A total k-labeling is a function fe from the edge set to {1, 2, . , ke} and fv vertex {0, 4, 2kv}, where k = max{ke, 2kv}. distance irregular reflexive of graph G k-labeling, if for every two different vertices u 0 G, w(u) 6= w(u ), Σui∈N(u)fv(ui) + Σuv∈E(G)fe(uv). The minimum which has k-labelling called strength denoted by Dref (G). In this paper, we determine exact value some connected graph...

This paper studies the problem of upper bounding number independent sets in a graph, expressed terms its degree distribution. For bipartite regular graphs, Kahn (2001) established tight bound using an information-theoretic approach, and he also conjectured for general graphs. His was recently proved by Sah et al. (2019), different techniques not involving information theory. The main contributi...

Let G = (V;E) be a graph. A total labeling ψ : V ⋃ E → {1, 2, ....k} is called totally irregular k-labeling of if every two distinct vertices u and v in (G) satisfy wt(u) ≠wt(v); edges u1u2 v1v2 E(G) wt(u1u2) ≠ wt(v1v2); where (u) + ∑uv∊E(G) ψ(uv) ψ(u1) ψ(u1u2) ψ(u2): The minimum k for which graph has the irregularity strength G, denoted by ts(G): In this paper, we determine exact value cubic g...

Despite of the fact that graph based methods are gaining more and more popularity in different scientific areas, it has to be considered that the choice of an appropriate algorithm for a given application is still the most crucial task. The lack of a large database of graphs makes the task of comparing the performance of different graph matching algorithms difficult, and often the selection of ...

BACKGROUND In disease surveillance, the prospective space-time permutation scan statistic is commonly used for the early detection of disease outbreaks. The scanning window that defines potential clusters of diseases is cylindrical in shape, which does not allow incorporating into the cluster shape potential factors that can contribute to the spread of the disease, such as information about roa...

Graph data models provide flexibility and extensibility that makes them well-suited to modelling data that may be irregular, complex, and evolving in structure and content. However, a consequence of this is that users may not be familiar with the full structure of the data, which itself may be changinng over time, making it hard for users to formulate queries that precisely match the data graph...

We present proximity graphs based approach to hierarchical image segmentation and vectorization. Our method produces an irregular pyramid that contains a stack of vectorized images of successively reduced levels of detail. We are jumping off from the over-segmented image represented by polygonal patches, which are attributed with spectral information. We employ constrained Delaunay triangulatio...

The Low Density Parity Check (LDPC) codes have been widely acclaimed in recent years for their nearcapacity performance, they have not found their way into many important applications. For some cases, this is due to their increased decoding complexity relative to the classical coding techniques. For other cases, this is due to their inability to reach very low bit error rates at low signal-to-n...

Algorithms that nd good partitionings of highly unstructured graphs are critical in developing eÆcient algorithms for problems in a variety of domains such as scienti c simulations that require solution to large sparse linear systems, VLSI design, and data mining. Even though this problem is NP-hard, eÆcient multi-level algorithms have been developed that can nd good partitionings of static irr...