Topic modeling with network regularization
Proceedings of the 17th international conference on World Wide Web
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic topic models with biased propagation on heterogeneous information networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining heterogeneous information networks: the next frontier
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
The contextual focused topic model
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
A phrase mining framework for recursive construction of a topical hierarchy
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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In this demo we present AMETHYST, a system for exploring and analyzing a topical hierarchy constructed from a heterogeneous information network (HIN). HINs, composed of multiple types of entities and links are very common in the real world. Many have a text component, and thus can benefit from a high quality hierarchical organization of the topics in the network dataset. By organizing the topics into a hierarchy, AMETHYST helps understand search results in the context of an ontology, and explain entity relatedness at different granularities. The automatically constructed topical hierarchy reflects a domain-specific ontology, interacts with multiple types of linked entities, and can be tailored for both free text and OLAP queries.