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STARINCOLORING OF COMPLETE BIPARTITE GRAPHS, WHEEL GRAPHS AND PRISM GRAPHS
, 2014
"... A kcoloring of a graph G = (V, E) is a mapping c:V(G) → {1,2,3, … } such that ∈ () ⇒ () ≠ (). In this paper, we have considered a complete bipartite graph ,for all , and proved that the starinchromatic number of , is either + 1 if ≥ or + 1 if > respectively. We h ..."
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A kcoloring of a graph G = (V, E) is a mapping c:V(G) → {1,2,3, … } such that ∈ () ⇒ () ≠ (). In this paper, we have considered a complete bipartite graph ,for all , and proved that the starinchromatic number of , is either + 1 if ≥ or + 1 if > respectively. We
Graceful labeling of bipartite related graphs
"... In this paper we have shown that the the splitting graph of the complete bipartite graph Km, n is graceful and the tensor product of the complete bipartite graph Km, n and a path graph Pk(k> 1) is also graceful. ..."
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In this paper we have shown that the the splitting graph of the complete bipartite graph Km, n is graceful and the tensor product of the complete bipartite graph Km, n and a path graph Pk(k> 1) is also graceful.
Heterogeneous Protection in Regular and Complete Bipartite Networks
, 2009
"... We examine the influence of heterogeneous curing rates for a S I S model, used for malware spreading on the Internet, information dissemination in unreliable networks, and propagation of failures in networks. The topology structures considered are the regular graph which represents the homogenous ..."
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network structures and the complete bipartite graph which represents the hierarchical network structures. We find the threshold in a regular graph with m different curing rates. Further, we consider a complete bipartite graph with 2 curing rates and find the threshold for any distribution of curing
Heterogeneous Protection in Regular and Complete Bipartite Networks (Work in Progress)
"... Abstract. We examine the influence of heterogeneous curing rates for a S I S model, used for malware spreading on the Internet, information dissemination in unreliable networks, and propagation of failures in networks. The topology structures considered are the regular graph which represents the ho ..."
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Cited by 2 (1 self)
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the homogenous network structures and the complete bipartite graph which represents the hierarchical network structures. We find the threshold in a regular graph with m different curing rates. Further, we consider a complete bipartite graph with 2 curing rates and find the threshold for any distribution
Description
, 2012
"... The Desc method takes in the protein complex name of a bipartite graph incidence matrix (usually an ad hoc name) and returns a description of that protein complex. The object to be referenced is an instance of the class yeastData. Desc(object, name) Arguments object name An instance of a subclass o ..."
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The Desc method takes in the protein complex name of a bipartite graph incidence matrix (usually an ad hoc name) and returns a description of that protein complex. The object to be referenced is an instance of the class yeastData. Desc(object, name) Arguments object name An instance of a subclass
Finding community structure in networks using the eigenvectors of matrices
, 2006
"... We consider the problem of detecting communities or modules in networks, groups of vertices with a higherthanaverage density of edges connecting them. Previous work indicates that a robust approach to this problem is the maximization of the benefit function known as “modularity ” over possible div ..."
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Cited by 494 (0 self)
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divisions of a network. Here we show that this maximization process can be written in terms of the eigenspectrum of a matrix we call the modularity matrix, which plays a role in community detection similar to that played by the graph Laplacian in graph partitioning calculations. This result leads us to a
Bipartite graph partitioning and data clustering
 Proc. Int'l Conf. Information and Knowledge Management (CIKM
, 2001
"... ƒ ƒf „ … †D‡i ˆ ‰ ŠŒ ‹ Ž ˆ'†‘k ’ ‹ ‰ “ ”• ‹ †D–˜—& ’ ‰ Ž: ’ ™ ‹ š=›iœ3X( " F17 67 "! # ž " ƒI „ ¡R¢EŠ} ’ ‰ Ž£ “ ‹ šM¤XŽ£†D ’ ‹ ‰Q¥Mš ¦ ’ §m‰, ‹ ›¨œnu "! # ( = 6M+ ( "  p ..."
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Cited by 102 (16 self)
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ƒ ƒf „ … †D‡i ˆ ‰ ŠŒ ‹ Ž ˆ'†‘k ’ ‹ ‰ “ ”• ‹ †D–˜—& ’ ‰ Ž: ’ ™ ‹ š=›iœ3X( " F17 67 "! # ž " ƒI „ ¡R¢EŠ} ’ ‰ Ž£ “ ‹ šM¤XŽ£†D ’ ‹ ‰Q¥Mš ¦ ’ §m‰, ‹ ›¨œnu "! # ( = 6M+ ( "  p
Results 1  10
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93,226