Learning in the presence of concept drift and hidden contexts
Machine Learning
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
GraphScope: parameter-free mining of large time-evolving graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Emerging Patterns in Spatial Databases: A Multi-relational Approach
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Novelty Detection from Evolving Complex Data Streams with Time Windows
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
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Most of the works on learning from networked data assume that the network is static. In this paper we consider a different scenario, where the network is dynamic, i.e. nodes/relationships can be added or removed and relationships can change in their type over time. We assume that the "core" of the network is more stable than the "marginal" part of the network, nevertheless it can change with time. These changes are of interest for this work, since they reflect a crucial step in the network evolution. Indeed, we tackle the problem of discovering evolution chains, which express the temporal evolution of the "core" of the network. To describe the "core" of the network, we follow a frequent pattern-mining approach, with the critical difference that the frequency of a pattern is computed along a time-period and not on a static dataset. The proposed method proceeds in two steps: 1) identification of changes through the discovery of emerging patterns; 2) composition of evolution chains by joining emerging patterns. We test the effectiveness of the method on both real and synthetic data.