Measuring the effects of preprocessing decisions and network forces in dynamic network analysis
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Randomization tests for distinguishing social influence and homophily effects
Proceedings of the 19th international conference on World wide web
Modeling relationship strength in online social networks
Proceedings of the 19th international conference on World wide web
Temporal Link Prediction Using Matrix and Tensor Factorizations
ACM Transactions on Knowledge Discovery from Data (TKDD)
Modeling the evolution of discussion topics and communication to improve relational classification
Proceedings of the First Workshop on Social Media Analytics
Regression on evolving multi-relational data streams
Proceedings of the 2011 Joint EDBT/ICDT Ph.D. Workshop
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Beyond friendship: modeling user activity graphs on social network-based gifting applications
Proceedings of the 2012 ACM conference on Internet measurement conference
Enhanced spatiotemporal relational probability trees and forests
Data Mining and Knowledge Discovery
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
Predicting group stability in online social networks
Proceedings of the 22nd international conference on World Wide Web
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
Data Mining and Knowledge Discovery
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Many relational domains contain temporal information and dynamics that are important to model (e.g., social networks, protein networks). However, past work in relational learning has focused primarily on modeling static "snapshots" of the data and has largely ignored the temporal dimension of these data. In this work, we extend relational techniques to temporally-evolving domains and outline a representational framework that is capable of modeling both temporal and relational dependencies in the data. We develop efficient learning and inference techniques within the framework by considering a restricted set of temporal-relational dependencies and using parameter-tying methods to generalize across relationships and entities. More specifically, we model dynamic relational data with a two-phase process, first summarizing the temporal-relational information with kernel smoothing, and then moderating attribute dependencies with the summarized relational information. We develop a number of novel temporal-relational models using the framework and then show that the current approaches to modeling static relational data are special cases within the framework. We compare the new models to the competing static relational methods on three real-world datasets and show that the temporal-relational models consistently outperform the relational models that ignore temporal information - achieving significant reductions in error ranging from 15% to 70%.