Research Article  Open Access
HighOrder Degree and Combined Degree in Complex Networks
Abstract
We define several novel centrality metrics: the highorder degree and combined degree of undirected network, the highorder outdegree and indegree and combined out outdegree and indegree of directed network. Those are the measurement of node importance with respect to the number of the node neighbors. We also explore those centrality metrics in the context of several bestknown networks. We prove that both the degree centrality and eigenvector centrality are the special cases of the highorder degree of undirected network, and both the indegree and PageRank algorithm without damping factor are the special cases of the highorder indegree of directed network. Finally, we also discuss the significance of highorder outdegree of directed network. Our centrality metrics work better in distinguishing nodes than degree and reduce the computation load compared with either eigenvector centrality or PageRank algorithm.
1. Introduction
The theory of network has gone through rapid development since the late 1990s. One of the hottest points is the research on the attributes of network. Node degree has always been considered as one of the most important and fundamental attributes. Many relative researches have defined other attributes of network based on node degree [1, 2], such as degree distribution [3, 4], clustering coefficient [5, 6], the characteristic path length [6], and so on. As early as 1960s, Rapoport [3, 4] emphasized the importance of the degree distribution in all kinds of real networks. Wasserman and Faust [5] introduced fraction of transitive triples in social network in 1994. In order to describe cliquishness of a typical neighborhood, Watts and Strogatz [6] defined clustering coefficient of general complex network in 1998 based on the fraction of transitive triples. Watts and Strogatz [6] also defined the characteristic path length to measure the typical separation between two nodes in the network. In more recent years, numerous researches paid attention to degree distribution [7–10]. Early researches believed that degree distribution followed Poisson Distribution just as the random network theory has described [11, 12]. Recent researches have found out that the degree distribution for a large number of networks, such as the World Wide Web [13], the Internet [14], the metabolic networks [15], genomewide disruption networks for yeast [16], and the network of interregional direct investment stocks across Europe [17], have a powerlaw tail. Such networks are called scalefree [18].
When a practical problem is transformed into a complex network model, people tend to use node centrality to describe the importance and influence of a node, or people need to sort these nodes [19]. Node degree (or degree centrality) is one of the basic methods of sorting the nodes [20–29]. Other common methods are also based on node degree [1, 2]. As long ago as 1948, Bavelas [30] studied the center of social network. Sabidussi et. al. [31] defined what it means for a network node to approach closeness. Freema [32] used node degree and betweenness to define two kinds of node centrality, and, furthermore, he used node centrality to define graph centrality. Network eigenvector centrality [33, 34] is often used to describe the importance of nodes in social network. In 1998, Brin and Page [35, 36] simplified the eigenvector centrality for undirected network into PageRank algorithm (), which is widely used in searching engine Google [36] and many other directed networks [37–42]. By using the degree distribution of neighbor nodes, Ai [43] gave the definition of the neighbor vector centrality. Zeng [44] implemented the Mixed Degree Decomposition () procedure by using coreness centrality, and he defined the mixed degree of nodes. Bae [45] defined the kernel centrality of a node by using the kernel of the neighbor nodes.
The degree centrality used for sorting nodes has the advantages of simple calculation, but the results are not accurate enough. Therefore, it may require verification by other methods [20] or other network attributes [27, 28]. Closeness centrality and betweenness centrality are often given with degree centrality for comparison purpose [27, 28]. The computation of both closeness centrality and betweenness centrality are so complicated that big networks often need fast approximate algorithm [46–48]. Even though the eigenvector centrality on undirected network and the PageRank on directed work can give satisfying results in nodes sorting, those two methods often involve expensive computations, such as iterations [42]. In the following paragraphs, we will define several novel centrality metrics, which are cheaper in computation compared with closeness centrality, betweenness centrality, eigenvector centrality on undirected network, and PageRank on directed network. We will show that node degree, the eigenvector centrality on undirected network and indegree, and PageRank on directed network are all special cases of (or equivalent to) one of our novel centrality metrics (see Sections 4.1 and 4.2). It should be pointed out that the combined degree defined in our paper is different from the mixed degree in Zeng [44], which is the Mixed Degree Decomposition of coreness centrality (see Section 4.4).
2. Methods
Random walk has always been one of the most important methods in the research of complex network [49–53]. Noh et. al. [49] derived the mean first passage time () between any two nodes by using random walk of complex network. Tejedor [50] computed the of a network based on a broad class of random walk. Rosvall et. al. [51] used the probability flow of random walks on a network as a proxy for information flow in the real system. Saramäki et. al. [52] generated scalefree networks based on selecting parent nodes by using random walk. Weng [53] used the newly defined mean first traverse distance () to describe anomalous random walks. Now let us think about an ideal random walk of a simple network (could be an undirected network or a directed network): choose a node of the network, which we call the origin node, put a ball at the origin node, and the ball obeys the following rules to split repeatedly and to walk randomly.(i)Splitting: the ball splits up into (or ) balls at the node , where is the degree of in undirected network and is the outdegree of in directed network.(ii)Random walk: after every split, the balls move to the adjacent nodes along the edges; in directed network they can only move along outgoing edges.(iii)Disappearance: balls that can no longer walk would disappear, for example, at the isolated nodes or dead nodes of directed network.
