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20082023
most citedHashing-Accelerated Graph Neural Networks for Link Prediction

38 citations · 275 across the 37 of their papers we have counts for

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Showing 2021Show all

7 papers · 1 filter

cs.LG2021

Unraveling Social Perceptions & Behaviors towards Migrants on Twitter

Aparup Khatua, Wolfgang Nejdl

We draw insights from the social psychology literature to identify two facets of Twitter deliberations about migrants, i.e., perceptions about migrants and behaviors towards mi-gra…

cs.SD2021

Prototype Learning for Interpretable Respiratory Sound Analysis

Zhao Ren, Thanh Tam Nguyen, Wolfgang Nejdl

Remote screening of respiratory diseases has been widely studied as a non-invasive and early instrument for diagnosis purposes, especially in the pandemic. The respiratory sound cl…

cs.LG2021

FARF: A Fair and Adaptive Random Forests Classifier

Wenbin Zhang, Albert Bifet, Xiangliang Zhang +2

As Artificial Intelligence (AI) is used in more applications, the need to consider and mitigate biases from the learned models has followed. Most works in developing fair learning…

cs.DL2021★ 21 cited

EduCOR: An Educational and Career-Oriented Recommendation Ontology

Eleni Ilkou, Hasan Abu-Rasheed, Mohammadreza Tavakoli +4

With the increased dependence on online learning platforms and educational resource repositories, a unified representation of digital learning resources becomes essential to suppor…

cs.LG2021★ 38 cited

Hashing-Accelerated Graph Neural Networks for Link Prediction

Wei Wu, Bin Li, Chuan Luo +1

Networks are ubiquitous in the real world. Link prediction, as one of the key problems for network-structured data, aims to predict whether there exists a link between two nodes. T…

eess.SP2021★ 4 cited

Data augmentation for dealing with low sampling rates in NILM

Tai Le Quy, Sergej Zerr, Eirini Ntoutsi +1

Data have an important role in evaluating the performance of NILM algorithms. The best performance of NILM algorithms is achieved with high-quality evaluation data. However, many e…

cs.LG2021★ 7 cited

Membership Inference Attack on Graph Neural Networks

Iyiola E. Olatunji, Wolfgang Nejdl, Megha Khosla

Graph Neural Networks (GNNs), which generalize traditional deep neural networks on graph data, have achieved state-of-the-art performance on several graph analytical tasks. We focu…