Modern networks are very large in size and also evolve with time. As their size grows, 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 temporalnetwork 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 influencemaximization problem on temporal networks.
Another task related with both network comparison and network visualization is network condensation REF ; its aim is to reduce the size of the temporal network significantly without much loss of information.
Condensing Temporal Networks using Propagation
SDM
Computer Science