The number of repeats of the random walk would be different depending on the selection of network and/or the selection of origin node, such as in Figure 1.
(a)
(b)
(c)
(d)
(e)
In the undirected network, the number of balls after split is defined as the order degree of the origin node , denoted by . Clearly, if , is the degree of (see Theorem 2, Section 4.1). In the directed network, the number of balls after split is called the sorder outdegree of the origin node , denoted by . Also, when , is the outdegree of (see Theorem 3, Section 4.2).
Consider a more complicated random walk of a directed network: select a network node , which we call the sink node. Then we put balls at every node in the network (including the sink node ), and all of the balls follow the above rules to split repeatedly and to walk randomly, the number of balls at the sink node after the random walk is defined as the order indegree of the sink node , denoted by . Clearly, if , is the indegree of (see Theorem 3, Section 4.2).
2.1. 2Order Degree
As for the undirected network, according to the above definition, 2order degree of the node is the number of twoedge paths connected to . The twoedge paths may overlap. For example, Figure 1(e), both nodes have an overlap twoedge path, then the 2order degree of both nodes is 1. Note that the 2order degree is not necessarily equal to the number of neighbors of a node’s neighbors, since there may be more than one twoedge path between any two nodes (here the two nodes may be the same one). The computation of node 2order degree is relatively simple. Suppose matrix is the adjacency matrix of the undirected network , we call the 2order adjacency matrix, and then the sum of elements in each row (or each column) of is the 2order degree of the corresponding node. Use to denote the sum of the th row of matrix , then the 2order degree of the th node of the undirected network is .
A directed graph has both a 2order indegree and a 2order outdegree for each node, which are the numbers of incoming and outgoing twoedge paths, respectively. Still denote the adjacency matrix as , and denote and as the sum of the th row and th column of the 2order adjacency matrix , respectively. Then the 2order outdegree and the 2order indegree of the th node in this directed network are and , respectively.
If has vertices , the sequence is called thedegree sequence of [54]. As we all know, the mean value and the distribution of degree sequence guide the definition of mean degree and degree distribution. We can obtain the mean 2order degree and the 2order degree distribution of the network by using the definition of 2order degree.
In Figure 1, (a) the 2order degree of the three nodes all is 2; (b) the 2order outdegree and 2order indegree of the three nodes all are 1; (c) the 2order outdegree and 2order indegree of the three nodes all are 0; (d) the 2order outdegree of the three nodes is 1, 0, 0, and the 2order indegree of the three nodes is 0, 0, 1; (e) the 2order degree of the two nodes is 1.
Figure 2 is a relatively complicated undirected network and a relatively complicated directed network. Table 1 shows the 2order (in/out)degree of the all nodes and both mean 2order degrees for the two networks in Figure 2. As we can see from Table 1, node 5 has the highest 2order degree in Figure 2(a), which is the same as the common sense that node 5 is the most important node in Figure 2(a). However this conclusion cannot be obtained simply by calculating node degree. Of course we can obtain such conclusion by calculating node betweenness centrality or eigenvector centrality, and so forth. Another thing we can tell from Table 1 is that nodes having the highest 2order outdegree are 1, 2, and 5, and the node having the highest 2order indegree is 10. Therefore, we could consider that nodes 1, 2, and 5 are the source nodes of (b), and the node 10 could be thought as the collection node of (b). In general, we need to run the PageRank algorithm of the entire network in order to get such conclusions.

(a)
(b)
2.2. Order Degree and Its Computation
Same as 2order degree, suppose matrix is an adjacency matrix of a network , we denote as the order adjacency matrix of . The matrix was used to calculate the number of walk with length between nodes [54–56]. Denote and as the sum of the th row and th column respectively in matrix ; then the order degree of node in undirected network is and (if is a directed network, then the order outdegree is , and the order indegree is ). It is easy to define mean order (out/in)degree and the order (out/in)degree distribution, too.
The computation of order degree can make use of adjacency matrix, but this involves the exponentiation computation of matrix. The order of matrix equals the number of nodes in the network, and there could be thousands and tens of thousands of nodes in a network. Therefore even though the computation of order adjacency matrix only involves the exponentiation computation of matrix and additions of integers, it would be a high requirement on the computer’s CPU and memory. A simple method to tackle this problem is to firstly compute the lower order degree sequence of the network and then compute the higherorder degree sequence. We can prove Theorem 1 by the method of mathematical induction.
