
Asymptotic degree distributions in large homogeneous random networks: A little theory and a counterexample
In random graph models, the degree distribution of an individual node s...
read it

Centrality measures for graphons
Graphs provide a natural mathematical abstraction for systems with pairw...
read it

Seedless Graph Matching via Tail of Degree Distribution for Correlated ErdosRenyi Graphs
The graph matching problem refers to recovering the nodetonode corresp...
read it

Asymptotic degree distributions in random threshold graphs
We discuss several limiting degree distributions for a class of random t...
read it

Efficient Reassembling of ThreeRegular Planar Graphs
A reassembling of a simple graph G = (V,E) is an abstraction of a proble...
read it

Efficient Recognition of Graph Languages
Graph transformation is the rulebased modification of graphs, and is a ...
read it

Latent Poisson models for networks with heterogeneous density
Empirical networks are often globally sparse, with a small average numbe...
read it
Ranking RDF Instances in Degreedecoupled RDF Graphs
In the last decade, RDF emerged as a new kind of standardized data model, and a sizable body of knowledge from fields such as Information Retrieval was adapted to RDF graphs. One common task in graph databases is to define an importance score for nodes based on centrality measures, such as PageRank and HITS. The majority of the strategies highly depend on the degree of the node. However, in some RDF graphs, called degreedecoupled RDF graphs, the notion of importance is not directly related to the node degree. Therefore, this work first proposes three novel node importance measures, named InfoRank I, II and III, for degreedecoupled RDF graphs. It then compares the proposed measures with traditional PageRank and other familiar centrality measures, using with an IMDb dataset.
READ FULL TEXT
Comments
There are no comments yet.