Narrow Results

In this paper, we propose a growing spatial network (GSN) model and investigate its topology properties and dynamical behaviors. The model is generated by adding one node i with m links into a square lattice at each time step and the new node i is connected to the existing nodes with probabilities proportional to: , where k_j is the degree of node j, α is the tunable parameter and d_ij is the Euclidean distance between i and j. It is found that both the degree heterogeneity and the clustering coefficient monotonously increase with the increment of α, while the average shortest path length monotonously decreases. Moreover, the evolutionary game dynamics and network traffic dynamics are investigated. Simulation results show that the value of α can also greatly influence the dynamic behaviors.

The epidemic spreading on spatial-driven network is studied with the spatial susceptible-infected-susceptible (SIS) model. The network is constructed by random addition of nodes on the plan. The probability for a previous node to be connected to the new one is inversely proportional to their spatial distance to the power α. The spreading rate between two nodes is inversely proportional to their spatial distance. The effective spreading time increases with the increasing of α. The proportional coefficient is found to have a α-dependent threshold with a maximum situated in the interval 1.5 < α < 2.

Traffic dynamics has drawn much more attention recently, but most current research barely considers the space factor, which is of critical importance in many real traffic systems. In this paper, we focus our research on traffic dynamics of a spatial scale-free network with the restriction of bandwidth proportional to link Euclidean distance, and a new routing strategy is proposed with consideration of both Euclidean distance and betweenness centralities (BC) of edges. It is found that compared with the shortest distance path (SDP) strategy and the minimum betweenness centralities (MBC) of links strategy, our strategy under some parameters can effectively balance the traffic load and avoid excessive traveling distance which can improve the spatial network capacity and some system behaviors reflecting transportation efficiency, such as average packets traveling time, average packets waiting time and system throughput, traffic load and so on. Besides, though the restriction of bandwidth can trigger congestion, the proposed routing strategy always has the best performance no matter what bandwidth becomes. These results can provide insights for research on real networked traffic systems.

The bilateral trading platform is an intermediary system which serves both consumers and suppliers. To make this happen, platforms must attract users on board from both sides and make them trade with each other. Spatial distribution of suppliers has the significant impact on consumers platform adoption decision, but how does the supplier’s network affect the platform competition has rarely been studied. In this paper, the supplier’s spatial network is added to bilateral trading platform network effect model, and influences of the supplier’s network structure on the platform competition are discussed. Simulation results show that, compared with the central network, the decentralized network is more conducive to the platform competition. When suppliers on two competitive platforms follow the same network structure, the reinforcement effect of the first-mover advantage on platforms will vanish. This result is robust in different spatial network patterns. Conversely, when one of the platforms has a superior network structure of suppliers, its first-mover advantage will get strengthened, and it will achieve a better competition result; otherwise, its first-mover advantage will get weakened. These results provide an insight into the platform competition mechanism and will help platforms to make corresponding competitive strategies.

In road networks, $k$-range nearest neighbor ($k$-RNN) queries locate the $k$-closest neighbors for every point on the road segments, within a given query region defined by the user, based on the network distance. This is an important task because the user's location information may be inaccurate; furthermore, users may be unwilling to reveal their exact location for privacy reasons. Therefore, under this type of specific situation, the server returns candidate objects for every point on the road segments and the client evaluates and chooses exact $k$ nearest objects from the candidate objects. Evaluating the query results at each timestamp to keep the freshness of the query answer, while the query object is moving, will create significant computation burden for the client. We therefore propose an efficient approach called a safe-region-based approach (SRA) for computing a safe segment region and the safe exit points of a moving nearest neighbor (NN) query in a road network. SRA avoids evaluation of candidate answers returned by the location-based server since it will have high computation cost in the query side. Additionally, we applied SRA for a directed road network, where each road network has a particular orientation and the network distances are not symmetric. Our experimental results demonstrate that SRA significantly outperforms a conventional solution in terms of both computational and communication costs.

Theoretical exploration of network structure significance requires a range of different networks for comparison. Here, we present a new method to construct networks in a spatial setting that uses spectral methods in combination with a probability distribution function. Nearly all previous algorithms for network construction have assumed randomized distribution of links or a distribution dependent on the degree of the nodes. We relax those assumptions. Our algorithm is capable of creating spectral networks along a gradient from random to highly clustered or diverse networks. Number of nodes and link density are specified from start and the structure is tuned by three parameters (γ, σ, κ). The structure is measured by fragmentation, degree assortativity, clustering and group betweenness of the networks. The parameter γ regulates the aggregation in the spatial node pattern and σ and κ regulates the probability of link forming.