Theorem 1. For order degree sequence, the following conclusions are true: (i)If is a order degree sequence of an undirected network , is the adjacency matrix, then the order degree sequence of is ; particularly, if is the degree sequence of an undirected network , then the order degree sequence of is .(ii)Similarly, if and are the order outdegree sequence and order indegree sequence of directed network respectively, is the adjacency matrix, then the order outdegree sequence and the order indegree sequence are and respectively; particularly, if and are the outdegree sequence and indegree sequence of the directed network respectively, then the order outdegree sequence and the order indegree sequence are and , respectively.
2.3. Combined Degree
Based on the order degree defined as above, the following gives the definition of combined degree of a node of an undirected network:where denotes the various order degrees of node , and constants are all nonnegative real numbers, with a sum of 1. If and , the combined degree is the node degree in common sense, if and , the combined degree is the 2order degree, and so forth. For the values of parameters, we usually consider the case , where indicates that a single neighbor’s influence to is no less than a single neighbor’s influence. Notice that the value of constant may not be integers; therefore the combined degree usually not be integer. Since the combined degree is the combination of regular degree and various highorder degree, we need not discuss the mean combined degree and combined degree distribution. Same as above, we can define combined indegree and combined outdegree of a directed network. According to Theorem 1, we could give the following formula of combined degree sequence. Here we omit the formulas of combined indegree sequence and combined outdegree sequence of directed networks. where is the identity matrix of order .
3. Results
In order to compare the behavior of degree and highorder degree and/or combined degree, we apply these attributes to a couple of bestknown networks. We believe that the attributes of network with better behavior should be better to discover the differences among different nodes. By the definition of the differences of a given set of data, standard deviation () is the most important metric parameter. The greater the standard deviation is, the more diversities the data has, and the better the discrimination is, the better the attributes behavior is. As a complement to , we define a novel parameter and call it as overflow ratio (), which denotes the ratio of the number of elements outside to the number of elements in the given set of data, where denotes the mean value of the given set of data and . The greater the overflow ratio is, the less the possibility that data cluster around the mean value of the given set is, and the better the discrimination is, the better the attributes behavior is. Therefore, these two parameters both reflect the diversity of a given set of data to some extent. Clearly, if one of the network attributes shows more diversity, it is easier to distinguish or sort the nodes.
3.1. Random Network
Random network was put forward for the first time by Paul Erds and Alfred Rényi in 1960 (which is called Random Network) [11]. Here we generate a random network with nodes, and we investigate the influence of node connection probability taking different values on degree and highorder degree. Figure 3 shows the simulation results of 1000 times for node degree distribution, 2order degree distribution, 4order degree distribution, and combined degree distribution with and . We can tell from the results that the two highorder degrees and combined degree of random network still preserve the property of degree distribution, which is similar to Poisson Distribution (or Normal Distribution) with mean degree/mean highorder degree as the peak value.
Table 2 gives the discrimination of some attributes with and . Since the node degree distribution and highorder degree/combined degree distribution are all similar to Poisson Distribution (or Normal Distribution), we take big values for the overflow ratios. We can also tell from Table 2 that highorder degree has better discrimination compared with regular node degree, which is also true when and change (see Supplementary Table 1).

3.2. Small World Model
Watts [57] gave the basic attributes of Small World Model in 1999. Watts and Stregatz [6] proposed the construction of small world network by edge redistribution, which is called the small world network. Monasson [58] and Newman et al. [59] initiated the construction of small world network through edge augment, which is called the small world network. In this subsection we construct a small world network by edge augment, and investigate the behaviors of highorder degrees in small world network. Firstly, we construct a regular network with nodes, and with each node connected to the nearest nodes (where ). For a given probability , we stochastic add edges to produce the small world network. We can tell from Figure 4 that the scatter plot of 2order degree distribution, 3order degree distribution and 4order degree distribution are all similar to Poisson Distribution (or Normal Distribution), which is the same as the degree distribution of the small world network.
For small world network, similar to random network, we are more concerned with the discrimination of small world network. Table 3 shows the mean discrimination of 1000 simulations for degree, 2order degree, 4order degree and 8order degree, with , initial edge connection number , and connection probability . Of all the situations, 4order degree and 8order degree show better discrimination results than the others. But sometimes the 2order degree does not show good results in discrimination than degree, which is probably caused by the smallness of the value of parameter . In Supplementary Table 2, we increase the value of , and show the discrimination of degree, 2order degree, and 4order degree, with the number of nodes and initial connection edges change over 54 cases. Among all of the cases, the standard deviation discrimination gets bigger (better), 4order degree shows smaller overflow ratio twice than that of node degree, and 2order degree shows smaller overflow ratio four times than that of node degree, which suggests that 4order degree and 2order degree are better than node degree in the aspect of overflow ratio. Moreover, as the order of highorder degree increases, the overflow ratio shows better results in discriminating the nodes.

