2019

1. Meng, C., Yang, J., Ribeiro, B., & Neville, J. (2019). HATS: A Hierarchical Sequence-Attention Framework for Inductive Set-of-Sets Embeddings. Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. pdf
2. Goindani, M., & Neville, J. (2019). Learning How to Intervene in True News Diffusion to Combat Fake News Spread. Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence. pdf
3. Lai, Y., Goldwasser, D., & Neville, J. (2019). TransConv: Relationship Embedding in Social Networks. Proceedings of the 33rd AAAI Conference on Artificial Intelligence. pdf
4. Park, H., & Neville, J. (2019). Exploiting Interaction Links for Node Classification with Deep Graph Neural Networks. Proceedings of the 29th International Joint Conference on Artificial Intelligence. pdf
5. Yang, J., Rao, V., & Neville, J. (2019). A Stein–Papangelou Goodness-of-Fit Test for Point Processes. Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTAT). pdf

2018

1. Meng, C., Mouli, C., Ribeiro, B., & Neville, J. (2018). Subgraph Pattern Neural Networks for High-Order Graph Evolution Prediction. Proceedings of the 32nd AAAI Conference on Artificial Intelligence. pdf
2. La Fond, T., Neville, J., & Gallagher, B. (2018). Designing Size Consistent Statistics for Accurate Anomaly Detection in Dynamic Networks. ACM Transactions on Knowledge Discovery from Data, 12(14). pdf
3. Hang, M., Pytlarz, I., & Neville, J. (2018). Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction. Proceedings of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. pdf
4. Moreno, S., Neville, J., & Kirshner, S. (2018). Tied Kronecker Product Graph Models to Capture Variance in Network Populations. ACM Transactions on Knowledge Discovery from Data, 20(3). pdf
5. Moreno, S., Pfeiffer III, J., & Neville, J. (2018). Scalable and exact sampling method for probabilistic generative graph models. Data Mining and Knowledge Discovery. pdf
6. Yang, J., Liu, Q., Rao, V., & Neville, J. (2018). Goodness-of-fit Testing for Discrete Distributions via Stein Discrepancy. Proceedings of the 35th International Conference on Machine Learning. pdf
7. Tan, X., Rao, V., & Neville, J. (2018). Nested CRP with Hawkes-Gaussian Processes. Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTAT). pdf
8. Tan, X., Rao, V., & Neville, J. (2018). The Indian Buffet Hawkes Process to Model Evolving Latent Influences. Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence. pdf
9. Gomes, G., Rao, V., & Neville, J. (2018). Multi-level hypothesis testing for populations of heterogeneous networks. Proceedings of the 18th IEEE International Conference on Data Mining. pdf

2017

1. Moore, J., & Neville, J. (2017). Deep Collective Inference. Proceedings of the 31st AAAI Conference on Artificial Intelligence. pdf
2. Ahmed, N., Neville, J., Rossi, R., Duffield, N., & Willke, T. (2017). Graphlet Decomposition: Framework, Algorithms, and Applications. Knowledge and Information Systems, 50(3). pdf
3. Robles, P., Moreno, S., & Neville, J. (2017). Unified Representation and Lifted Sampling for Generative Models of Social Networks. Proceedings of the 26th International Joint Conference on Artificial Intelligence. pdf
4. Yang, J., Rao, V., & Neville, J. (2017). Decoupling Homophily and Reciprocity with Latent Space Network Models. Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence. pdf
5. Yang, J., Ribeiro, B., & Neville, J. (2017). Should We Be Confident in Peer Effects Estimated From Partial Crawls of Social Networks? Proceedings of the 11th International AAAI Conference on Weblogs and Social Media. pdf
6. Yang, J., Ribeiro, B., & Neville, J. (2017). Stochastic Gradient Descent for Relational Logistic Regression via Partial Network Crawls. Proceedings of the 7th International Workshop on Statistical Relational AI, UAI. pdf
7. Li, C., Lai, Y., Goldwasser, D., & Neville, J. (2017). Joint Embedding Models for Textual and Social Analysis. Proceedings of the 1st Workshop on Deep Structured Prediction, ICML. pdf
8. Park, H., Moore, J., & Neville, J. (2017). Deep Dynamic Relational Classifiers: Exploiting Dynamic Neighborhoods in Complex Networks. Proceedings of the Mining Actionable Insights from Social Networks Workshop, WSDM. pdf

