most citedLaplacian Change Point Detection for Dynamic Graphs

56 citations · 60 across the 3 of their papers we have counts for

collaborators

5 papers

cs.SI2020

Contact Graph Epidemic Modelling of COVID-19 for Transmission and Intervention Strategies

Abby Leung, Xiaoye Ding, Shenyang Huang +1

The coronavirus disease 2019 (COVID-19) pandemic has quickly become a global public health crisis unseen in recent years. It is known that the structure of the human contact networ…

physics.soc-ph20201 cited

Incorporating Dynamic Flight Network in SEIR to Model Mobility between Populations

Xiaoye Ding, Shenyang Huang, Abby Leung +1

Current efforts of modelling COVID-19 are often based on the standard compartmental models such as SEIR and their variations. As pre-symptomatic and asymptomatic cases can spread t…

cs.LG202056 cited

Laplacian Change Point Detection for Dynamic Graphs

Shenyang Huang, Yasmeen Hitti, Guillaume Rabusseau +1

Dynamic and temporal graphs are rich data structures that are used to model complex relationships between entities over time. In particular, anomaly detection in temporal graphs is…

cs.LG20203 cited

RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning

Stefano Alletto, Shenyang Huang, Vincent Francois-Lavet +2

Almost all neural architecture search methods are evaluated in terms of performance (i.e. test accuracy) of the model structures that it finds. Should it be the only metric for a g…

cs.LG2019

Neural Architecture Search for Class-incremental Learning

Shenyang Huang, Vincent François-Lavet, Guillaume Rabusseau

In class-incremental learning, a model learns continuously from a sequential data stream in which new classes occur. Existing methods often rely on static architectures that are ma…