3.3. Undirected ScaleFree Network
Barabási and Albert [18, 60, 61] proposed scalefree network (which is called scalefree network) with node degree distribution following the powerlaw distribution. In this subsection, we construct an undirected, scalefree network to investigate the behavior of various orders of degree. Firstly, we produce a random network with the number of nodes , stochastic connection probability . Then we add one node each time whose degree equals by the preferential attachment mechanism. Preferential attachment means that the more connected a node is, the more likely it is to receive new links. Nodes with higher degree have stronger ability to grab links added to the network. In our paper, the probability that new node connects to node is proportional to the degree of . Repeating this process for times, we get an undirected scalefree network with nodes and about edges.
The highorder degree distribution and combined degree distribution are also powerlaw distribution; see Figure 5. Supplementary Table 3 gives the discrimination results of various orders of degree, with initial number of nodes , initial node connection probability , and running times , and the new node added to the network each time has a degree of . As a matter of fact, only if , the higherorder degree shows better discrimination results. The reason maybe that the range of node degree sequence is relatively large for scalefree network (compared with random network and small world network), which coincides with our knowledge [18, 60, 61].
3.4. Directed ScaleFree Network
In this subsection, we construct a directed scalefree network. Firstly, produce a random network with nodes number and connection probability . Secondly assign the direction of each existing edge at random (with equal probability) to establish a directed network. Thirdly, run this step times, add a new node at each time, and add inedges by outdegree priority mechanism and outedges by indegree priority mechanism; there we usually choose . Therefore we obtain a directed network with nodes and edges. Figure 6 shows that both the indegree distribution and the outdegree distribution in the directed network we developed follow powerlaw distribution; therefore it is a directed scalefree network [56, 57]. But there are variations at the beginning of the graphs for highorder indegree and highorder outdegree (especially the outdegree), even though the general shapes of these graphs still resemble the powerrule distributions; see Figure 6. We suspect that this may be caused by the fact that we always add inedges and outedges at each time when we construct the network, and the numbers of edges maintain constants rather than random numbers. Since the discrimination comparison between highorder degree and node degree in directed scalefree network is similar to that in undireceted scalefree network, we will not demonstrate the results here.
4. Discussion
The highorder degree in a network considers the influence of different path distances on a node. In a social network, consider a node is a person, and an edge is a friendship. Node degree of node indicates the influence of ’s friends on . The 2order degree of indicates the influence of ’s friends’ friends on . An interesting question is that, if and are friends, there is an undirected edge connecting and , then will influence , which will in return have influence on itself also. If has many friends, will have influence on all of its friends, which in return will affect many times.
We know from Section 3 that highorder degrees and combined degree are superior to degree in discriminating nodes. However the computation of degree is simpler than highorder degrees and combined degree. Compared with the eigenvector centrality in undirected network, and PageRank in directed network, the highorder (out/in)degree and/or combined (out/in)degree we defined in this paper do not need iterations. The computation of our method only involves multiplications of matrices and vectors, which is clearly easier than eigenvector centrality or PageRank.
4.1. The HighOrder Degree and Eigenvector Centrality/Betweenness in Undirected Network
In the undirected network, node degree is used to measure the influence of all the nodes connected to a node. 2order degree considers the influence of a node’s neighbor’s neighbor on the node. 3order degree considers the influence of a node’s neighbors’ neighbors’ neighbor on that node. As a result, the lower the order of the highorder degree is, the closer it is to the node degree. On the other hand, the higher the order of the highorder degree is, the closer it is to eigenvector centrality.
Theorem 2. For order degree in undirected network, if , it is the node degree of the network. As approaches to infinity, order degree is equivalent to eigenvector centrality.
We only need to show that when approaches to infinity, order degree is equivalent to eigenvector centrality. Suppose is the adjacency matrix in an undirected network and is the degree sequence. Then by Theorem 1, the order degree sequence is , which is consistent with calculating the largest eigenvalue and the corresponding eigenvector by the method of power rule [62]. Therefore, the normalized result of is the node eigenvector centrality. If the eigenvector centrality is deemed as the most accurate method in ranking nodes, then from node degree to highorder degree, and to eigenvector centrality, the accuracies get higher and higher, and the computation complexity gets higher and higher at the same time. Therefore, if there is not high requirement on ranking accuracy and computation complexity, highorder degree is a relatively good choice.
In fact, highorder degrees are not simple alternative to eigenvector centrality. Sometimes highorder degrees may be more intuitive than some methods including eigenvector centrality. For example, Figure 2(a), the top three nodes sorted by both 2order degree and 4order degree are 5, 6, 4, which we can see from Tables 1 and 4; it is the same as our supposition. Otherwise the top three nodes sorted by both betweenness and eigenvector centrality are 6, 5, 4.
