices has been deeply analysed in the last decade. However, this problem is computationally not easy, especially for large networks: in the first part of the paper, we prove that it is not solvable in time O(|E|^{2-epsilon) on directed graphs, for any constant epsilon > 0, under reasonable complexity assumptions. Furthermore, we propose a new algorithm for selecting the k most central nodes in a graph: we experimentally show that this algorithm improves significantly both the textbook algorithm, which is based on computing the distance between all pairs of vertices, and the state of the art. For example, we are able to compute the top k nodes in few dozens of seconds in real-world networks with millions of nodes and edges. Finally, as a case study, we compute the 10 most central actors in the IMDB collaboration network, where two actors are linked if they played together in a movie, and in the Wikipedia citation network, which contains a directed edge from a page p to a page q if p contains a link to q.

Networks, Creativity, and Time: Staying Creative through Brokerage and Network Rejuvenation

Abstract: In this paper, we adopt a dynamic perspective on networks and creativity to propose that the oft-theorized creative benefits of open networks and heterogeneous content are less likely to be...