Identifying graphs from noisy and incomplete data
Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data
Role of weak ties in link prediction of complex networks
Proceedings of the 1st ACM international workshop on Complex networks meet information & knowledge management
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Opinion graphs for polarity and discourse classification
TextGraphs-4 Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing
Identifying graphs from noisy and incomplete data
ACM SIGKDD Explorations Newsletter
Learning algorithms for link prediction based on chance constraints
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Collective classification of congressional floor-debate transcripts
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Classification and annotation in social corpora using multiple relations
Proceedings of the 20th ACM international conference on Information and knowledge management
Reciprocal and heterogeneous link prediction in social networks
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
Discovering relationship types between users using profiles and shared photos in a social network
Multimedia Tools and Applications
A supervised machine learning classification algorithm for research articles
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Mining collective intelligence in diverse groups
Proceedings of the 22nd international conference on World Wide Web
Transferring heterogeneous links across location-based social networks
Proceedings of the 7th ACM international conference on Web search and data mining
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The problems of object classification (labeling the nodes of a graph) and link prediction (predicting the links in a graph) have been largely studied independently. Com- monly, object classification is performed assuming a com- plete set of known links and link prediction is done assum- ing a fully observed set of node attributes. In most real world domains, however, attributes and links are often miss- ing or incorrect. Object classification is not provided with all the links relevant to correct classification and link pre- diction is not provided all the labels needed for accurate link prediction. In this paper, we propose an approach that addresses these two problems by interleaving object clas- sification and link prediction in a collective algorithm. We investigate empirically the conditions under which an inte- grated approach to object classification and link prediction improves performance, and find that performance improves over a wide range of network types, and algorithm settings.