88,825 research outputs found
Convergence of rank based degree-degree correlations in random directed networks
We introduce, and analyze, three measures for degree-degree dependencies,
also called degree assortativity, in directed random graphs, based on
Spearman's rho and Kendall's tau. We proof statistical consistency of these
measures in general random graphs and show that the directed configuration
model can serve as a null model for our degree-degree dependency measures.
Based on these results we argue that the measures we introduce should be
preferred over Pearson's correlation coefficients, when studying degree-degree
dependencies, since the latter has several issues in the case of large networks
with scale-free degree distributions
A Formal Account of the Open Provenance Model
On the Web, where resources such as documents and data are published, shared, transformed, and republished, provenance is a crucial piece of metadata that would allow users to place their trust in the resources they access. The Open Provenance Model (OPM) is a community data model for provenance that is designed to facilitate the meaningful interchange of provenance information between systems. Underpinning OPM is a notion of directed graph, where nodes represent data products and processes involved in past computations, and edges represent dependencies between them; it is complemented by graphical inference rules allowing new dependencies to be derived. Until now, however, the OPM model was a purely syntactical endeavor. The present paper extends OPM graphs with an explicit distinction between precise and imprecise edges. Then a formal semantics for the thus enriched OPM graphs is proposed, by viewing OPM graphs as temporal theories on the temporal events represented in the graph. The original OPM inference rules are scrutinized in view of the semantics and found to be sound but incomplete. An extended set of graphical rules is provided and proved to be complete for inference. The paper concludes with applications of the formal semantics to inferencing in OPM graphs, operators on OPM graphs, and a formal notion of refinement among OPM graphs
Quantifying structure in networks
We investigate exponential families of random graph distributions as a
framework for systematic quantification of structure in networks. In this paper
we restrict ourselves to undirected unlabeled graphs. For these graphs, the
counts of subgraphs with no more than k links are a sufficient statistics for
the exponential families of graphs with interactions between at most k links.
In this framework we investigate the dependencies between several observables
commonly used to quantify structure in networks, such as the degree
distribution, cluster and assortativity coefficients.Comment: 17 pages, 3 figure
GGDs: Graph Generating Dependencies
We propose Graph Generating Dependencies (GGDs), a new class of dependencies
for property graphs. Extending the expressivity of state of the art constraint
languages, GGDs can express both tuple- and equality-generating dependencies on
property graphs, both of which find broad application in graph data management.
We provide the formal definition of GGDs, analyze the validation problem for
GGDs, and demonstrate the practical utility of GGDs.Comment: 5 page
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