activity
20182021
most citedExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions

12 citations · 17 across the 7 of their papers we have counts for

collaborators

12 papers

cs.LG2021

ExClus: Explainable Clustering on Low-dimensional Data Representations

Xander Vankwikelberge, Bo Kang, Edith Heiter +1

Dimensionality reduction and clustering techniques are frequently used to analyze complex data sets, but their results are often not easy to interpret. We consider how to support u…

cs.SI2021

Adversarial Robustness of Probabilistic Network Embedding for Link Prediction

Xi Chen, Bo Kang, Jefrey Lijffijt +1

In today's networked society, many real-world problems can be formalized as predicting links in networks, such as Facebook friendship suggestions, e-commerce recommendations, and t…

cs.SI2020

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…

cs.LG2020

FONDUE: A Framework for Node Disambiguation Using Network Embeddings

Ahmad Mel, Bo Kang, Jefrey Lijffijt +1

Real-world data often presents itself in the form of a network. Examples include social networks, citation networks, biological networks, and knowledge graphs. In their simplest fo…

cs.LG20202 cited

ALPINE: Active Link Prediction using Network Embedding

Xi Chen, Bo Kang, Jefrey Lijffijt +1

Many real-world problems can be formalized as predicting links in a partially observed network. Examples include Facebook friendship suggestions, consumer-product recommendations,…

cs.SI2020

Explainable Subgraphs with Surprising Densities: A Subgroup Discovery Approach

Junning Deng, Bo Kang, Jefrey Lijffijt +1

The connectivity structure of graphs is typically related to the attributes of the nodes. In social networks for example, the probability of a friendship between two people depends…