10 citations · 21 across the 6 of their papers we have counts for
8 papers · 1 filter
Leap: Inductive Link Prediction via Learnable TopologyAugmentation
Ahmed E. Samy, Zekarias T. Kefato, Sarunas Girdzijauskas
Link prediction is a crucial task in many downstream applications of graph machine learning. To this end, Graph Neural Network (GNN) is a widely used technique for link prediction,…
Data-Driven Self-Supervised Graph Representation Learning
Ahmed E. Samy, Zekarias T. Kefatoa, Sarunas Girdzijauskasa
Self-supervised graph representation learning (SSGRL) is a representation learning paradigm used to reduce or avoid manual labeling. An essential part of SSGRL is graph data augmen…
Jointly Learnable Data Augmentations for Self-Supervised GNNs
Zekarias T. Kefato, Sarunas Girdzijauskas, Hannes Stärk
Self-supervised Learning (SSL) aims at learning representations of objects without relying on manual labeling. Recently, a number of SSL methods for graph representation learning h…
Self-supervised Graph Neural Networks without explicit negative sampling
Zekarias T. Kefato, Sarunas Girdzijauskas
Real world data is mostly unlabeled or only few instances are labeled. Manually labeling data is a very expensive and daunting task. This calls for unsupervised learning techniques…
Dynamic Embeddings for Interaction Prediction
Zekarias T. Kefato, Sarunas Girdzijauskas, Nasrullah Sheikh +1
In recommender systems (RSs), predicting the next item that a user interacts with is critical for user retention. While the last decade has seen an explosion of RSs aimed at identi…
Gossip and Attend: Context-Sensitive Graph Representation Learning
Zekarias T. Kefato, Sarunas Girdzijauskas
Graph representation learning (GRL) is a powerful technique for learning low-dimensional vector representation of high-dimensional and often sparse graphs. Most studies explore the…
Which way? Direction-Aware Attributed Graph Embedding
Zekarias T. Kefato, Nasrullah Sheikh, Alberto Montresor
Graph embedding algorithms are used to efficiently represent (encode) a graph in a low-dimensional continuous vector space that preserves the most important properties of the graph…
Graph Neighborhood Attentive Pooling
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. Most studies explore the str…