2016

1. Alodah, I., & Neville, J. (2016). Combining Gradient Boosting Machines with Collective Inference to Predict Continuous Values. Proceedings of the 6th International Workshop on Statistical Relational AI, IJCAI. pdf
2. Robles, P., Moreno, S., & Neville, J. (2016). Sampling of Attributed Networks from Hierarchical Generative Models. Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining. pdf
3. La Fond, T., Neville, J., & Gallagher, B. (2016). Generating Local Explanations of Network Anomalies via Score Decomposition. Proceedings of the ODD 4.0: Outlier Definition, Detection, and Description on Demand, KDD. pdf
4. Lai, Y., Li, C., Goldwasser, D., & Neville, J. (2016). Better Together: Combining Language and Social Interactions into a Shared Representation. Proceedings of the TextGraphs Workshop 2016, NAACL. pdf
5. Zhe, S., Lee, K., Zhang, K., & Neville, J. (2016). Online Spike-and-slab Inference with Stochastic Expectation Propagation. Proceedings of the 2016 Workshop on Advances in Approximate Bayesian Inference, NIPS.
6. Zeno, G., & Neville, J. (2016). Investigating the Impact of Graph Structure and Attribute Correlation on Collective Classification Performance. Proceedings of the 13th Workshop on Mining and Learning with Graphs, KDD. pdf

2015

1. Pfeiffer III, J., Neville, J., & Bennett, P. (2015). Overcoming Relational Learning Biases to Accurately Predict Preferences in Large Scale Networks. Proceedings of the 24th International World Wide Web Conference (WWW). pdf
2. Ahmed, N., Neville, J., Rossi, R., & Duffield, N. (2015). Efficient Graphlet Counting for Large Networks. Proceedings of the 15th IEEE International Conference on Data Mining. pdf
3. Robles, P., Moreno, S., & Neville, J. (2015). Using Bayesian Network Representations for Effective Sampling from Generative Network Models. Proceedings of the 5th International Workshop on Statistical Relational AI, UAI. pdf
4. Niu, R., Moreno, S., & Neville, J. (2015). Analyzing the Transferability of Collective Inference Models Across Networks. Proceedings of the International Workshop on Information Analysis and Data Mining Over Social Network, ICDM. pdf
5. Moore, J., Mussmann, S., Pfeiffer III, J., & Neville, J. (2015). Incorporating Assortativity and Degree Dependence into Scalable Network Models. Proceedings of the 29th AAAI Conference on Artificial Intelligence. pdf

2014

1. Pfeiffer III, J., Moreno, S., La Fond, T., Neville, J., & Gallagher, B. (2014). Attributed Graph Models: Modeling network structure with correlated attributes. Proceedings of the 23rd International World Wide Web Conference (WWW). pdf
2. Ahmed, N., Duffield, N., Neville, J., & Kompella, R. (2014). Graph Sample and Hold: A Framework for Big-Graph Analytics. Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. pdf
3. Pfeiffer III, J., Neville, J., & Bennett, P. (2014). Active Exploration in Networks: Using Probabilistic Relationships for Learning and Inference. Proceedings of the 23st ACM International Conference on Information and Knowledge Management. pdf
4. Moreno, S., Pfeiffer III, J., Neville, J., & Kirshner, S. (2014). A Scalable Method for Accurate Sampling from Kronecker Models. Proceedings of the 14th IEEE International Conference on Data Mining. pdf
5. Pfeiffer III, J., Neville, J., & Bennett, P. (2014). Composite Likelihood Data Augmentation for Within-Network Statistical Relational Learning. Proceedings of the 14th IEEE International Conference on Data Mining. pdf
6. La Fond, T., Neville, J., & Gallagher, B. (2014). Anomaly detection in networks with changing trends. Proceedings of the ODD^2 Workshop, KDD. pdf
7. Moore, J., Mussmann, S., Pfeiffer III, J., & Neville, J. (2014). Assortativity in Chung Lu Random Graph Models. Proceedings of the 8th SNA-KDD Workshop, KDD.
8. Ahmed, N., Neville, J., & Kompella, R. (2014). Network Sampling: From Static to Streaming Graphs. ACM Transactions on Knowledge Discovery from Data, 8(2). pdf