 
PageRank without damping factor. PageRank with damping factor equal to 0.85. 
4.2. The HighOrder Degree and PageRank for Directed Network
Theorem 3. For order outdegree in a directed network, if , it is the outdegree of the directed network. For order indegree in a directed network, if , it is the indegree of the directed network. When approaches to infinity, the order indegree is equivalent to PageRank (without damping factor).
Theorem 3 is clearly true because PageRank is the simplification of eigenvector centrality in directed network [63]. Moreover, we can reach similar conclusions that when ranking network nodes, highorder indegree is a good choice if one has certain but not high requirements on the accuracy and computation complexity.
Same as Section 4.2, high inorder degrees are not simple alternative to PageRank. Sometimes high inorder degrees may be more intuitive than PageRank. For example Figure 2(b), the top node sorted by both 2order indegree and 3order indegree is 9, which we can see from Tables 1 and 4, it is the same with our supposition. Otherwise the top node sorted by both PageRank without damping factor and PageRank with damping factor is 9.
4.3. The Significance of Network HighOrder OutDegree
PageRank (highorder indegree) indicates that the value of a node is larger when there are more nodes pointing to in a directed network (quantity hypothesis) and/or the nodes pointing have larger values (quality hypothesis) [35, 36]. Reversely, we call highorder outdegree Reverse PageRank (). The value of a node in a directed network is determined by how many nodes pointed by (reverse quantity hypothesis) and/or how large value that nodes pointed by have (reverse quality hypothesis). For example, we can say that the World Wide Web navigation websites have large value. When someone surfs the Internet, if he does not know which website is worth visiting (with larger value), he should start with the navigation website (with larger value). There is more research on the highorder indegree () for directed network, but there is no research on highorder outdegree () as far as we know.
Figure 2(b), although nodes 1, 2, 5 have the same 2order outdegree, only node 1 has the largest order outdegree (). Node 1 is the only one that can reach any nodes of this graph.
4.4. The Combined Degree We Defined and the Mixed Degree Zeng Defined
Zeng [44] used the weighted sum of both the residual degree and the exhausted degree to define the mixed degree of the node of a network. On one hand, Zeng’s definition involves Mixed Degree Decomposition () to the whole network by using coreness centrality, while the combined degree we defined is based on a linear combination of various highorder degrees. Even though our method involves more terms in the weighted sum, it does not need to consider network decomposition. Therefore it is cheaper in computation than Zeng’s definition. On the other hand, Zeng divides the nodes that are connected to a node into two classes according to , while we divide the nodes that are connected to a node into finite number of classes according to path distance, which is more delicate in classification and has more accurate results.
5. Conclusion
In this paper, we define several novel centrality metrics: the highorder (out/in)degree and combined degree. For the values of combined degree’s parameters, we usually consider the case and . We prove that both the degree centrality and eigenvector centrality are the special cases of the highorder degree of undirected network, and both the indegree and PageRank algorithm without damping factor are the special cases of the highorder indegree of directed network. We present several experiments to discuss the performance of our novel centrality metrics. It can be seen from the experiments that the centrality metrics we defined are easy to calculate and perform better than degree centrality. In a largescale complex network study, our centrality metrics will be an effective alternative to the eigenvector centrality/PageRank algorithm. The manuscript is only limited in introducing the definition of new metrics. We hope to discuss their efficacy and computational cost in the further works.
Data Availability
The data used to support the findings of this study are available from the corresponding author upon request.
Conflicts of Interest
The authors declare no conflicts of interest.
Authors’ Contributions
All authors worked together to produce the results and read and approved the final manuscript.
Acknowledgments
The research is supported by the National Natural Science Foundation of China (61572522 and 11371230) and Shandong Provincial Natural Science Foundation (ZR2018PF004).
Supplementary Materials
Supplementary 1 . Supplementary Table 1: in the random network, we increase the value of and show the discrimination of degree, 2order degree, and 4order degree, with the number of nodes and initial connection edges changing over 27 cases.
Supplementary 2 . Supplementary Table 2: in the small world network, we increase the value of and show the discrimination of degree, 2order degree, and 4order degree, with the number of nodes and initial connection edges changing over 54 cases.
Supplementary 3 . Supplementary Table 3: in the undirected scalefree network, the discrimination results of various orders of degree, with initial number of nodes , initial node connection probability , running times , and the new node added to the network each time have a degree of over 90 cases.
