arXiv subjects

Yanqing Hu

Publications and source records attributed to Yanqing Hu.

51 records · Page 3Linked to original sources

arXiv2010

Dynamics on Spatial Networks and the Effect of Distance Coarse Graining

Very recently, a kind of spatial network constructed with power-law distance distribution and total energy constriction is proposed. Moreover, it has been pointed out that such spatial networks have the optimal exponents $δ$ in the power-law distance distribution for the average shortest path, traffic dynamics and navigation. Because the distance is estimated approximately in real world, we present an distance coarse graining procedure to generate the binary spatial networks in this paper. We find that the distance coarse graining procedure will result in the shifting of the optimal exponents $δ$. Interestingly, when the network is large enough, the effect of distance coarse graining can be ignored eventually. Additionally, we also study some main dynamic processes including traffic dynamics, navigation, synchronization and percolation on this spatial networks with coarse grained distance. The results lead us to the enhancement of spatial networks' specifical functions.

physics.soc-ph↗

arXiv2010

How to Measure Significance of Community Structure in Complex Networks

Community structure analysis is a powerful tool for complex networks, which can simplify their functional analysis considerably. Recently, many approaches were proposed to community structure detection, but few works were focused on the significance of community structure. Since real networks obtained from complex systems always contain error links, and most of the community detection algorithms have random factors, evaluate the significance of community structure is important and urgent. In this paper, we use the eigenvectors' stability to characterize the significance of community structures. By employing the eigenvalues of Laplacian matrix of a given network, we can evaluate the significance of its community structure and obtain the optimal number of communities, which are always hard for community detection algorithms. We apply our method to many real networks. We find that significant community structures exist in many social networks and C.elegans neural network, and that less significant community structures appear in protein-interaction networks and metabolic networks. Our method can be applied to broad clustering problems in data mining due to its solid mathematical basis and efficiency.

physics.soc-ph↗

arXiv2010

Maximizing Entropy Yields Spatial Scaling in Social Networks

In addition to the well known common properties such as small world and community structures, recent empirical investigations suggest a universal scaling law for the spatial structure of social networks. It is found that the probability density distribution of an individual to have a friend at distance $r$ scales as $P(r)\propto r^{-1}$. The basic principle that yields this spatial scaling property is not yet understood. Here we propose a fundamental origin for this law based on the concept of entropy. We show that this spatial scaling law can result from maximization of information entropy, which means individuals seek to maximize the diversity of their friendships. Such spatial distribution can benefit individuals significantly in optimally collecting information in a social network.

physics.soc-ph↗

arXiv2010

Effects of Levy Flights Mobility Pattern on Epidemic Spreading under Limited Energy Constraint

Recently, many empirical studies uncovered that animal foraging, migration and human traveling obey Levy flights with an exponent around -2. Inspired by the deluge of H1N1 this year, in this paper, the effects of Levy flights' mobility pattern on epidemic spreading is studied from a network perspective. We construct a spatial weighted network which possesses Levy flight spatial property under a restriction of total energy. The energy restriction is represented by the limitation of total travel distance within a certain time period of an individual. We find that the exponent -2 is the epidemic threshold of SIS spreading dynamics. Moreover, at the threshold the speed of epidemics spreading is highest. The results are helpful for the understanding of the effect of mobility pattern on epidemic spreading.

physics.soc-ph↗

arXiv2009

Unevenness of Loop Location in Complex Networks

The loop structure plays an important role in many aspects of complex networks and attracts much attention. Among the previous works, Bianconi et al find that real networks often have fewer short loops as compared to random models. In this paper, we focus on the uneven location of loops which makes some parts of the network rich while some other parts sparse in loops. We propose a node removing process to analyze the unevenness and find rich loop cores can exist in many real networks such as neural networks and food web networks. Finally, an index is presented to quantify the unevenness of loop location in complex networks.

physics.soc-ph↗

arXiv2009

Analyzing Netizen's View and Reply Behaviors on the Forum

Quantitative understanding of human behaviors provides elementary comprehension of the complexity of many human-initiated systems. In this paper, we investigate the behavior of people on the $BBS$ forum by the statistical analysis of the amounts of view and reply of posts. According to our statistics, we find that the amounts of view and reply of posts follow the power law distributions with different power exponent. Furthermore, we discover that the amounts of view and reply of posts have nonlinear relationship. They are related by power function and show us straight line in log-log plot. Based on the estimation of slope and intercept of the line, we can characterize the behaviors quantitatively and know that people of Chinese forum and those of foreign forum have different preference towards replying to and viewing the posts. At last, we analyze the burstiness and memory in replying time series. They show some universal properties among different forum. All of them locate themselves in the high-$B$, low-$M$ region.

physics.soc-ph↗

arXiv2009

Scaling properties in spatial networks and its effects on topology and traffic dynamics

Empirical studies on the spatial structures in several real transport networks reveal that the distance distribution in these networks obeys power law. To discuss the influence of the power-law exponent on the network's structure and function, a spatial network model is proposed. Based on a regular network and subject to a limited cost $C$, long range connections are added with power law distance distribution $P(r)=ar^{-δ}$. Some basic topological properties of the network with different $δ$ are studied. It is found that the network has the smallest average shortest path when $δ=2$. Then a traffic model on this network is investigated. It is found that the network with $δ=1.5$ is best for the traffic process. All of these results give us some deep understandings about the relationship between spatial structure and network function.

