**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**2752

# Search results for: Graph Structure

##### 2752 Using Spectral Vectors and M-Tree for Graph Clustering and Searching in Graph Databases of Protein Structures

**Authors:**
Do Phuc,
Nguyen Thi Kim Phung

**Abstract:**

**Keywords:**
Eigenvalues,
m-tree,
graph database,
protein
structure,
spectra graph theory.

##### 2751 Topological Queries on Graph-structured XML Data: Models and Implementations

**Authors:**
Hongzhi Wang,
Jianzhong Li,
Jizhou Luo

**Abstract:**

**Keywords:**
XML,
Graph Structure,
Topological query.

##### 2750 2D Structured Non-Cyclic Fuzzy Graphs

**Authors:**
T. Pathinathan,
M. Peter

**Abstract:**

**Keywords:**
Double layered fuzzy graph,
double layered non-cyclic fuzzy graph,
strong,
order,
degree and size.

##### 2749 Protein Graph Partitioning by Mutually Maximization of cycle-distributions

**Authors:**
Frank Emmert Streib

**Abstract:**

**Keywords:**
Graph partitioning,
unweighted graph,
protein domains.

##### 2748 Detecting Community Structure in Amino Acid Interaction Networks

**Authors:**
Omar GACI,
Stefan BALEV,
Antoine DUTOT

**Abstract:**

In this paper we introduce the notion of protein interaction network. This is a graph whose vertices are the protein-s amino acids and whose edges are the interactions between them. Using a graph theory approach, we observe that according to their structural roles, the nodes interact differently. By leading a community structure detection, we confirm this specific behavior and describe thecommunities composition to finally propose a new approach to fold a protein interaction network.

**Keywords:**
interaction network,
protein structure,
community structure detection.

##### 2747 Efficient Filtering of Graph Based Data Using Graph Partitioning

**Authors:**
Nileshkumar Vaishnav,
Aditya Tatu

**Abstract:**

**Keywords:**
Graph signal processing,
graph partitioning,
inverse
filtering on graphs,
algebraic signal processing.

##### 2746 A Study and Implementation of On-line Learning Diagnosis and Inquiry System

**Authors:**
YuLung Wu

**Abstract:**

**Keywords:**
Knowledge Structure Graph,
On-line LearningDiagnosis

##### 2745 A Neighborhood Condition for Fractional k-deleted Graphs

**Authors:**
Sizhong Zhou,
Hongxia Liu

**Abstract:**

Abstract–Let k ≥ 3 be an integer, and let G be a graph of order n with n ≥ 9k +3- 42(k - 1)2 + 2. Then a spanning subgraph F of G is called a k-factor if dF (x) = k for each x ∈ V (G). A fractional k-factor is a way of assigning weights to the edges of a graph G (with all weights between 0 and 1) such that for each vertex the sum of the weights of the edges incident with that vertex is k. A graph G is a fractional k-deleted graph if there exists a fractional k-factor after deleting any edge of G. In this paper, it is proved that G is a fractional k-deleted graph if G satisfies δ(G) ≥ k + 1 and |NG(x) ∪ NG(y)| ≥ 1 2 (n + k - 2) for each pair of nonadjacent vertices x, y of G.

**Keywords:**
Graph,
minimum degree,
neighborhood union,
fractional k-factor,
fractional k-deleted graph.

##### 2744 The Extremal Graph with the Largest Merrifield-Simmons Index of (n, n + 2)-graphs

**Authors:**
M. S. Haghighat,
A. Dolati,
M. Tabari,
E. Mohseni

**Abstract:**

The Merrifield-Simmons index of a graph G is defined as the total number of its independent sets. A (n, n + 2)-graph is a connected simple graph with n vertices and n + 2 edges. In this paper we characterize the (n, n+2)-graph with the largest Merrifield- Simmons index. We show that its Merrifield-Simmons index i.e. the upper bound of the Merrifield-Simmons index of the (n, n+2)-graphs is 9 × 2n-5 +1 for n ≥ 5.

**Keywords:**
Merrifield-Simmons index,
(n,
n+2)-graph.

##### 2743 N-Sun Decomposition of Complete, Complete Bipartite and Some Harary Graphs

**Authors:**
R. Anitha,
R. S. Lekshmi

**Abstract:**

**Keywords:**
Decomposition,
Hamilton cycle,
n-sun graph,
perfect matching,
spanning tree.

##### 2742 The Diameter of an Interval Graph is Twice of its Radius

**Authors:**
Tarasankar Pramanik,
Sukumar Mondal,
Madhumangal Pal

**Abstract:**

In an interval graph G = (V,E) the distance between two vertices u, v is de£ned as the smallest number of edges in a path joining u and v. The eccentricity of a vertex v is the maximum among distances from all other vertices of V . The diameter (δ) and radius (ρ) of the graph G is respectively the maximum and minimum among all the eccentricities of G. The center of the graph G is the set C(G) of vertices with eccentricity ρ. In this context our aim is to establish the relation ρ = δ 2 for an interval graph and to determine the center of it.