References
 M. E. Newman, “The structure and function of complex networks,” SIAM Review, vol. 45, no. 2, pp. 167–256, 2003. View at: Publisher Site  Google Scholar  MathSciNet
 R. Albert and A.L. Barabási, “Statistical mechanics of complex networks,” Reviews of Modern Physics, vol. 74, no. 1, pp. 47–97, 2002. View at: Publisher Site  Google Scholar  MathSciNet
 A. Rapoport, “Contribution to the theory of random and biased nets,” Bulletin of Mathematical Biology, vol. 19, no. 4, pp. 257–277, 1957. View at: Publisher Site  Google Scholar
 A. Rapoport and W. J. Horvath, “A study of a large sociogram,” Behavioural Science, vol. 6, pp. 279–291, 1961. View at: Google Scholar
 S. Wasserman and K. Faust, Social Network Analysis: Methods and Applications, Cambridge University Press, Cambridge, UK, 1994.
 D. J. Watts and S. H. Strogatz, “Collective dynamics of smallworld’ networks,” Nature, vol. 393, pp. 440–442, 1998. View at: Google Scholar
 S. N. Dorogovtsev, J. F. F. Mendes, and A. N. Samukhin, “Sizedependent degree distribution of a scalefree growing network,” Physical Review E: Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, vol. 63, no. 6, 2001. View at: Google Scholar
 J. Lee, C. Heaukulani, Z. Ghahramani, L. F. James, and S. Choi, “Bayesian inference on random simple graphs with power law degree distributions,” in Proceedings of the International Conference on Machine Learning, pp. 2004–2013, 2017. View at: Google Scholar
 F. MohdZaid, C. M. S. Kabban, R. F. Deckro, and E. D. White, “Parameter specification for the degree distribution of simulated BarabásiAlbert graphs,” Physica A: Statistical Mechanics and Its Applications, vol. 465, pp. 141–152, 2017. View at: Publisher Site  Google Scholar  MathSciNet
 X. Wang, S. Trajanovski, R. E. Kooij, and P. Van Mieghem, “Degree distribution and assortativity in line graphs of complex networks,” Physica A: Statistical Mechanics and Its Applications, vol. 445, pp. 343–356, 2016. View at: Publisher Site  Google Scholar  MathSciNet
 P. Erdös and A. Rényi, “On the evolution of random graphs,” Publications of the Mathematical Institute of the Hungarian Academy of Sciences, vol. 38, pp. 17–61, 2012. View at: Google Scholar
 P. Erdös, “On extremal problems of graphs and generalized graphs,” Israel Journal of Mathematics, vol. 2, pp. 183–190, 1964. View at: Publisher Site  Google Scholar  MathSciNet
 R. Albert, H. Jeong, and A.L. Barabási, “Internet: diameter of the WorldWide Web,” Nature, vol. 401, no. 6749, pp. 130131, 1999. View at: Publisher Site  Google Scholar
 V. M. Eguíluz and K. Klemm, “Epidemic threshold in structured scalefree networks,” Physical Review Letters, vol. 89, no. 10, Article ID 108701, 2002. View at: Publisher Site  Google Scholar
 H. Jeong, B. Tombor, R. Albert, Z. N. Oltvai, and A. L. Barabási, “The largescale organization of metabolic networks,” Nature, vol. 407, pp. 651–654, 2002. View at: Google Scholar
 J. Rung, T. Schlitt, A. Brazma, K. Freivalds, and J. Vilo, “Building and analysing genomewide gene disruption networks,” Bioinformatics, vol. 18, no. 2, pp. S202–S210, 2002. View at: Publisher Site  Google Scholar
 S. Battiston, J. F. Rodrigues, and H. Zeytinoglu, “The network of interregional direct investment stocks across Europe,” Advances in Complex Systems (ACS), vol. 10, no. 1, pp. 29–51, 2007. View at: Publisher Site  Google Scholar
 A.L. Barabási and R. Albert, “Emergence of scaling in random networks,” American Association for the Advancement of Science: Science, vol. 286, no. 5439, pp. 509–512, 1999. View at: Publisher Site  Google Scholar  MathSciNet
 L. D. F. Costa, O. N. Oliveira, G. Travieso et al., “Analyzing and modeling realworld phenomena with complex networks: a survey of applications,” Advances in Physics, vol. 60, no. 3, pp. 329–412, 2011. View at: Publisher Site  Google Scholar
 L. Li, Y. Wei, C. To et al., “Integrated Omic analysis of lung cancer reveals metabolism proteome signatures with prognostic impact,” Nature Communications, vol. 5, article no. 5469, 2014. View at: Publisher Site  Google Scholar
 Y. Zhang, M. Zhao, J. Su, X. Lu, and K. Lv, “Novel model for cascading failure based on degree strength and its application in directed gene logic networks,” Computational & Mathematical Methods in Medicine, vol. 2018, Article ID 8950794, 2018. View at: Google Scholar