physics.soc-ph↗

arXiv2009

Optimal Tree for Both Synchronizability and Converging Time

It has been proved that the spanning tree from a given network has the optimal synchronizability, which means the index $R=λ_{N}/λ_{2}$ reaches the minimum 1. Although the optimal synchronizability is corresponding to the minimal critical overall coupling strength to reach synchronization, it does not guarantee a shorter converging time from disorder initial configuration to synchronized state. In this letter, we find that it is the depth of the tree that affects the converging time. In addition, we present a simple and universal way to get such an effective oriented tree in a given network to reduce the converging time significantly by minimizing the depth of the tree. The shortest spanning tree has both the maximal synchronizability and efficiency.

nlin.CD↗

arXiv2009

The Scaling laws of Spatial Structure in Social Networks

Social network structure is very important for understanding human information diffusing, cooperating and competing patterns. It can bring us with some deep insights about how people affect each other. As a part of complex networks, social networks have been studied extensively. Many important universal properties with which we are quite familiar have been recovered, such as scale free degree distribution, small world, community structure, self-similarity and navigability. According to some empirical investigations, we conclude that our social network also possesses another important universal property. The spatial structure of social network is scale invariable. The distribution of geographic distance between friendship is about $Pr(d)\propto d^{-1}$ which is harmonious with navigability. More importantly, from the perspective of searching information, this kind of property can benefit individuals most.

physics.soc-ph↗

arXiv2009

Orientation in Social Networks

Stanley Milgram's small world experiment presents "six degrees of separation" of our world. One phenomenon of the experiment still puzzling us is that how individuals operating with the social network information with their characteristics can be very adept at finding the short chains. The previous works on this issue focus whether on the methods of navigation in a given network structure, or on the effects of additional information to the searching process. In this paper, we emphasize that the growth and shape of network architecture is tightly related to the individuals' attributes. We introduce a method to reconstruct nodes' intimacy degree based on local interaction. Then we provide an intimacy based approach for orientation in networks. We find that the basic reason of efficient search in social networks is that the degree of "intimacy" of each pair of nodes decays with the length of their shortest path exponentially. Meanwhile, the model can explain the hubs limitation which was observed in real-world experiment.

physics.soc-ph↗

arXiv2009

Measuring Significance of Community Structure in Complex Networks

Many complex systems can be represented as networks and separating a network into communities could simplify the functional analysis considerably. Recently, many approaches have been proposed for finding communities, but none of them can evaluate the communities found are significant or trivial definitely. In this paper, we propose an index to evaluate the significance of communities in networks. The index is based on comparing the similarity between the original community structure in network and the community structure of the network after perturbed, and is defined by integrating all the similarities. Many artificial networks and real-world networks are tested. The results show that the index is independent from the size of network and the number of communities. Moreover, we find the clear communities always exist in social networks, but don't find significative communities in proteins interaction networks and metabolic networks.

physics.soc-ph↗

arXiv2008

A New Comparative Definition of Community and Corresponding Identifying Algorithm

In this paper, a new comparative definition for community in networks is proposed and the corresponding detecting algorithm is given. A community is defined as a set of nodes, which satisfy that each node's degree inside the community should not be smaller than the node's degree toward any other community. In the algorithm, the attractive force of a community to a node is defined as the connections between them. Then employing attractive force based self-organizing process, without any extra parameter, the best communities can be detected. Several artificial and real-world networks, including Zachary Karate club network and College football network are analyzed. The algorithm works well in detecting communities and it also gives a nice description for network division and group formation.

physics.soc-ph↗

arXiv2008

Enhance the Efficiency of Heuristic Algorithm for Maximizing Modularity Q

Modularity Q is an important function for identifying community structure in complex networks. In this paper, we prove that the modularity maximization problem is equivalent to a nonconvex quadratic programming problem. This result provide us a simple way to improve the efficiency of heuristic algorithms for maximizing modularity Q. Many numerical results demonstrate that it is very effective.

physics.soc-ph↗

arXiv2007

Upper Bound of Relative Error of Random Ball Coverage for Calculating Fractal Network Dimension

Least box number coverage problem for calculating dimension of fractal networks is a NP-hard problem. Meanwhile, the time complexity of random ball coverage for calculating dimension is very low. In this paper we strictly present the upper bound of relative error for random ball coverage algorithm. We also propose twice-random ball coverage algorithm for calculating network dimension. For many real-world fractal networks, when the network diameter is sufficient large, the relative error upper bound of this method will tend to 0. In this point of view, given a proper acceptable error range, the dimension calculation is not a NP-hard problem, but P problem instead.

cond-mat.stat-mech↗

arXiv2007

Community Detecting By Signaling on Complex Networks

Based on signaling process on complex networks, a method for identification community structure is proposed. For a network with $n$ nodes, every node is assumed to be a system which can send, receive, and record signals. Each node is taken as the initial signal source once to inspire the whole network by exciting its neighbors and then the source node is endowed a $n$d vector which recording the effects of signaling process. So by this process, the topological relationship of nodes on networks could be transferred into the geometrical structure of vectors in $n$d Euclidian space. Then the best partition of groups is determined by $F$-statistic and the final community structure is given by Fuzzy $C$-means clustering method (FCM). This method can detect community structure both in unweighted and weighted networks without any extra parameters. It has been applied to ad hoc networks and some real networks including Zachary Karate Club network and football team network. The results are compared with that of other approaches and the evidence indicates that the algorithm based on signaling process is effective.

physics.soc-ph↗