**Keywords:**
Interval graph,
interval tree,
radius,
center.

##### 2741 Bottom Up Text Mining through Hierarchical Document Representation

**Authors:**
Y. Djouadi.,
F. Souam.

**Abstract:**

**Keywords:**
Graph based association rules mining,
Hierarchical
document structure,
Text mining.

##### 2740 Completion Number of a Graph

**Authors:**
Sudhakar G

**Abstract:**

In this paper a new concept of partial complement of a graph G is introduced and using the same a new graph parameter, called completion number of a graph G, denoted by c(G) is defined. Some basic properties of graph parameter, completion number, are studied and upperbounds for completion number of classes of graphs are obtained , the paper includes the characterization also.

**Keywords:**
Completion Number,
Maximum Independent subset,
Partial complements,
Partial self complementary

##### 2739 On Fractional (k,m)-Deleted Graphs with Constrains Conditions

**Authors:**
Sizhong Zhou,
Hongxia Liu

**Abstract:**

Let G be a graph of order n, and let k 2 and m 0 be two integers. Let h : E(G) [0, 1] be a function. If e∋x h(e) = k holds for each x V (G), then we call G[Fh] a fractional k-factor of G with indicator function h where Fh = {e E(G) : h(e) > 0}. A graph G is called a fractional (k,m)-deleted graph if there exists a fractional k-factor G[Fh] of G with indicator function h such that h(e) = 0 for any e E(H), where H is any subgraph of G with m edges. In this paper, it is proved that G is a fractional (k,m)-deleted graph if (G) k + m + m k+1 , n 4k2 + 2k − 6 + (4k 2 +6k−2)m−2 k−1 and max{dG(x), dG(y)} n 2 for any vertices x and y of G with dG(x, y) = 2. Furthermore, it is shown that the result in this paper is best possible in some sense.

**Keywords:**
Graph,
degree condition,
fractional k-factor,
fractional (k,
m)-deleted graph.

##### 2738 Metric Dimension on Line Graph of Honeycomb Networks

**Authors:**
M. Hussain,
Aqsa Farooq

**Abstract:**

**Keywords:**
Resolving set,
metric dimension,
honeycomb network,
line graph.

##### 2737 A General Model for Amino Acid Interaction Networks

**Authors:**
Omar Gaci,
Stefan Balev

**Abstract:**

**Keywords:**
interaction network,
protein structure,
small-world network.

##### 2736 Comparison of Full Graph Methods of Switched Circuits Solution

**Authors:**
Zdeňka Dostálová,
David Matoušek,
Bohumil Brtnik

**Abstract:**

**Keywords:**
Switched capacitors of two phases,
switched
currents of two phases,
transformation graph,
two-graph,
Mason's
formula,
voltage transfer,
summary graph.

##### 2735 Speedup Breadth-First Search by Graph Ordering

**Abstract:**

Breadth-First Search (BFS) is a core graph algorithm that is widely used for graph analysis. As it is frequently used in many graph applications, improving the BFS performance is essential. In this paper, we present a graph ordering method that could reorder the graph nodes to achieve better data locality, thus, improving the BFS performance. Our method is based on an observation that the sibling relationships will dominate the cache access pattern during the BFS traversal. Therefore, we propose a frequency-based model to construct the graph order. First, we optimize the graph order according to the nodes’ visit frequency. Nodes with high visit frequency will be processed in priority. Second, we try to maximize the child nodes’ overlap layer by layer. As it is proved to be NP-hard, we propose a heuristic method that could greatly reduce the preprocessing overheads.We conduct extensive experiments on 16 real-world datasets. The result shows that our method could achieve comparable performance with the state-of-the-art methods while the graph ordering overheads are only about 1/15.

**Keywords:**
Breadth-first search,
BFS,
graph ordering,
graph algorithm.

##### 2734 On Detour Spectra of Some Graphs

**Authors:**
S.K.Ayyaswamy,
S.Balachandran

**Abstract:**

**Keywords:**
Detour eigenvalue (of a graph),
detour spectrum(of a graph),
detour energy(of a graph),
detour - equienergetic graphs.