2013

1. Rossi, R., Gallagher, B., Neville, J., & Henderson, K. (2013). Modeling Dynamic Behavior in Large Evolving Graphs. Proceedings of the 6th ACM International Conference on Web Search and Data Mining. pdf
2. Moreno, S., Neville, J., & Kirshner, S. (2013). Learning Mixed Kronecker Product Graph Models with Simulated Method of Moments. Proceedings of the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. pdf
3. Pfeiffer III, J., Neville, J., & Bennett, P. (2013). Combining Active Sampling with Parameter Estimation and Prediction in Single Networks. Proceedings of the Structured Learning: Inferring Graphs from Structured and Unstructured Inputs Workshop, ICML. pdf
4. Moreno, S., & Neville, J. (2013). Network Hypothesis Testing Using Mixed Kronecker Product Graph Models. Proceedings of the 13th IEEE International Conference on Data Mining. pdf
5. Moreno, S., Robles, P., & Neville, J. (2013). Block Kronecker Product Graph Models. Proceedings of the 11th Workshop on Mining and Learning with Graphs, KDD. pdf
6. Xiang, R., & Neville, J. (2013). Collective Inference for Network Data with Copula Latent Markov Networks. Proceedings of the 6th ACM International Conference on Web Search and Data Mining. pdf

2012

1. Neville, J., Gallagher, B., Eliassi-Rad, T., & Wang, T. (2012). Correcting Evaluation Bias of Relational Classifiers with Network Cross Validation. Knowledge and Information Systems, 30(1), 31–55. pdf
2. Rossi, R., & Neville, J. (2012). Time-Evolving Relational Classification and Ensemble Methods. Proceedings of the 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining. pdf
3. Nagaraj, K., Killian, C., & Neville, J. (2012). Structured Comparative Analysis of Systems Logs to Diagnose Performance Problems. Proceedings of the 9th USENIX Symposium on Networked Systems Design and Implementation. pdf
4. Ahmed, N., Neville, J., & Kompella, R. (2012). Network Sampling Designs for Relational Classification. Proceedings of the 6th International AAAI Conference on Weblogs and Social Media. pdf
5. Rossi, R., Gallagher, B., Neville, J., & Henderson, K. (2012). Role-Dynamics: Fast Mining of Large Dynamic Networks. Proceedings of the 1st Workshop on Large Scale Network Analysis, WWW. pdf
6. Pfeiffer III, J., Neville, J., & Bennett, P. (2012). Active Sampling of Networks. Proceedings of the 10th Workshop on Mining and Learning with Graphs, ICML. pdf
7. Xiang, R., & Neville, J. (2012). On the Mismatch Between Learning and Inference for Single Network Domains. Proceedings of Inferning: Interactions between Inference and Learning Workshop, ICML. pdf
8. Bates, J., Neville, J., & Tyler, J. (2012). Using Latent Communication Styles to Predict Individual Characteristics. Proceedings of the 3rd Workshop on Social Media Analytics, KDD. pdf
9. Ahmed, N., Neville, J., & Kompella, R. (2012). Space-Efficient Sampling from Social Activity Streams. Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining, KDD. pdf
10. La Fond, T., Roberts, D., Neville, J., Tyler, J., & Connaughton, S. (2012). The Impact of Communication Structure and Interpersonal Dependencies on Distributed Teams. Proceedings of the 4th ASE/IEEE International Conference on Social Computing. pdf
11. Pfeiffer III, J., La Fond, T., Moreno, S., & Neville, J. (2012). Fast Generation of Large Scale Social Networks While Incorporating Transitive Closures. Proceedings of the 4th ASE/IEEE International Conference on Social Computing. pdf
12. Eldardiry, H., & Neville, J. (2012). An Analysis of How Ensembles of Collective Classifiers Improve Predictions in Graphs. Proceedings of the 21st ACM International Conference on Information and Knowledge Management. pdf
13. Rossi, R., McDowell, L., Aha, D., & Neville, J. (2012). Transforming Graph Data for Statistical Relational Learning. Journal of Artificial Intelligence Research, 45, 363–441. pdf

