most citedTMIXT: A process flow for Transcribing MIXed handwritten and machine-printed Text

3 citations · 3 across the 1 of their papers we have counts for

collaborators

5 papers

cs.DC2019

Distributed Edge Partitioning for Trillion-edge Graphs

Masatoshi Hanai, Toyotaro Suzumura, Wen Jun Tan +3

We propose Distributed Neighbor Expansion (Distributed NE), a parallel and distributed graph partitioning method that can scale to trillion-edge graphs while providing high partiti…

cs.SI2019

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions

Stephen Bonner, Amir Atapour-Abarghouei, Philip T Jackson +5

Graphs have become a crucial way to represent large, complex and often temporal datasets across a wide range of scientific disciplines. However, when graphs are used as input to ma…

cs.LG20193 cited

TMIXT: A process flow for Transcribing MIXed handwritten and machine-printed Text

Fady Medhat, Mahnaz Mohammadi, Sardar Jaf +6

Handling large corpuses of documents is of significant importance in many fields, no more so than in the areas of crime investigation and defence, where an organisation may be pres…

cs.SI2018

Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation Learning

Stephen Bonner, John Brennan, Ibad Kureshi +3

Graphs are a commonly used construct for representing relationships between elements in complex high dimensional datasets. Many real-world phenomenon are dynamic in nature, meaning…

cs.LG2018

Exploring the Semantic Content of Unsupervised Graph Embeddings: An Empirical Study

Stephen Bonner, Ibad Kureshi, John Brennan +3

Graph embeddings have become a key and widely used technique within the field of graph mining, proving to be successful across a broad range of domains including social, citation,…