The egocentric network of the edge u-v with no common neighbors for the end vertices would be a truly

bipartite graph (as in Figure 1) as the only edges in the graph would be edges connecting vertices in partition-u to vertices in partition-u.

It was proved that every 3-connected

bipartite graph admits a vertex-coloring S-edge-weighting for S = {1,2} (H.

We construct a

bipartite graph to model the relationship between codebooks of different feature set.

The super subdivision graph S (G) is obtained from G by replacing every edge e of G by a complete

bipartite graph K2,m (m 2) in such a way that the ends of e are merged with the two vertices of the 2-vertices part of K2,m after removing the edge e from G.

1: A biclique C = (S', A') is a subgraph of G induced by a pair of two disjoint subsets S' [subset or equal to] S, A' [subset or equal to] A, such that [for all]s [member of] S', a [member of] A, (s, a) [member of] E, meaning that a biclique in a

bipartite graph is a complete bipartite subgraph that contains all permissible edges.

This method combines diverse superpixels using a principled

bipartite graph partitioning framework, and it needs determine how many pieces could be classified in advance.

In the Graph Theory, a

bipartite Graph is a Graph G = (N, E) whose vertices can be separated into two disjoint sets U and V so that the edges only can connect vertices of a set with other vertices,

Bipartite graphs are often represented graphically by two columns (or rows) of vertices and edges joining vertices of different columns (or rows).

In a

bipartite graph, the vertex set is partitioned into independent sets [V.

Gene expression datasets can be represented in

bipartite graph where:

n])} is called bigraphical if there exists a

bipartite graph G with bipartition (M,N), where M = {[x.

Topics include a survey of computational approaches to reconstruct and partition biological networks, modeling for evolving biological networks, the structure of an evolving random

bipartite graph, and network-based information synergy analysis for Alzheimer disease.

Students and features could be modelled as a

bipartite graph and a simultaneous clustering could be posed as a

bipartite graph partitioning problem.