Network theory

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

Almost linear-time algorithms for adaptive betweenness centrality using hypergraph sketches

Abstract: Betweenness centrality measures the importance of a vertex by quantifying the number of times it acts as a midpoint of the shortest paths between other vertices. This measure is widely used in network analysis. In many applications, we wish to choose the k vertices with the maximum adaptive betweenness centrality, which is the betweenness centrality without considering the shortest paths that have been taken into account by already-chosen vertices. All previous methods are designed to compute the betweenness centrality in a fixed graph. Thus, to solve the above task, we have to run these methods $k$ times. In this paper, we present a method that directly solves the task, with an almost linear runtime no matter how large the value of k. Our method first constructs a hypergraph that encodes the betweenness centrality, and then computes the adaptive betweenness centrality by examining this graph. Our technique can be utilized to handle other centrality measures. We theoretically prove that our method is very accurate, and experimentally confirm that it is three orders of magnitude faster than previous methods. Relying on the scalability of our method, we experimentally demonstrate that strategies based on adaptive betweenness centrality are effective in important applications studied in the network science and database communities.

Taking: A Study in Lexical Network Theory

Abstract: Proceedings of the Thirteenth Annual Meeting of the Berkeley Linguistics Society (1987), pp. 195-206

From Structure to Activity: Using Centrality Measures to Predict Neuronal Activity.

Abstract: It is clear that the topological structure of a neural network somehow determines the activity of the neurons within it. In the present work, we ask to what extent it is possible to examine the structural features of a network and learn something about its activity? Specifically, we consider how the centrality (the importance of a node in a network) of a neuron correlates with its firing rate. To investigate, we apply an array of centrality measures, including In-Degree, Closeness, Betweenness, Eigenvector, Katz, PageRank, Hyperlink-Induced Topic Search (HITS) and NeuronRank to Leaky-Integrate and Fire neural networks with different connectivity schemes. We find that Katz centrality is the best predictor of firing rate given the network structure, with almost perfect correlation in all cases studied, which include purely excitatory and excitatory–inhibitory networks, with either homogeneous connections or a small-world structure. We identify the properties of a network which will cause this correlation to...

Views on inter-enterprise relationships

Abstract: The paper gives an overview of major scientific approaches to inter-enterprise relationships. A distinction is made between approaches that focus on business process integration (Supply Chain Management, Costing approaches), approaches that focus on organizational collaboration (Transaction Cost Theory, Agency Theory), and approaches that focus on the business environment (Strategic Management, Network Theory, Resource Dependency Theory). Although these approaches are different with regard to their perspectives on collaboration, variables and methods, the paper shows that they are complementary tools in the analysis and design of inter-enterprise relationships.