38 citations · 98 across the 11 of their papers we have counts for
13 papers
STEP: Career-Path Recommendation via Temporal and Educational Trajectory Modeling
Iman Johary, Guillaume Bied, Alexandru C. Mara +1
Career paths encode decades of skill acquisition, role transitions, and educational investment, and understanding them at scale underpins workforce planning, labor market policy, a…
JobHop v2: A Large-Scale Career Trajectory Dataset from Unstructured Resumes
Iman Johary, Guillaume Bied, Alexandru C. Mara +1
Large-scale, richly annotated career trajectory data underpins workforce planning, job recommendation, and labour market analysis, yet publicly available datasets are either small,…
JobHop: A Large-Scale Dataset of Career Trajectories
Iman Johary, Raphael Romero, Alexandru C. Mara +1
Understanding labor market dynamics is essential for policymakers, employers, and job seekers. However, comprehensive datasets that capture real-world career trajectories are scarc…
Large Language Models Reflect the Ideology of their Creators
Maarten Buyl, Alexander Rogiers, Sander Noels +8
Large language models (LLMs) are trained on vast amounts of data to generate natural language, enabling them to perform tasks like text summarization and question answering. These…
A Systematic Evaluation of Node Embedding Robustness
Alexandru Mara, Jefrey Lijffijt, Stephan Günnemann +1
Node embedding methods map network nodes to low dimensional vectors that can be subsequently used in a variety of downstream prediction tasks. The popularity of these methods has g…
CSNE: Conditional Signed Network Embedding
Alexandru Mara, Yoosof Mashayekhi, Jefrey Lijffijt +1
Signed networks are mathematical structures that encode positive and negative relations between entities such as friend/foe or trust/distrust. Recently, several papers studied the…
Benchmarking Network Embedding Models for Link Prediction: Are We Making Progress?
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 pr…
Block-Approximated Exponential Random Graphs
Florian Adriaens, Alexandru Mara, Jefrey Lijffijt +1
An important challenge in the field of exponential random graphs (ERGs) is the fitting of non-trivial ERGs on large graphs. By utilizing fast matrix block-approximation techniques,…
EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction
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. Link prediction is one of the most popular choices for evaluati…
Semi-supervised Learning in Network-Structured Data via Total Variation Minimization
Alexander Jung, Alfred O. Hero, Alexandru Mara +3
We propose and analyze a method for semi-supervised learning from partially-labeled network-structured data. Our approach is based on a graph signal recovery interpretation under a…
Recovery Conditions and Sampling Strategies for Network Lasso
Alexandru Mara, Alexander Jung
The network Lasso is a recently proposed convex optimization method for machine learning from massive network structured datasets, i.e., big data over networks. It is a variant of…
When is Network Lasso Accurate?
Alexander Jung, Nguyen Tran Quang, Alexandru Mara
The "least absolute shrinkage and selection operator" (Lasso) method has been adapted recently for networkstructured datasets. In particular, this network Lasso method allows to le…
Scalable Semi-Supervised Learning over Networks using Nonsmooth Convex Optimization
Alexander Jung, Alfred O. Hero, Alexandru Mara +1
We propose a scalable method for semi-supervised (transductive) learning from massive network-structured datasets. Our approach to semi-supervised learning is based on representing…