Search Results for author: Zekarias T. Kefato

Found 6 papers, 6 papers with code

Jointly Learnable Data Augmentations for Self-Supervised GNNs

1 code implementation23 Aug 2021 Zekarias T. Kefato, Sarunas Girdzijauskas, Hannes Stärk

Recently, a number of SSL methods for graph representation learning have achieved performance comparable to SOTA semi-supervised GNNs.

Data Augmentation Graph Representation Learning +2

Self-supervised Graph Neural Networks without explicit negative sampling

2 code implementations27 Mar 2021 Zekarias T. Kefato, Sarunas Girdzijauskas

This calls for unsupervised learning techniques that are powerful enough to achieve comparable results as semi-supervised/supervised techniques.

Contrastive Learning Data Augmentation +2

Dynamic Embeddings for Interaction Prediction

1 code implementation10 Nov 2020 Zekarias T. Kefato, Sarunas Girdzijauskas, Nasrullah Sheikh, Alberto Montresor

In recommender systems (RSs), predicting the next item that a user interacts with is critical for user retention.

Recommendation Systems

Gossip and Attend: Context-Sensitive Graph Representation Learning

1 code implementation30 Mar 2020 Zekarias T. Kefato, Sarunas Girdzijauskas

In this study we show that in-order to extract high-quality context-sensitive node representations it is not needed to rely on supplementary node features, nor to employ computationally heavy and complex models.

Clustering Community Detection +3

Which way? Direction-Aware Attributed Graph Embedding

1 code implementation30 Jan 2020 Zekarias T. Kefato, Nasrullah Sheikh, Alberto Montresor

Most studies ignore the directionality, so as to learn high-quality representations optimized for node classification.

General Classification Graph Embedding +2

Graph Neighborhood Attentive Pooling

1 code implementation28 Jan 2020 Zekarias T. Kefato, Sarunas Girdzijauskas

Network representation learning (NRL) is a powerful technique for learning low-dimensional vector representation of high-dimensional and sparse graphs.

Clustering Community Detection +3

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