arXiv subjects

Pengli Lu

Publications and source records attributed to Pengli Lu.

11 recordsLinked to original sources

arXiv2021

A novel method based on node correlation to evaluate the important nodes in complex networks

Finding the important nodes in complex networks by topological structure is of great significance to network invulnerability. Several centrality measures have been proposed recently to evaluate the performance of nodes based on their correlation, showing that the interaction between nodes has an influence on the importance of nodes. In this paper, a novel method based on node distribution and global influence in complex networks is proposed. Our main idea is that the importance of nodes being linked not only to the relative position in the network but also to the correlations with each other. The nodes in the complex networks are classified according to the distance matrix, then the correlation coefficient between pairs of nodes is calculated. From the whole perspective in the network, the global similarity centrality (GSC) is proposed based on the relevance and shortest distance between any two nodes. The efficiency, accuracy and monotonicity of the proposed method are analyzed in two artificial datasets and eight real datasets of different sizes. Experimental results show that the performance of GSC method outperforms those current state-of-the-art algorithms.

cs.SI↗

arXiv2020

Distance matrix correlation spectrum of graphs

Let $G$ be a simple, connected graph, $\mathcal{D}(G)$ be the distance matrix of $G$, and $Tr(G)$ be the diagonal matrix of vertex transmissions of $G$. The distance Laplacian matrix and distance signless Laplacian matrix of $G$ are defined by $\mathcal{L}(G) = Tr(G)-\mathcal{D}(G)$ and $\mathcal{Q}(G) = Tr(G)+\mathcal{D}(G)$, respectively. The eigenvalues of $\mathcal{D}(G)$, $\mathcal{L}(G)$ and $\mathcal{Q}(G)$ is called the $\mathcal{D}-$spectrum, $\mathcal{L}-$spectrum and $\mathcal{Q}-$spectrum, respectively. The generalized distance matrix of $G$ is defined as $\mathcal{D}_α(G)=αTr(G)+(1-α)\mathcal{D}(G),~0\leqα\leq1$, and the generalized distance spectral radius of $G$ is the largest eigenvalue of $\mathcal{D}_α(G)$. In this paper, we give a complete description of the $\mathcal{D}-$spectrum, $\mathcal{L}-$spectrum and $\mathcal{Q}-$spectrum of some graphs obtained by operations. In addition, we present some new upper and lower bounds on the generalized distance spectral radius of $G$ and of its line graph $L(G)$, based on other graph-theoretic parameters, and characterize the extremal graphs. Finally, we study the generalized distance spectrum of some composite graphs.

math.CO↗

arXiv2020

Extremality of graph entropy based on Laplacian degrees of k-uniform hypergraphs

The graph entropy describes the structural information of graph. Motivated by the definition of graph entropy in general graphs, the graph entropy of hypergraphs based on Laplacian degree are defined. Some results on graph entropy of simple graphs are extended to k-uniform hypergraphs. Using an edge-moving operation, the maximum and minimum graph entropy based on Laplacian degrees are determined in k-uniform hypertrees, unicyclic k-uniform hypergraphs, bicyclic k-uniform hypergraphs and k-uniform chemical hypertrees, respectively, and the corresponding extremal graphs are determined.

math.CO↗

arXiv2020

Two new methods for identifying proteins based on the domain protein complexes and topological properties

The recognition of essential proteins not only can help to understand the mechanism of cell operation, but also help to study the mechanism of biological evolution. At present, many scholars have been discovering essential proteins according to the topological structure of protein network and complexes. While some proteins still can not be recognized. In this paper, we proposed two new methods complex degree centrality (CDC) and complex in-degree and betweenness definition (CIBD) which integrate the local character of protein complexes and topological properties to determine the essentiality of proteins. First, we give the definitions of complex average centrality (CAC) and complex hybrid centrality (CHC) which both describe the properties of protein complexes. Then we propose these new methods CDC and CIBD based on CAC and CHC definitions. In order to access these two methods, different Protein-Protein Interaction (PPI) networks of Saccharomyces cerevisiae, DIP, MIPS and YMBD are used as experimental materials. Experimental results in networks show that the methods of CDC and CIBD can help to improve the precision of predicting essential proteins.

