On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Enhanced hypertext categorization using hyperlinks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A Machine Learning Approach to Building Domain-Specific Search Engines
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Learning Dynamic Bayesian Networks
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Simple Estimators for Relational Bayesian Classifiers
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Aggregation-based feature invention and relational concept classes
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning relational probability trees
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Exploiting relational structure to understand publication patterns in high-energy physics
ACM SIGKDD Explorations Newsletter
Why collective inference improves relational classification
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Using relational knowledge discovery to prevent securities fraud
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Community mining from multi-relational networks
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
WebKDD/SNAKDD 2007: web mining and social network analysis post-workshop report
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Local likelihood modeling of temporal text streams
Proceedings of the 25th international conference on Machine learning
Fast mining of complex time-stamped events
Proceedings of the 17th ACM conference on Information and knowledge management
Quantifying the Impact of Information Aggregation on Complex Networks: A Temporal Perspective
WAW '09 Proceedings of the 6th International Workshop on Algorithms and Models for the Web-Graph
Towards time-aware link prediction in evolving social networks
Proceedings of the 3rd Workshop on Social Network Mining and Analysis
Referral based expertise search system in a time evolving social network
Proceedings of the Third Annual ACM Bangalore Conference
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Love all, trust a few: link prediction for trust and psycho-social factors in MMOs
SBP'12 Proceedings of the 5th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Enhanced spatiotemporal relational probability trees and forests
Data Mining and Knowledge Discovery
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In a growing number of relational domains, the data record temporal sequences of interactions among entities. For example, in citation domains authors publish scientific papers together each year and in telephone fraud detection domains people make calls to each other each day. The temporal dynamics of these interactions contain information that can improve predictive models (e.g., people publishing together frequently are likely to be publishing on the same topic) but to date there has been little effort to incorporate timevarying dependencies into relational models. Past work in relational learning has focused primarily on static "snapshots" of relational data. In this paper, we present an initial approach to modeling dynamic relational data graphs in predictive models of attributes. More specifically, we use a two-step process that first summarizes the dynamic graph with a weighted static graph and then incorporates the link weights in a relational Bayes classifier. We evaluate our approach on the Cora dataset (where co-author and citation links vary over time) showing that our approach results in significant performance gains over a baseline snapshot approach that ignores the temporal component of the data.