The increasing popularity of graph data in various domains has led to a renewed interest in understanding hidden relationships between nodes in a single large graph. And graph summarization is to find a concise but meaningful representation of a given graph. In this paper, we studied the problem of summarizing graph with content associated with nodes. We propose a graph summarization algorithm AGSUMMARY, which achieves a combination of topological and attribute similarities. Our method utilizes the Minimum Description Length (MDL) principle to model the graph summarization problem into a code cost function, and compute an optimal summary of graph with neighborhood greedy strategy. It requires no specified number of summary parts and its running time scales linearly with graph size and the average degree of nodes. Experimental results demonstrate the effectiveness and efficiency of our proposed method. For further illustration, a case example is given to explain our method.

The scheduling of earth observation satellites (EOSs) data transmission is a complex combinatorial optimization problem. Current researches mainly deal with this problem on the assumption that the data transmission mode is xed, either playback or real-time transmission. Considering the characteristic of the problem, a multi-satellite real-time and playback data transmission scheduling model is established and a novel algorithm based on quantum discrete particle swarm optimization (QDPSO) is proposed. Furthermore, we design the longest compatible transmission chain mutation operator to enhance the performance of the algorithm. Finally, some experiments are implemented to validate correctness and practicability of the proposed algorithm.

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Multi-satellite data downlink resource scheduling is a complex combinatorial optimization problem. Current researches cannot handle the problem effectivel y when the satellite observation tasks increase over time. Considering the characteristic of the problem, a data downlink resource scheduling model adapting to observation task increments is established and a novel algorithm based on evolutionar y computation is proposed. Finall y , some experiments are conducted to validate the correctness and practicabilit y of our algorithm.

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