q-bio.MN↗

arXiv2020

Ranking the spreading influence of nodes in complex networks based on mixing degree centrality and local structure

The safety and robustness of the network have attracted the attention of people from all walks of life, and the damage of several key nodes will lead to extremely serious consequences. In this paper, we proposed the clustering H-index mixing (CHM) centrality based on the H- index of the node itself and the relative distance of its neighbors. Starting from the node itself and combining with the topology around the node, the importance of the node and its spreading capability were determined. In order to evaluate the performance of the proposed method, we use Susceptible-Infected-Recovered (SIR) model, monotonicity and resolution as the evaluation standard of experiment. Experimental results in artificial networks and real-world networks show that CHM centrality has excellent performance in identifying node importance and its spreading capability.

physics.soc-ph↗

arXiv2020

A mixed clustering coefficient centrality for identifying essential proteins

Essential protein plays a crucial role in the process of cell life. The identification of essential proteins can not only promote the development of drug target technology, but also contribute to the mechanism of biological evolution. There are plenty of scholars who pay attention to discovering essential proteins according to the topological structure of protein network and biological information. The accuracy of protein recognition still demands to be improved. In this paper, we propose a method which integrate the clustering coefficient in protein complexes and topological properties to determine the essentiality of proteins. First, we give the definition of In-clustering coefficient (IC) to describe the properties of protein complexes. Then we propose a new method, complex edge and node clustering coefficient (CENC) to identify essential proteins. Different Protein-Protein Interaction (PPI) networks of Saccharomyces cerevisiae, MIPS and DIP are used as experimental materials. Through some experiments of logistic regression model, the results show that the method of CENC can promote the ability of recognizing essential proteins, by comparing with the existing methods DC, BC, EC, SC, LAC, NC and the recent method UC.

q-bio.MN↗

arXiv2020

EMH: Extended Mixing H-index centrality for identification important users in social networks based on neighborhood diversity

The rapid expansion of social network provides a suitable platform for users to deliver messages. Through the social network, we can harvest resources and share messages in a very short time. The developing of social network has brought us tremendous conveniences. However, nodes that make up the network have different spreading capability, which are constrained by many factors, and the topological structure of network is the principal element. In order to calculate the importance of nodes in network more accurately, this paper defines the improved H-index centrality (IH) according to the diversity of neighboring nodes, then uses the cumulative centrality (MC) to take all neighboring nodes into consideration, and proposes the extended mixing H-index centrality (EMH). We evaluate the proposed method by Susceptible-Infected-Recovered (SIR) model and monotonicity which are used to assess accuracy and resolution of the method, respectively. Experimental results indicate that the proposed method is superior to the existing measures of identifying nodes in different networks.

cs.SI↗

arXiv2014

Laplacian spectral characterization of dumbbell graphs and theta graphs

Let $P_n$ and $C_n$ denote the path and cycle on $n$ vertices respectively. The dumbbell graph, denoted by $D_{p,k,q}$, is the graph obtained from two cycles $C_p$, $C_q$ and a path $P_{k+2}$ by identifying each pendant vertex of $P_{k+2}$ with a vertex of a cycle respectively. The theta graph, denoted by $Θ_{r,s,t}$, is the graph formed by joining two given vertices via three disjoint paths $P_{r}$, $P_{s}$ and $P_{t}$ respectively. In this paper, we prove that all dumbbell graphs as well as theta graphs are determined by their Laplacian spectra.

