Estimating node degree in bait-prey graphs

Estimating node degree in bait-prey graphs

MOTIVATION Proteins work together to drive biological processes in cellular machines. Summarizing global and local properties of the set of protein interactions, the interactome, is necessary for describing cellular systems. We consider a relatively simple per-protein feature of the interactome: the number of interaction partners for a protein, which in graph terminology is the degree of the pr...

DRAFT - - DRAFT - - DRAFT - - DRAFT - - DRAFT - - Bait Prey

Estimating the Degree Centrality Ranking of a Node

Complex networks have gained more attention from the last few years. The size of the real world complex networks, such as online social networks, WWW networks, collaboration networks, is exponentially increasing with time. It is not feasible to completely collect, store and process these networks. In the present work, we propose a method to estimate the degree centrality ranking of a node witho...

Estimating Node Similarity by Sampling Streaming Bipartite Graphs

Bipartite graph data increasingly occurs as a stream of edges that represent transactions, e.g., purchases by retail customers. Applications such as recommender systems employ neighborhood-based measures of node similarity, such as the pairwise number of common neighbors (CN) and related metrics. While the number of node pairs that share neighbors is potentially enormous, in real-word graphs on...

Reciprocal Degree Distance of Grassmann Graphs

Recently, Hua et al. defined a new topological index based on degrees and inverse of distances between all pairs of vertices. They named this new graph invariant as reciprocal degree distance as 1 { , } ( ) ( ( ) ( ))[ ( , )] RDD(G) = u v V G d u  d v d u v , where the d(u,v) denotes the distance between vertices u and v. In this paper, we compute this topological index for Grassmann graphs.