2011

1. Yakout, M., Elmagarmid, A., Neville, J., Ouzzani, M., & Ilyas, I. (2011). Guided Data Repair. Proceedings of the VLDB Endowment. pdf
2. Xiang, R., & Neville, J. (2011). Relational Learning with One Network: An Asymptotic Analysis. Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTAT). pdf
3. Pfeiffer III, J., & Neville, J. (2011). Methods to Determine Node Centrality and Clustering in Graphs with Uncertain Structure. Proceedings of the 5th International AAAI Conference on Weblogs and Social Media. pdf
4. Eldardiry, H., & Neville, J. (2011). Across-Model Collective Ensemble Classification. Proceedings of the 25th AAAI Conference on Artificial Intelligence. pdf
5. Kuwadekar, A., & Neville, J. (2011). Relational Active Learning for Joint Collective Classification Models. Proceedings of the 28th International Conference on Machine Learning. pdf
6. Wang, T., Neville, J., Gallagher, B., & Eliassi-Rad, T. (2011). Correcting Bias in Statistical Tests for Network Classifier Evaluation. Proceedings of the 21st European Conference on Machine Learning. pdf
7. Xiang, R., & Neville, J. (2011). Understanding Propagation Error and Its Effect on Collective Classification. Proceedings of the 11th IEEE International Conference on Data Mining. pdf
8. Xiang, R., & Neville, J. (2011). Understanding Propagation Error and Its Effect on Collective Classification. Proceedings of the 9th Workshop on Mining and Learning with Graphs, KDD.
9. Baumann, D., Hambrusch, S., & Neville, J. (2011). Gender demographics trends and changes in U.S. CS departments. Communications of the ACM, 54(11), 38–42. pdf
10. Ahmed, N., Neville, J., & Kompella, R. (2011). Network Sampling via Edge-based Node Selection with Graph Induction (No.11-016; Numbers 11-016). Dept of Computer Science, Purdue University. pdf

2010

1. Xiang, R., Neville, J., & Rogati, M. (2010). Modeling Relationship Strength in Online Social Networks. Proceedings of the International World Wide Web Conference (WWW). pdf
2. La Fond, T., & Neville, J. (2010). Randomization tests for distinguishing social influence and homophily effects. Proceedings of the International World Wide Web Conference (WWW). pdf
3. Yakout, M., Elmagarmid, A., & Neville, J. (2010). Ranking for Data Repairs. Proceedings of the 4th International Workshop on Ranking in Databases, ICDE. pdf
4. Yakout, M., Elmagarmid, A., Neville, J., & Ouzzani, M. (2010). GDR: A System for Guided Data Repair. Proceedings of the 2010 International Conference on Management of Data (SIGMOD). pdf
5. Mayfield, C., Neville, J., & Prabhakar, S. (2010). ERACER: A Database Approach for Statistical Inference and Data Cleaning. Proceedings of the 2010 ACM SIGMOD Conference. pdf
6. Khosla, R., Fahmy, S., Hu, C., & Neville, J. (2010). Predicting Prex Availability in the Internet. Proceedings of the 29th IEEE Conference on Computer Communications (INFOCOM) Mini-Conference. pdf
7. Kuwadekar, A., & Neville, J. (2010). Combining Semi-supervised Learning and Relational Resampling for Active Learning in Network Domains. Proceedings of the Budgeted Learning Workshop, ICML. pdf
8. Eldardiry, H., & Neville, J. (2010). Multi-Network Fusion for Collective Inference. Proceedings of the 8th Workshop on Mining and Learning with Graphs, KDD. pdf
9. Ahmed, N., Berchmans, F., Neville, J., & Kompella, R. (2010). Time-Based Sampling of Social Network Activity Graphs. Proceedings of the 8th Workshop on Mining and Learning with Graphs. pdf
10. Rossi, R., & Neville, J. (2010). Modeling the Evolution of Discussion Topics and Communication to Improve Relational Classification. Proceedings of the 1st Workshop on Social Media Analytics, KDD. pdf
11. Pfeiffer III, J., & Neville, J. (2010). Probabilistic Paths and Centrality in Time. Proceedings of the 4th SNA-KDD Workshop, KDD. pdf
12. Ahmed, N., Neville, J., & Kompella, R. (2010). Reconsidering the Foundations of Network Sampling. Proceedings of the 2nd Workshop on Information in Networks. pdf
13. Moreno, S., Kirshner, S., Neville, J., & Vishwanathan, S. V. N. (2010). Tied Kronecker Product Graph Models to Capture Variance in Network Populations. Proceedings of the 48th Annual Allerton Conference on Communications, Control and Computing. pdf
14. Khosla, R., Fahmy, S., Hu, C., & Neville, J. (2010). Prediction models for long-term Internet prefix availability. Computer Networks. pdf
15. Moreno, S., Neville, J., Kirshner, S., & Vishwanathan, S. V. N. (2010). Modeling the Variance of Network Populations with Mixed Kronecker Product Graph Models. Proceedings of the Workshop on Networks Across Disciplines: Theory and Applications, NIPS. pdf

