• Publications
  • Influence
Mining Persistent Activity in Continually Evolving Networks
TLDR
This work proposes the problem of mining activity that persists through time in continually evolving networks-i.e., activity that repeatedly and consistently occurs, and proposes PENminer, an efficient framework for mining activity snippets' Persistence in Evolving Networks.
The Greedy and Recursive Search for Morphological Productivity
TLDR
This work proposes a greedy search model that automatically hypothesizes rules and evaluates their productivity over a vocabulary and produces responses to nonce words that are more similar to those of human subjects than current neural network models’ responses are.
When to Remember Where You Came from: Node Representation Learning in Higher-order Networks
TLDR
This work proposes a node representation learning framework called EVO or Embedding Variable Orders, which captures non-Markovian dependencies by combining work on higher-order networks with work on node embeddings, and shows that EVO outperforms baselines in tasks where high-order dependencies are likely to matter.
Personalized Knowledge Graph Summarization: From the Cloud to Your Pocket
TLDR
GLIMPSE, a summarization framework that provides theoretical guarantees on the summary's utility and is linear in the number of edges in the KG, is proposed and shown to efficiently create summaries that outperform strong baselines in query answering F1 score.
What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization
TLDR
This work introduces 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.
What is Normal, What is Strange, and What is Missing in an Knowledge Graph
TLDR
This work proposes 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 strange or missing.
A Hidden Challenge of Link Prediction: Which Pairs to Check?
  • Caleb Belth
  • Computer Science
    IEEE International Conference on Data Mining…
  • 1 November 2020
TLDR
LinkWaldo is introduced, a framework for choosing from this quadratic, massively-skewed search space of node pairs, a concise set of candidate pairs that, in addition to being in close proximity, also structurally resemble the observed edges.