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Propagation-Based Temporal Network Summarization
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DOI：10.1109/TKDE.2017.2776282.png)
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摘要
En 中文
Modern networks are very large in size and also evolve with time. As their sizes grow, the complexity of performing network analysis grows as well. Getting a smaller representation of a temporal network with similar properties will help in various data mining tasks. In this paper, we study the novel problem of getting a smaller diffusion-equivalent representation of a set of time-evolving networks. We first formulate a well-founded and general temporal-network condensation problem based on the so-called system-matrix of the network. We then propose NETCONDENSE, a scalable and effective algorithm which solves this problem using careful transformations in sub-quadratic running time, and linear space complexities. Our extensive experiments show that we can reduce the size of large real temporal networks (from multiple domains such as social, co-authorship, and email) significantly without much loss of information. We also show the wide-applicability of NETCONDENSE by leveraging it for several tasks: for example, we use it to understand, explore, and visualize the original datasets and to also speed-up algorithms for the influence-maximization and event detection problems on temporal networks.
Keyword:
Graph summarization
temporal networks
propagation
graph mining
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10.4
论文数:
6.9K
被引数:
3.2W
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