119 citations · 121 across the 12 of their papers we have counts for
13 papers
Graph Reduction in Multirelational Networks: A Spreading-Oriented Reduction Benchmark
Mateusz Stolarski, Michał Czuba, Piotr Bielak +1
Real-world networks are inherently incomplete, noisy, and dynamically evolving, making it difficult to capture all actors and their relationships. Their scale often renders direct…
Towards Graph Foundation Models for Dynamics in Complex Networked Systems: Lessons from Super-Spreader Identification in Multilayer Networks
Michał Czuba, Mateusz Stolarski, Adam Piróg +2
Network dynamics - including spreading, influence maximisation, and epidemic modelling - remain largely confined to the transductive paradigm, where models are trained on a single…
KG-Guard: Graph-Based Hallucination Detection for Knowledge Base Question Answering
Albert Sawczyn, Piotr Bielak, Tomasz Kajdanowicz
Large language models (LLMs) are increasingly used for knowledge base question answering (KBQA), where answering requires selecting entities from a question-specific knowledge-grap…
Identifying Super Spreaders in Multilayer Networks
Michał Czuba, Mateusz Stolarski, Adam Piróg +2
Identifying super-spreaders can be framed as a subtask of the influence maximisation problem. It seeks to pinpoint agents within a network that, if selected as single diffusion see…
Empowering Small-Scale Knowledge Graphs: A Strategy of Leveraging General-Purpose Knowledge Graphs for Enriched Embeddings
Albert Sawczyn, Jakub Binkowski, Piotr Bielak +1
Knowledge-intensive tasks pose a significant challenge for Machine Learning (ML) techniques. Commonly adopted methods, such as Large Language Models (LLMs), often exhibit limitatio…
Representation learning in multiplex graphs: Where and how to fuse information?
Piotr Bielak, Tomasz Kajdanowicz
In recent years, unsupervised and self-supervised graph representation learning has gained popularity in the research community. However, most proposed methods are focused on homog…
Unveiling the Potential of Probabilistic Embeddings in Self-Supervised Learning
Denis Janiak, Jakub Binkowski, Piotr Bielak +1
In recent years, self-supervised learning has played a pivotal role in advancing machine learning by allowing models to acquire meaningful representations from unlabeled data. An i…
Graph-level representations using ensemble-based readout functions
Jakub Binkowski, Albert Sawczyn, Denis Janiak +2
Graph machine learning models have been successfully deployed in a variety of application areas. One of the most prominent types of models - Graph Neural Networks (GNNs) - provides…
RAFEN -- Regularized Alignment Framework for Embeddings of Nodes
Kamil Tagowski, Piotr Bielak, Jakub Binkowski +1
Learning representations of nodes has been a crucial area of the graph machine learning research area. A well-defined node embedding model should reflect both node features and the…
On Graph Neural Network Ensembles for Large-Scale Molecular Property Prediction
Edward Elson Kosasih, Joaquin Cabezas, Xavier Sumba +5
In order to advance large-scale graph machine learning, the Open Graph Benchmark Large Scale Challenge (OGB-LSC) was proposed at the KDD Cup 2021. The PCQM4M-LSC dataset defines a…
Graph Barlow Twins: A self-supervised representation learning framework for graphs
Piotr Bielak, Tomasz Kajdanowicz, Nitesh V. Chawla
The self-supervised learning (SSL) paradigm is an essential exploration area, which tries to eliminate the need for expensive data labeling. Despite the great success of SSL method…
AttrE2vec: Unsupervised Attributed Edge Representation Learning
Piotr Bielak, Tomasz Kajdanowicz, Nitesh V. Chawla
Representation learning has overcome the often arduous and manual featurization of networks through (unsupervised) feature learning as it results in embeddings that can apply to a…
FILDNE: A Framework for Incremental Learning of Dynamic Networks Embeddings
Piotr Bielak, Kamil Tagowski, Maciej Falkiewicz +2
Representation learning on graphs has emerged as a powerful mechanism to automate feature vector generation for downstream machine learning tasks. The advances in representation on…