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
Mining newsgroups using networks arising from social behavior
WWW '03 Proceedings of the 12th international conference on World Wide Web
The Journal of Machine Learning Research
Learning relational probability trees
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering evolutionary theme patterns from text: an exploration of temporal text mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Using relational knowledge discovery to prevent securities fraud
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Ontologies are us: A unified model of social networks and semantics
Web Semantics: Science, Services and Agents on the World Wide Web
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Temporal-Relational Classifiers for Prediction in Evolving Domains
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Topic and role discovery in social networks with experiments on enron and academic email
Journal of Artificial Intelligence Research
Joint group and topic discovery from relations and text
ICML'06 Proceedings of the 2006 conference on Statistical network analysis
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
Time-Evolving relational classification and ensemble methods
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
Improving government services with social media feedback
Proceedings of the 19th international conference on Intelligent User Interfaces
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Textual analysis is one means by which to assess communication type and moderate the influence of network structure in predictive models of individual behavior. However, there are few methods available to incorporate textual content into time-evolving network models. In particular, modeling both the evolution of network topology and textual content change in time-varying communication data poses a difficult challenge. In this work, we propose a Temporally-Evolving Network Classifier (TENC) to incorporate the influence of time-varying edges and temporally-evolving attributes in relational classification models. To facilitate this, we use an evolutionary latent topic approach to automatically discover and label communications between individuals in a network with their corresponding latent topic. The topics of the messages are incorporated into the TENC along with time-varying relationships and temporally-evolving attributes, using weighted, exponential kernel summarization. We evaluate the utility of the TENC on a real-world classification task, where the aim is to predict the effectiveness of a developer in the python open-source developer network. We take advantage of the textual content in developer emails and bug communications, which both evolve over time. The TENC paired with the latent topics significantly improves performance over the baseline classifiers that only take into account the static properties of the topics and communications. The results show that the TENC can be used to accurately model the complete-set of temporal dynamics in time-evolving communication networks.