 S. Wang, Y. Chen, Q. Wang, E. Li, Y. Su, and D. Meng, “Analysis for gene networks based on logic relationships,” Journal of Systems Science & Complexity, vol. 23, no. 5, pp. 999–1011, 2010. View at: Publisher Site  Google Scholar  MathSciNet
 H. Jeong, S. P. Mason, A. L. Barabási, and Z. N. Oltvai, “Lethality and centrality in protein networks,” Nature, vol. 411, no. 6833, pp. 4142, 2001. View at: Publisher Site  Google Scholar
 Y. Zhang, K. Lv, S. Wang, J. Su, and D. Meng, “Modeling gene networks in saccharomyces cerevisiae based on gene expression profiles,” Computational and Mathematical Methods in Medicine, vol. 2015, Article ID 621264, 10 pages, 2015. View at: Publisher Site  Google Scholar
 M. C. González, C. A. Hidalgo, and A.L. Barabási, “Understanding individual human mobility patterns,” Nature, vol. 453, no. 7196, pp. 779–782, 2008. View at: Publisher Site  Google Scholar
 W. Wang and T. Zhang, “Caspase1mediated pyroptosis of the predominance for driving CD_{4}^{+} T cells death: a nonlocal spatial mathematical model,” Bulletin of Mathematical Biology, vol. 80, no. 3, pp. 540–582, 2018. View at: Publisher Site  Google Scholar  MathSciNet
 M. Rubinov and E. Bullmore, “Schizophrenia and abnormal brain network hubs,” Dialogues in Clinical Neuroscience, vol. 15, no. 3, pp. 339–349, 2013. View at: Google Scholar
 J. Bohannon, “Counterterrorism's new tool: 'metanetwork' analysis,” Science, vol. 325, no. 5939, pp. 409–411, 2009. View at: Publisher Site  Google Scholar
 T. Zhang, X. Meng, and T. Zhang, “Global dynamics of a virus dynamical model with celltocell transmission and cure rate,” Computational and Mathematical Methods in Medicine, Article ID 758362, 8 pages, 2015. View at: Publisher Site  Google Scholar  MathSciNet
 D. W. Franks, J. Noble, P. Kaufmann, and S. Stagl, “Extremism propagation in social networks with hubs,” Adaptive Behavior, vol. 16, no. 4, pp. 264–274, 2008. View at: Publisher Site  Google Scholar
 A. Bavelas, “A mathematical model for group structures,” Human Organization, vol. 7, no. 3, pp. 16–30, 1948. View at: Publisher Site  Google Scholar
 G. Sabidussi, “The centrality index of a graph,” Psychometrika, vol. 31, pp. 581–603, 1966. View at: Publisher Site  Google Scholar  MathSciNet
 L. C. Freeman, “Centrality in social networks conceptual clarification,” Social Networks, vol. 1, no. 3, pp. 215–239, 1978. View at: Publisher Site  Google Scholar
 P. Bonacich, “Technique for analyzing overlapping memberships,” Sociological Methodology, vol. 4, pp. 176–185, 1972. View at: Publisher Site  Google Scholar
 E. Costenbader and T. W. Valente, “The stability of centrality measures when networks are sampled,” Social Networks, vol. 25, no. 4, pp. 283–307, 2003. View at: Publisher Site  Google Scholar
 L. Page, The PageRank citation ranking: bringing order to the web, Stanford Digital Libraries Working Paper, vol. 9, 1998.
 S. Brin and L. Page, “The anatomy of a largescale hypertextual web search engine,” Computer Networks, vol. 56, no. 18, pp. 3825–3833, 2012. View at: Publisher Site  Google Scholar
 M. A. Kłopotek, S. T. Wierzchoń, R. A. Kłopotek, and E. A. Kłopotek, “Traditional PageRank versus network capacity bound,” in Social and Information Networks, 2017. View at: Publisher Site  Google Scholar
 T. H. Haveliwala, “Topicsensitive PageRank,” in Proceedings of the 11th International Conference on World Wide Web, WWW '02, pp. 517–526, May 2002. View at: Google Scholar
 A. N. Langville and C. D. Meyer, “Deeper inside PageRank,” Internet Mathematics, vol. 1, no. 3, pp. 335–380, 2004. View at: Publisher Site  Google Scholar  MathSciNet
 M. Bressan, E. Peserico, and L. Pretto, “The power of local information in pagerank,” in Proceedings of the 22nd International Conference on World Wide Web, WWW 2013, pp. 179180, May 2013. View at: Google Scholar
 I. M. Kloumann, J. Ugander, and J. Kleinberg, “Block models and personalized PageRank,” Proceedings of the National Acadamy of Sciences of the United States of America, vol. 114, no. 1, pp. 33–38, 2017. View at: Publisher Site  Google Scholar