2009

1. Neville, J., Gallagher, B., & Eliassi-Rad, T. (2009). Evaluating Statistical Tests for Within-Network Classifiers of Relational Data. Proceedings of the 9th IEEE International Conference on Data Mining. pdf
2. Moreno, S., & Neville, J. (2009). An Investigation of the Distributional Characteristics of Generative Graph Models. Proceedings of the 1st Workshop on Information in Networks. pdf
3. Xiang, R., Neville, J., & Rogati, M. (2009). Modeling Relationship Strength in Online Social Networks. Proceedings of the Workshop on Analyzing Networks and Learning With Graphs, NIPS. pdf
4. Kahanda, I., & Neville, J. (2009). Using Transactional Information to Predict Link Strength in Online Social Networks. Proceedings of the 3rd International AAAI Conference on Weblogs and Social Media. pdf

2008

1. Neville, J., & Jensen, D. (2008). A Bias/Variance Decomposition for Models Using Collective Inference. Machine Learning Journal. pdf
2. Xiang, R., & Neville, J. (2008). Pseudolikelihood EM for Within-Network Relational Learning. Proceedings of the 2nd SNA Workshop, KDD. pdf
3. Angin, P., & Neville, J. (2008). A Shrinkage Approach for Modeling Non-Stationary Relational Autocorrelation. Proceedings of the 2nd SNA Workshop, KDD. pdf
4. Eldardiry, H., & Neville, J. (2008). A Resampling Technique for Relational Data Graphs. Proceedings of the 2nd SNA Workshop, KDD. pdf
5. Xiang, R., & Neville, J. (2008). Pseudolikelihood EM for Within-Network Relational Learning. Proceedings of the 8th IEEE International Conference on Data Mining. pdf
6. Angin, P., & Neville, J. (2008). A Shrinkage Approach for Modeling Non-Stationary Relational Autocorrelation. Proceedings of the 8th IEEE International Conference on Data Mining. pdf
7. Sharan, U., & Neville, J. (2008). Temporal-Relational Classifiers for Prediction in Evolving Domains. Proceedings of the 8th IEEE International Conference on Data Mining. pdf
8. Singh, S., Mayfield, C., Shah, R., Prabhakar, S., Hambrusch, S., Neville, J., & Cheng, R. (2008). Database support for probabilistic attributes and tuples. Proceedings of the 24th International Conference on Data Engineering. pdf

2007

1. Neville, J., & Jensen, D. (2007). Relational Dependency Networks. Journal of Machine Learning Research. pdf
2. Neville, J., & Jensen, D. (2007). Bias-Variance Analysis for Relational Domains. Proceedings of the 17th International Conference on Inductive Logic Programming. pdf
3. Sharan, U., & Neville, J. (2007). Exploiting Time-Varying Relationships in Statistical Relational Models. Proceedings of the 1st SNA-KDD Workshop, KDD. pdf
4. Neville, J., & Jensen, D. (2007). Relational Dependency Networks. In L. Getoor & B. Taskar (Eds.), Introduction to Statistical Relational Learning. pdf

2006

1. Neville, J. (2006). Statistical Models and Analysis Techniques for Learning in Relational Data [PhD thesis]. University of Massachusetts Amherst. pdf

