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Hoda Eldardiry

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JMLR Journal 2020 Journal Article

Ensemble Learning for Relational Data

Hoda Eldardiry
Jennifer Neville
Ryan A. Rossi

We present a theoretical analysis framework for relational ensemble models. We show that ensembles of collective classifiers can improve predictions for graph data by reducing errors due to variance in both learning and inference. In addition, we propose a relational ensemble framework that combines a relational ensemble learning approach with a relational ensemble inference approach for collective classification. The proposed ensemble techniques are applicable for both single and multiple graph settings. Experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed framework. Finally, our experimental results support the theoretical analysis and confirm that ensemble algorithms that explicitly focus on both learning and inference processes and aim at reducing errors associated with both, are the best performers. [abs] [ pdf ][ bib ] &copy JMLR 2020. ( edit, beta )

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TIST Journal 2018 Journal Article

Interactive Visual Graph Mining and Learning

Ryan A. Rossi
Nesreen K. Ahmed
Rong Zhou
Hoda Eldardiry

This article presents a platform for interactive graph mining and relational machine learning called GraphVis. The platform combines interactive visual representations with state-of-the-art graph mining and relational machine learning techniques to aid in revealing important insights quickly as well as learning an appropriate and highly predictive model for a particular task (e.g., classification, link prediction, discovering the roles of nodes, and finding influential nodes). Visual representations and interaction techniques and tools are developed for simple, fast, and intuitive real-time interactive exploration, mining, and modeling of graph data. In particular, we propose techniques for interactive relational learning (e.g., node/link classification), interactive link prediction and weighting, role discovery and community detection, higher-order network analysis (via graphlets, network motifs), among others. GraphVis also allows for the refinement and tuning of graph mining and relational learning methods for specific application domains and constraints via an end-to-end interactive visual analytic pipeline that learns, infers, and provides rapid interactive visualization with immediate feedback at each change/prediction in real-time. Other key aspects include interactive filtering, querying, ranking, manipulating, exporting, as well as tools for dynamic network analysis and visualization, interactive graph generators (including new block model approaches), and a variety of multi-level network analysis techniques.

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AAAI Conference 2011 Conference Paper

Across-Model Collective Ensemble Classification

Hoda Eldardiry
Jennifer Neville

Ensemble classiﬁcation methods that independently construct component models (e. g. , bagging) improve accuracy over single models by reducing the error due to variance. Some work has been done to extend ensemble techniques for classiﬁcation in relational domains by taking relational data characteristics or multiple link types into account during model construction. However, since these approaches follow the conventional approach to ensemble learning, they improve performance by reducing the error due to variance in learning. We note however, that variance in inference can be an additional source of error in relational methods that use collective classiﬁcation, since inferred values are propagated during inference. We propose a novel ensemble mechanism for collective classiﬁcation that reduces both learning and inference variance, by incorporating prediction averaging into the collective inference process itself. We show that our proposed method signiﬁcantly outperforms a straightforward relational ensemble baseline on both synthetic and real-world datasets.

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