Bisociative discovery of interesting relations between domains
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
From information networks to bisociative information networks
Bisociative Knowledge Discovery
Towards discovery of subgraph bisociations
Bisociative Knowledge Discovery
Semantic Pattern Transformation: Applying Knowledge Discovery Processes in Heterogeneous Domains
Proceedings of the 13th International Conference on Knowledge Management and Knowledge Technologies
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In this paper we propose two methods to derive two different kinds of node similarities in a network based on their neighborhood. The first similarity measure focuses on the overlap of direct and indirect neighbors. The second similarity compares nodes based on the structure of their - possibly also very distant - neighborhoods. Instead of using standard node measures, both similarities are derived from spreading activation patterns over time. Whereas in the first method the activation patterns are directly compared, in the second method the relative change of activation over time is compared. We apply both methods to a real-world graph dataset and discuss the results.