##### 2733 Performance Analysis of Learning Automata-Based Routing Algorithms in Sparse Graphs

**Authors:**
Z.Farhadpour,
Mohammad.R.Meybodi

**Abstract:**

**Keywords:**
Learning automata,
routing,
algorithm,
sparse
graph

##### 2732 Steady State of Passive and Active Suspensions in the Physical Domain

**Authors:**
Gilberto Gonzalez-A,
Jorge Madrigal

**Abstract:**

**Keywords:**
Bond graph,
steady state,
active suspension.

##### 2731 Face Recognition using a Kernelization of Graph Embedding

**Authors:**
Pang Ying Han,
Hiew Fu San,
Ooi Shih Yin

**Abstract:**

**Keywords:**
Face recognition,
Fisher discriminant,
graph
embedding,
kernelization.

##### 2730 Reductions of Control Flow Graphs

**Authors:**
Robert Gold

**Abstract:**

Control ﬂow graphs are a well-known representation of the sequential control ﬂow structure of programs with a multitude of applications. Not only single functions but also sets of functions or complete programs can be modeled by control ﬂow graphs. In this case the size of the graphs can grow considerably and thus makes it difﬁcult for software engineers to analyze the control ﬂow. Graph reductions are helpful in this situation. In this paper we deﬁne reductions to subsets of nodes. Since executions of programs are represented by paths through the control ﬂow graphs, paths should be preserved. Furthermore, the composition of reductions makes a stepwise analysis approach possible.

**Keywords:**
Control ﬂow graph,
graph reduction.

##### 2729 Analysis of Electrical Networks Using Phasors: A Bond Graph Approach

**Authors:**
Israel Núñez-Hernández,
Peter C. Breedveld,
Paul B. T. Weustink,
Gilberto Gonzalez-A

**Abstract:**

This paper proposes a phasor representation of electrical networks by using bond graph methodology. A so-called phasor bond graph is built up by means of two-dimensional bonds, which represent the complex plane. Impedances or admittances are used instead of the standard bond graph elements. A procedure to obtain the steady-state values from a phasor bond graph model is presented. Besides the presentation of a phasor bond graph library in SIDOPS code, also an application example is discussed.

**Keywords:**
Bond graphs,
phasor theory,
steady-state,
complex
power,
electrical networks.

##### 2728 An Efficient Graph Query Algorithm Based on Important Vertices and Decision Features

**Authors:**
Xiantong Li,
Jianzhong Li

**Abstract:**

**Keywords:**
Decision Feature,
Frequent Feature,
Graph Dataset,
Graph Query

##### 2727 Notes on Fractional k-Covered Graphs

**Authors:**
Sizhong Zhou,
Yang Xu

**Abstract:**

**Keywords:**
graph,
binding number,
fractional k-factor,
fractional k-covered graph.

##### 2726 Application of a Similarity Measure for Graphs to Web-based Document Structures

**Authors:**
Matthias Dehmer,
Frank Emmert Streib,
Alexander Mehler,
Jürgen Kilian,
Max Mühlhauser

**Abstract:**

**Keywords:**
Graph similarity,
hierarchical and directed graphs,
hypertext,
generalized trees,
web structure mining.

##### 2725 Syntactic Recognition of Distorted Patterns

**Authors:**
Marek Skomorowski

**Abstract:**

**Keywords:**
Syntactic pattern recognition,
Distorted patterns,
Random graphs,
Graph grammars.

##### 2724 Automatic Fingerprint Classification Using Graph Theory

**Authors:**
Mana Tarjoman,
Shaghayegh Zarei

**Abstract:**

Using efficient classification methods is necessary for automatic fingerprint recognition system. This paper introduces a new structural approach to fingerprint classification by using the directional image of fingerprints to increase the number of subclasses. In this method, the directional image of fingerprints is segmented into regions consisting of pixels with the same direction. Afterwards the relational graph to the segmented image is constructed and according to it, the super graph including prominent information of this graph is formed. Ultimately we apply a matching technique to compare obtained graph with the model graphs in order to classify fingerprints by using cost function. Increasing the number of subclasses with acceptable accuracy in classification and faster processing in fingerprints recognition, makes this system superior.

**Keywords:**
Classification,
Directional image,
Fingerprint,
Graph,
Super graph.

##### 2723 Graphs with Metric Dimension Two-A Characterization

**Authors:**
Sudhakara G,
Hemanth Kumar A.R

**Abstract:**

In this paper, we define distance partition of vertex set of a graph G with reference to a vertex in it and with the help of the same, a graph with metric dimension two (i.e. β (G) = 2 ) is characterized. In the process, we develop a polynomial time algorithm that verifies if the metric dimension of a given graph G is two. The same algorithm explores all metric bases of graph G whenever β (G) = 2 . We also find a bound for cardinality of any distance partite set with reference to a given vertex, when ever β (G) = 2 . Also, in a graph G with β (G) = 2 , a bound for cardinality of any distance partite set as well as a bound for number of vertices in any sub graph H of G is obtained in terms of diam H .

**Keywords:**
Metric basis,
Distance partition,
Metric dimension.