G
Galileo Namata
Researcher at University of Maryland, College Park
Publications - 20
Citations - 3594
Galileo Namata is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Graph (abstract data type) & Biological network. The author has an hindex of 14, co-authored 20 publications receiving 2552 citations.
Papers
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Journal ArticleDOI
Collective Classification in Network Data
TL;DR: This article introduces four of the most widely used inference algorithms for classifying networked data and empirically compare them on both synthetic and real-world data.
Proceedings Article
Relationship identification for social network discovery
TL;DR: A supervised ranking approach is proposed to the challenge of relationship identification where the objective is to identify relevant communications that substantiate a given social relationship type and its performance on a manager-subordinate relationship identification task using the Enron email corpus is assessed.
Proceedings ArticleDOI
Supervised and Unsupervised Methods in Employing Discourse Relations for Improving Opinion Polarity Classification
TL;DR: Two diverse global inference paradigms are used: a supervised collective classification framework and an unsupervised optimization framework, which perform substantially better than baseline approaches, establishing the efficacy of the methods and the underlying discourse scheme.
Proceedings ArticleDOI
Combining Collective Classification and Link Prediction
TL;DR: This paper investigates empirically the conditions under which an integrated approach to object classification and link prediction improves performance, and finds that performance improves over a wide range of network types, and algorithm settings.
Proceedings ArticleDOI
Opinion Graphs for Polarity and Discourse Classification
TL;DR: This work shows how to construct discourse-level opinion graphs to perform a joint interpretation of opinions and discourse relations, and how this inter-dependent framework can be used to augment and improve the performance of local polarity and discourse-link classifiers.