The temporalgraph can represent a temporal relationship widely used in compound synthesis analysis, biological gene analysis, etc. However, the temporalgraph would embody vertex updates frequently, high time resolut...
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The temporalgraph can represent a temporal relationship widely used in compound synthesis analysis, biological gene analysis, etc. However, the temporalgraph would embody vertex updates frequently, high time resolution, and not enumerated rules. The construction and update of some temporal graph models are too dependent on the graph operation sequence, which leads to a lack of an effective model. Simultaneously, the temporal subgraph clustering of the temporalgraph with frequent updating for the lack of an effective model leads to low accuracy. Therefore, we propose an efficient and frequently updated temporal graph model as vertex driven and corresponding temporal subgraph clustering method. First, we propose a temporalgraph construction algorithm and set two thresholds to divide the temporalgraph on a timeline to obtain temporal subgraphs. Next, an enhancement strategy based on the sliding window is proposed to accelerate the construction process. Third, we offer a double-standard temporal subgraph clustering method based on community comparison and temporal distance. The temporal subgraph can be effectively distinguished in temporal and structure dimensions. Lastly, experimental results on both real and synthetic datasets show that the temporal graph model proposed in this work can reduce the time overhead of construction compared to other existing models. The cluster method improves the clustering accuracy of temporal subgraphs. The clustering results show through the hierarchical clustering at the same time.
For deep space networks, the major challenge is that transmission power consumption is serious while the power resource is limited. In this paper, we proposed a temporal graph model based Power Aware Routing algorithm...
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ISBN:
(纸本)9781509055074
For deep space networks, the major challenge is that transmission power consumption is serious while the power resource is limited. In this paper, we proposed a temporal graph model based Power Aware Routing algorithm using a weighted metric of remaining power level, data rate and distance of nodes for determining routes. The traditional standards and techniques of static networks is hard to solve routing problem in our networks. And the temporal graph model contributes to our scenario. Because the future contact information between nodes can be predicted accurately and we can use temporalgraphs to model it. Our algorithm finds the route that obtains the minimal transmission power cost although these routes may be different. The numerical results demonstrate that our routing algorithm selects the path with low total transmission power in the first transmission round and achieves the maximum support transmission opportunities, making the network live longer.
With global coverage abilities, satellite networks are expected to provide users with ubiquitous data services. However, routing in satellite networks faces more challenges due to the satellite movement. Fortunately, ...
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ISBN:
(纸本)9781467389990
With global coverage abilities, satellite networks are expected to provide users with ubiquitous data services. However, routing in satellite networks faces more challenges due to the satellite movement. Fortunately, the satellite movement can be predicted by orbit calculation. Based on this characteristic, a lot of existing routing algorithms use temporal graph model (TGM) to calculate instantaneous satellite network topologies at discrete times as priori knowledge for routing decision. In this case, high computation cost will be involved. In this paper, we propose a novel temporal Netgrid model (TNM) to represent the time-varying satellite network topology. In TNM, the whole space is divided into small cubes (i.e. netgrids) and then satellites can be located by netgrids instead of coordinates. By doing so, TNM reduces the computation complexity from O(N-2) to O(N-2) compared with TGM. Furthermore, an Earliest Arrival Space Routing (EASR) algorithm is proposed, which attempt to find the earliest arrival paths from the source node to any other reachable nodes with low computation cost. Simulations are performed to validate the effectiveness of the proposed routing algorithm. Results show that EASR algorithm achieves a significant reduction in computation complexity as well as acceptable routing performance in terms of data delivery ratio.
We adopt temporal graph model to explore the dynamic temporal properties of three mobility datasets in DTN (Delay Tolerant Network), collected from different sources, including one university campus WLAN and two confe...
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We adopt temporal graph model to explore the dynamic temporal properties of three mobility datasets in DTN (Delay Tolerant Network), collected from different sources, including one university campus WLAN and two conferences WLANs. With this model, we observe that the temporal network density of DTN changes with time and approximate varies periodically. Then we study the impact of this phenomenon on information diffusion. Our studies and findings can be used for establishing more realistic mobile model, building intelligent routing algorithm, and design advanced applications for DTNs.
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