25 citations · 147 across the 36 of their papers we have counts for
6 papers · 1 filter
Discovering Interesting Cycles in Directed Graphs
Florian Adriaens, Cigdem Aslay, Tijl De Bie +2
Cycles in graphs often signify interesting processes. For example, cyclic trading patterns can indicate inefficiencies or economic dependencies in trade networks, cycles in food we…
Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information
Bo Kang, Darío García García, Jefrey Lijffijt +2
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-dimensio…
ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions
Bo Kang, Jefrey Lijffijt, Tijl De Bie
Networks are powerful data structures, but are challenging to work with for conventional machine learning methods. Network Embedding (NE) methods attempt to resolve this by learnin…
Mining Subjectively Interesting Attributed Subgraphs
Anes Bendimerad, Ahmad Mel, Jefrey Lijffijt +3
Community detection in graphs, data clustering, and local pattern mining are three mature fields of data mining and machine learning. In recent years, attributed subgraph mining is…
Opinion Dynamics with Backfire Effect and Biased Assimilation
Xi Chen, Panayiotis Tsaparas, Jefrey Lijffijt +1
The democratization of AI tools for content generation, combined with unrestricted access to mass media for all (e.g. through microblogging and social media), makes it increasingly…
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…