 A. Altman and M. Tennenholtz, “Ranking systems: the PageRank axioms,” in Proceedings of the EC'05: 6th ACM Conference on Electronic Commerce, pp. 1–8, June 2005. View at: Google Scholar
 J. Ai, H. Zhao, K. M. Carley, Z. Su, and H. Li, “Neighbor vector centrality of complex networks based on neighbors degree distribution,” The European Physical Journal B, vol. 86, no. 4, 2013. View at: Google Scholar
 A. Zeng and C.J. Zhang, “Ranking spreaders by decomposing complex networks,” Physics Letters A, vol. 377, no. 14, pp. 1031–1035, 2013. View at: Publisher Site  Google Scholar
 J. Bae and S. Kim, “Identifying and ranking influential spreaders in complex networks by neighborhood coreness,” Physica A: Statistical Mechanics and Its Applications, vol. 395, pp. 549–559, 2014. View at: Publisher Site  Google Scholar  MathSciNet
 U. Brandes, “A faster algorithm for betweenness centrality,” Journal of Mathematical Sociology, vol. 25, no. 2, pp. 163–177, 2001. View at: Publisher Site  Google Scholar
 K. Goel, R. R. Singh, S. Iyengar, and S. Gupta, “A faster algorithm to update betweenness centrality after node alteration,” Internet Mathematics, vol. 11, no. 45, pp. 403–420, 2015. View at: Publisher Site  Google Scholar  MathSciNet
 R. Guns, Y. X. Liu, and D. Mahbuba, “Qmeasures and betweenness centrality in a collaboration network: a case study of the field of informetrics,” Scientometrics, vol. 87, no. 1, pp. 133–147, 2011. View at: Publisher Site  Google Scholar
 J. D. Noh and H. Rieger, “Random walks on complex networks,” Physical Review Letters, vol. 92, no. 11, Article ID 118701, 2004. View at: Google Scholar
 V. Tejedor, O. Bénichou, and R. Voituriez, “Global mean firstpassage times of random walks on complex networks,” Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, vol. 80, no. 6, 2009. View at: Publisher Site  Google Scholar
 M. Rosvall and C. T. Bergstrom, “Maps of random walks on complex networks reveal community structure,” Proceedings of the National Acadamy of Sciences of the United States of America, vol. 105, no. 4, pp. 1118–1123, 2008. View at: Publisher Site  Google Scholar
 J. Saramäki and K. Kaski, “Scalefree networks generated by random walkers,” Physica A: Statistical Mechanics and Its Applications, vol. 341, no. 14, pp. 80–86, 2004. View at: Publisher Site  Google Scholar  MathSciNet
 T. Weng, J. Zhang, M. Khajehnejad, M. Small, R. Zheng, and P. Hui, “Navigation by anomalous random walks on complex networks,” Scientific Reports, vol. 6, 2016. View at: Google Scholar
 E. K. Lloyd, J. A. Bondy, and U. S. Murty, “Graph theory with applications,” The Mathematical Gazette, vol. 28, pp. 237238, 1976. View at: Publisher Site  Google Scholar
 E. Terzi and M. Winkler, “A spectral algorithm for computing social balance,” in Algorithms and Models for the Web Graph, vol. 6732 of Lecture Notes in Comput. Sci., pp. 1–13, Springer, Heidelberg, 2011. View at: Publisher Site  Google Scholar  MathSciNet
 J. Tang, Y. Chang, C. Aggarwal, and H. Liu, “A survey of signed network mining in social media,” in ACM Computing Surveys (CSUR), vol. 49, pp. 237238, 2015. View at: Google Scholar
 D. J. Watts, Small Worlds: The Dynamics of Networks between Order and Randomness, Princeton University Press, Princeton, NJ, USA, 1999.
 R. Monasson, “Diffusion, localization and dispersion relations on smallworld lattices,” The European Physical Journal B, vol. 12, pp. 555–567, 1999. View at: Google Scholar
 M. E. Newman and D. J. Watts, “Renormalization group analysis of the smallworld network model,” Physics Letters A, vol. 263, no. 46, pp. 341–346, 1999. View at: Publisher Site  Google Scholar  MathSciNet
 A. L. Barabási, R. Albert, and H. Jeong, “Meanfield theory for scalefree random networks,” Physica A Statistical Mechanics & Its Applications, vol. 272, pp. 173–178, 1999. View at: Google Scholar  MathSciNet
 A. Barabási, E. Ravasz, and T. Vicsek, “Deterministic scalefree networks,” Physica A: Statistical Mechanics and Its Applications, vol. 299, no. 34, pp. 559–564, 2001. View at: Publisher Site  Google Scholar
 M. E. J. Newman, “The mathematics of networks,” in The New Palgrave Encyclopedia of Economics, vol. 208, pp. 1–12, 2008. View at: Google Scholar
Copyright
Copyright © 2018 Shudong Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.