math.CO↗

arXiv2013

Laplacian spectral characterization of some double starlike trees

A tree is called double starlike if it has exactly two vertices of degree greater than two. Let $H(p,n,q)$ denote the double starlike tree obtained by attaching $p$ pendant vertices to one pendant vertex of the path $P_n$ and $q$ pendant vertices to the other pendant vertex of $P_n$. In this paper, we prove that $H(p,n,q)$ is determined by its Laplacian spectrum.

math.CO↗

arXiv2013

Spectra of the subdivision-vertex and subdivision-edge coronae

The subdivision graph $\mathcal{S}(G)$ of a graph $G$ is the graph obtained by inserting a new vertex into every edge of $G$. Let $G_1$ and $G_2$ be two vertex disjoint graphs. The \emph{subdivision-vertex corona} of $G_1$ and $G_2$, denoted by $G_1\odot G_2$, is the graph obtained from $\mathcal{S}(G_1)$ and $|V(G_1)|$ copies of $G_2$, all vertex-disjoint, by joining the $i$th vertex of $V(G_1)$ to every vertex in the $i$th copy of $G_2$. The \emph{subdivision-edge corona} of $G_1$ and $G_2$, denoted by $G_1\circleddash G_2$, is the graph obtained from $\mathcal{S}(G_1)$ and $|I(G_1)|$ copies of $G_2$, all vertex-disjoint, by joining the $i$th vertex of $I(G_1)$ to every vertex in the $i$th copy of $G_2$, where $I(G_1)$ is the set of inserted vertices of $\mathcal{S}(G_1)$. In this paper we determine the adjacency spectra, the Laplacian spectra and the signless Laplacian spectra of $G_1\odot G_2$ (respectively, $G_1\circleddash G_2$) in terms of the corresponding spectra of $G_1$ and $G_2$. As applications, the results on the spectra of $G_1\odot G_2$ (respectively, $G_1\circleddash G_2$) enable us to construct infinitely many pairs of cospectral graphs. The adjacency spectra of $G_1\odot G_2$ (respectively, $G_1\circleddash G_2$) help us to construct many infinite families of integral graphs. By using the Laplacian spectra, we also obtain the number of spanning trees and Kirchhoff index of $G_1\odot G_2$ and $G_1\circleddash G_2$, respectively.

math.CO↗

arXiv2012

Spectra of subdivision-vertex and subdivision-edge neighbourhood coronae

Let $G=(V(G),E(G))$ be a graph with vertex set $V(G)$ and edge set $E(G)$. The subdivision graph $\mathcal{S}(G)$ of a graph $G$ is the graph obtained by inserting a new vertex into every edge of $G$. Let $G_1$ and $G_2$ be two vertex disjoint graphs. The subdivision-vertex neighbourhood corona of $G_1$ and $G_2$, denoted by $G_1 \boxdot G_2$, is the graph obtained from $\mathcal{S}(G_1)$ and $|V(G_1)|$ copies of $G_2$, all vertex disjoint, and joining the neighbours of the $i$th vertex of $V(G_1)$ to every vertex in the $i$th copy of $G_2$. The subdivision-edge neighbourhood corona of $G_1$ and $G_2$, denoted by $G_1 \boxminus G_2$, is the graph obtained from $\mathcal{S}(G_1)$ and $|I(G_1)|$ copies of $G_2$, all vertex disjoint, and joining the neighbours of the $i$th vertex of $I(G_1)$ to every vertex in the $i$th copy of $G_2$, where $I(G_1)$ is the set of inserted vertices of $\mathcal{S}(G_1)$. In this paper we determine the adjacency spectra, the Laplacian spectra and the signless Laplacian spectra of $G_1\boxdot G_2$ (respectively, $G_1\boxminus G_2$) in terms of the corresponding spectra of $G_1$ and $G_2$. As applications, these results enable us to construct infinitely many pairs of cospectral graphs, and using the results on the Laplacian spectra of subdivision-vertex neighbourhood coronae, new families of expander graphs are constructed from known ones.

math.CO↗