2005

1. Neville, J., Simsek, O., Jensen, D., Komoroske, J., Palmer, K., & Goldberg, H. (2005). Using Relational Knowledge Discovery to Prevent Securities Fraud. Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 449–458. pdf
2. Neville, J., & Jensen, D. (2005). Leveraging Relational Autocorrelation with Latent Group Models. Proceedings of the 5th IEEE International Conference on Data Mining, 322–329. pdf

2004

1. Neville, J., & Jensen, D. (2004). Dependency Networks for Relational Data. Proceedings of the 4th IEEE International Conference on Data Mining, 170–177. pdf
2. Neville, J., Adler, M., & Jensen, D. (2004). Spectral Clustering with Links and Attributes (No.04-42; Numbers 04-42). Dept of Computer Science, University of Massachusetts Amherst. pdf
3. Neville, J., Simsek, O., & Jensen, D. (2004). Autocorrelation and Relational Learning: Challenges and Opportunities. Proceedings of the Workshop on Statistical Relational Learning, ICML. pdf
4. Jensen, D., Neville, J., & Gallagher, B. (2004). Why Collective Inference Improves Relational Classification. Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 593–598. pdf

2003

1. Neville, J., Jensen, D., & Gallagher, B. (2003). Simple Estimators for Relational Bayesian Classifers. Proceedings of the 3rd IEEE International Conference on Data Mining, 609–612. pdf
2. Neville, J., Jensen, D., Friedland, L., & Hay, M. (2003). Learning Relational Probability Trees. Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 625–630. pdf
3. Neville, J., Rattigan, M., & Jensen, D. (2003). Statistical Relational Learning: Four Claims and a Survey. Proceedings of the Workshop on Learning Statistical Models from Relational Data, IJCAI. pdf
4. Jensen, D., Neville, J., & Rattigan, M. (2003). Randomization Tests for Relational Learning (No.03-05; Numbers 03-05). Dept of Computer Science, University of Massachusetts Amherst. pdf
5. Jensen, D., Neville, J., & Hay, M. (2003). Avoiding bias when aggregating relational data with degree disparity. Proceedings of the 20th International Conference on Machine Learning, 274–281. pdf
6. Neville, J., Adler, M., & Jensen, D. (2003). Clustering Relational Data Using Attribute and Link Information. Proceedings of the Text Mining and Link Analysis Workshop, IJCAI. pdf
7. McGovern, A., Friedland, L., Hay, M., Gallagher, B., Fast, A., Neville, J., & Jensen, D. (2003). Exploiting Relational Structure to Understand Publication Patterns in High-Energy Physics. SIGKDD Explorations, 5(2), 165–172. pdf
8. Neville, J., & Jensen, D. (2003). Collective Classification with Relational Dependency Networks. Proceedings of the 2nd Multi-Relational Data Mining Workshop, KDD, 77–91. pdf

2002

1. Jensen, D., & Neville, J. (2002). Schemas and Models. Proceedings of the Multi-Relational Data Mining Workshop, KDD. pdf
2. Jensen, D., & Neville, J. (2002). Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning. Proceedings of the 19th International Conference on Machine Learning, 259–266. pdf
3. Jensen, D., & Neville, J. (2002). Autocorrelation and Linkage Cause Bias in Evaluation of Relational Learners. Proceedings of the 12th International Conference on Inductive Logic Programming, 101–116. pdf
4. Jensen, D., & Neville, J. (2002). Data Mining in Social Networks. National Academy of Sciences Symposium on Dynamic Social Network Analysis. pdf
5. Neville, J., & Jensen, D. (2002). Supporting Relational Knowledge Discovery: Lessons in Architecture and Algorithm Design. Proceedings of the Data Mining Lessons Learned Workshop, ICML, 57–64. pdf

2001

1. Jensen, D., & Neville, J. (2001). Correlation and Sampling in Relational Data Mining. Proceedings of the 33rd Symposium on the Interface of Computing Science and Statistics. pdf

2000

1. Neville, J., & Jensen, D. (2000). Iterative Classification in Relational Data. Proceedings of the Workshop on Statistical Relational Learning, AAAI, 42–49. pdf