• Publications
  • Influence
Personalized Knowledge Graph Summarization: From the Cloud to Your Pocket
TLDR
We propose a new problem called personalized knowledge graph summarization. Expand
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Mining Persistent Activity in Continually Evolving Networks
TLDR
Frequent pattern mining is a key area of study that gives insights into the structure and dynamics of evolving networks, such as social or road networks. Expand
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What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization
TLDR
We introduce a unified solution to KG characterization by formulating the problem as unsupervised KG summarization with a set of inductive, soft rules, which describe what is normal in a KG, and thus can be used to identify what is abnormal, whether it be strange or missing. Expand
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A Hidden Challenge of Link Prediction: Which Pairs to Check?
  • Caleb Belth
  • Computer Science
  • IEEE International Conference on Data Mining…
  • 1 November 2020
TLDR
The traditional setup of link prediction in networks assumes that a test set of node pairs, which is usually balanced, is available over which to predict the presence of links. Expand
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When to Remember Where You Came from: Node Representation Learning in Higher-order Networks
TLDR
We propose a node representation learning framework called EVO or Embedding Variable Orders, which captures non-Markovian dependencies by combining work on higher-order networks with node embeddings. Expand
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The Greedy and Recursive Search for Morphological Productivity
TLDR
We propose a greedy search model that automatically hypothesizes rules and evaluates their productivity over a vocabulary. Expand
What is Normal, What is Strange, and What is Missing in an Knowledge Graph
TLDR
We introduce a unified solution to KG characterization that learns a summary of inductive rules that best compress the KG according to the Minimum Description Length principle---a formulation that we are the first to use in the context of KG rule mining, while also being efficient for large knowledge graphs. Expand