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no code implementations • 16 Sep 2022 • Alexandru Mara, Jefrey Lijffijt, Stephan Günnemann, Tijl De Bie

Node embedding methods map network nodes to low dimensional vectors that can be subsequently used in a variety of downstream prediction tasks.

no code implementations • 12 Sep 2022 • Yoosof Mashayekhi, Nan Li, Bo Kang, Jefrey Lijffijt, Tijl De Bie

The recommendations are generated based on the suitability of the job seekers for the positions as well as the job seekers' and the recruiters' preferences.

no code implementations • 14 Mar 2022 • Raphaël Romero, Bo Kang, Tijl De Bie

Continuous time temporal networks are attracting increasing attention due their omnipresence in real-world datasets and they manifold applications.

1 code implementation • 22 Feb 2022 • Arne Gevaert, Axel-Jan Rousseau, Thijs Becker, Dirk Valkenborg, Tijl De Bie, Yvan Saeys

Feature attribution maps are a popular approach to highlight the most important pixels in an image for a given prediction of a model.

no code implementations • 8 Feb 2022 • Maarten Buyl, Tijl De Bie

In past work on fairness in machine learning, the focus has been on forcing the prediction of classifiers to have similar statistical properties for people of different demographics.

1 code implementation • ICLR 2022 • Robin Vandaele, Bo Kang, Jefrey Lijffijt, Tijl De Bie, Yvan Saeys

For tasks for which prior expert topological knowledge is available, incorporating this into the learned representation may lead to higher quality embeddings.

1 code implementation • 22 Sep 2021 • Robin Vandaele, Bo Kang, Tijl De Bie, Yvan Saeys

Previously, it has been argued that neighborhood queries become meaningless and unstable when distance concentration occurs, which means that there is a poor relative discrimination between the furthest and closest neighbors in the data.

no code implementations • 5 Jul 2021 • Xi Chen, Bo Kang, Jefrey Lijffijt, Tijl De Bie

However, these methods lack transparency when compared to simpler baselines, and as a result their robustness against adversarial attacks is a possible point of concern: could one or a few small adversarial modifications to the network have a large impact on the link prediction performance when using a network embedding model?

no code implementations • 12 May 2021 • Tijl De Bie, Luc De Raedt, José Hernández-Orallo, Holger H. Hoos, Padhraic Smyth, Christopher K. I. Williams

Given the complexity of typical data science projects and the associated demand for human expertise, automation has the potential to transform the data science process.

1 code implementation • 2 Mar 2021 • Maarten Buyl, Tijl De Bie

Given this, we propose a fairness regularizer defined as the KL-divergence between the graph model and its I-projection onto the set of fair models.

2 code implementations • 19 May 2020 • Alexandru Mara, Yoosof Mashayekhi, Jefrey Lijffijt, Tijl De Bie

Experiments on a variety of real-world networks confirm that CSNE outperforms the state-of-the-art on the task of sign prediction.

1 code implementation • ICML 2020 • Maarten Buyl, Tijl De Bie

As machine learning algorithms are increasingly deployed for high-impact automated decision making, ethical and increasingly also legal standards demand that they treat all individuals fairly, without discrimination based on their age, gender, race or other sensitive traits.

2 code implementations • 25 Feb 2020 • Alexandru Mara, Jefrey Lijffijt, Tijl De Bie

Network embedding methods map a network's nodes to vectors in an embedding space, in such a way that these representations are useful for estimating some notion of similarity or proximity between pairs of nodes in the network.

no code implementations • 24 Feb 2020 • Ahmad Mel, Bo Kang, Jefrey Lijffijt, Tijl De Bie

Real-world data often presents itself in the form of a network.

1 code implementation • 14 Feb 2020 • Florian Adriaens, Alexandru Mara, Jefrey Lijffijt, Tijl De Bie

An important challenge in the field of exponential random graphs (ERGs) is the fitting of non-trivial ERGs on large graphs.

no code implementations • 4 Feb 2020 • Xi Chen, Bo Kang, Jefrey Lijffijt, Tijl De Bie

Often, the link status of a node pair can be queried, which can be used as additional information by the link prediction algorithm.

1 code implementation • 10 Jan 2020 • Junning Deng, Bo Kang, Jefrey Lijffijt, Tijl De Bie

The connectivity structure of graphs is typically related to the attributes of the nodes.

1 code implementation • 20 Sep 2019 • Rafael Poyiadzi, Kacper Sokol, Raul Santos-Rodriguez, Tijl De Bie, Peter Flach

First, a counterfactual example generated by the state-of-the-art systems is not necessarily representative of the underlying data distribution, and may therefore prescribe unachievable goals(e. g., an unsuccessful life insurance applicant with severe disability may be advised to do more sports).

no code implementations • 24 May 2019 • Bo Kang, Darío García García, Jefrey Lijffijt, Raúl Santos-Rodríguez, Tijl De Bie

Dimensionality reduction and manifold learning methods such as t-Distributed Stochastic Neighbor Embedding (t-SNE) are routinely used to map high-dimensional data into a 2-dimensional space to visualize and explore the data.

no code implementations • 22 Apr 2019 • Bo Kang, Jefrey Lijffijt, Tijl De Bie

Networks are powerful data structures, but are challenging to work with for conventional machine learning methods.

1 code implementation • 22 Jan 2019 • Alexandru Mara, Jefrey Lijffijt, Tijl De Bie

In this paper we present EvalNE, a Python toolbox for evaluating network embedding methods on link prediction tasks.

Social and Information Networks

no code implementations • ICLR 2019 • Bo Kang, Jefrey Lijffijt, Tijl De Bie

Network Embeddings (NEs) map the nodes of a given network into $d$-dimensional Euclidean space $\mathbb{R}^d$.

1 code implementation • 23 Oct 2017 • Kai Puolamäki, Emilia Oikarinen, Bo Kang, Jefrey Lijffijt, Tijl De Bie

We conclude that the information theoretic approach to exploratory data analysis where patterns observed by a user are formalized as constraints provides a principled, intuitive, and efficient basis for constructing an EDA system.

no code implementations • 12 Oct 2017 • Jefrey Lijffijt, Bo Kang, Wouter Duivesteijn, Kai Puolamäki, Emilia Oikarinen, Tijl De Bie

The subgroup descriptions are in terms of a succinct set of arbitrarily-typed other attributes.

no code implementations • 27 Nov 2015 • Tijl De Bie, Jefrey Lijffijt, Raul Santos-Rodriguez, Bo Kang

Methods for Projection Pursuit aim to facilitate the visual exploration of high-dimensional data by identifying interesting low-dimensional projections.

no code implementations • TBD 2011 • Yizhao Ni, Matt Mcvicar, Raul Santos-Rodriguez, Tijl De Bie

We present a new approach to evaluate chord recognition systems on songs which do not have full annotations.

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