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08/18/2023
ReCon: Reducing Congestion in Job Recommendation using Optimal Transport
Recommender systems may suffer from congestion, meaning that there is an...
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02/07/2023
Revised Conditional t-SNE: Looking Beyond the Nearest Neighbors
Conditional t-SNE (ct-SNE) is a recent extension to t-SNE that allows re...
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09/16/2022
A Systematic Evaluation of Node Embedding Robustness
Node embedding methods map network nodes to low dimensional vectors that...
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09/12/2022
A challenge-based survey of e-recruitment recommendation systems
E-recruitment recommendation systems recommend jobs to job seekers and j...
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11/04/2021
ExClus: Explainable Clustering on Low-dimensional Data Representations
Dimensionality reduction and clustering techniques are frequently used t...
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10/18/2021
Topologically Regularized Data Embeddings
Unsupervised feature learning often finds low-dimensional embeddings tha...
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07/05/2021
Adversarial Robustness of Probabilistic Network Embedding for Link Prediction
In today's networked society, many real-world problems can be formalized...
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05/19/2020
CSNE: Conditional Signed Network Embedding
Signed networks are mathematical structures that encode positive and neg...
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02/25/2020
Network Representation Learning for Link Prediction: Are we improving upon simple heuristics?
Network representation learning has become an active research area in re...
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02/24/2020
FONDUE: A Framework for Node Disambiguation Using Network Embeddings
Real-world data often presents itself in the form of a network. Examples...
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02/14/2020
Scalable Dyadic Independence Models with Local and Global Constraints
An important challenge in the field of exponential random graphs (ERGs) ...
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02/04/2020
ALPINE: Active Link Prediction using Network Embedding
Many real-world problems can be formalized as predicting links in a part...
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09/03/2019
Discovering Interesting Cycles in Directed Graphs
Cycles in graphs often signify interesting processes. For example, cycli...
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05/24/2019
Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information
Dimensionality reduction and manifold learning methods such as t-Distrib...
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04/22/2019
ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions
Networks are powerful data structures, but are challenging to work with ...
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05/19/2018
Conditional Network Embeddings
Network embeddings map the nodes of a given network into d-dimensional E...
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10/23/2017
Interactive Visual Data Exploration with Subjective Feedback: An Information-Theoretic Approach
Visual exploration of high-dimensional real-valued datasets is a fundame...
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10/12/2017