Inferring Web communities from link topology
Proceedings of the ninth ACM conference on Hypertext and hypermedia : links, objects, time and space---structure in hypermedia systems: links, objects, time and space---structure in hypermedia systems
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Topic detection and tracking evaluation overview
Topic detection and tracking
SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Topic-conditioned novelty detection
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
Scalable modeling of real graphs using Kronecker multiplication
Proceedings of the 24th international conference on Machine learning
Evolutionary spectral clustering by incorporating temporal smoothness
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Microscopic evolution of social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
ICCSIT '08 Proceedings of the 2008 International Conference on Computer Science and Information Technology
Mining communities in networks: a solution for consistency and its evaluation
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Using friendship ties and family circles for link prediction
SNAKDD'08 Proceedings of the Second international conference on Advances in social network mining and analysis
LPmade: Link Prediction Made Easy
The Journal of Machine Learning Research
An In-depth Study of Stochastic Kronecker Graphs
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
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Citation networks contain temporal information about what researchers are interested in at a certain time. A community in such a network is built around either a renowned researcher or a common research field; either way, analyzing how the community will change in the future will give insight into the research trend in the future. The paper proposes methods to analyze how communities change over time in the citation network graph without additional external information and based on node and link prediction and community detection. Different combinations of the proposed methods are also analyzed. Experiments show that the proposed methods can identify the changes in citation communities multiple years in the future with performance differing according to the analyzed time span. Furthermore, the method is shown to produce higher performance when analyzing communities to be disbanded and to be formed in the future.