Node embedding methods map network nodes to low dimensional vectors that can be subsequently used in a variety of downstream prediction tasks.
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.
Continuous time temporal networks are attracting increasing attention due their omnipresence in real-world datasets and they manifold applications.
Feature attribution maps are a popular approach to highlight the most important pixels in an image for a given prediction of a model.
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.
For tasks for which prior expert topological knowledge is available, incorporating this into the learned representation may lead to higher quality embeddings.
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.
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?
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.
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.
Experiments on a variety of real-world networks confirm that CSNE outperforms the state-of-the-art on the task of sign prediction.
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.
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.
Often, the link status of a node pair can be queried, which can be used as additional information by the link prediction algorithm.
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).
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.
Networks are powerful data structures, but are challenging to work with for conventional machine learning methods.
In this paper we present EvalNE, a Python toolbox for evaluating network embedding methods on link prediction tasks.
Social and Information Networks
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.
The subgroup descriptions are in terms of a succinct set of arbitrarily-typed other attributes.
Methods for Projection Pursuit aim to facilitate the visual exploration of high-dimensional data by identifying interesting low-dimensional projections.