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Found 23 papers, 13 papers with code
New Perspectives on the Evaluation of Link Prediction Algorithms for Dynamic Graphs
We leverage these visualization tools to investigate the effect of negative sampling on the predictive performance, at the node and edge level.
FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources
In settings such as e-recruitment and online dating, recommendation involves distributing limited opportunities, calling for novel approaches to quantify and enforce fairness.
ReCon: Reducing Congestion in Job Recommendation using Optimal Transport
Recommenders are increasingly used in domains where items have limited availability, such as the job market, where congestion is especially problematic: Recommending a vacancy -- for which typically only one person will be hired -- to a large number of job seekers may lead to frustration for job seekers, as they may be applying for jobs where they are not hired.
Revised Conditional t-SNE: Looking Beyond the Nearest Neighbors
Conditional t-SNE (ct-SNE) is a recent extension to t-SNE that allows removal of known cluster information from the embedding, to obtain a visualization revealing structure beyond label information.
Topologically Regularized Data Embeddings
In some cases, users may have prior topological knowledge about the data, such as a known cluster structure or the fact that the data is known to lie along a tree- or graph-structured topology.
A Systematic Evaluation of Node Embedding Robustness
We find that node classification results are impacted more than network reconstruction ones, that degree-based and label-based attacks are on average the most damaging and that label heterophily can strongly influence attack performance.
A challenge-based survey of e-recruitment recommendation systems
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.
ExClus: Explainable Clustering on Low-dimensional Data Representations
Dimensionality reduction and clustering techniques are frequently used to analyze complex data sets, but their results are often not easy to interpret.
Topologically Regularized Data Embeddings
For tasks for which prior expert topological knowledge is available, incorporating this into the learned representation may lead to higher quality embeddings.
Adversarial Robustness of Probabilistic Network Embedding for Link Prediction
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?
CSNE: Conditional Signed Network Embedding
Experiments on a variety of real-world networks confirm that CSNE outperforms the state-of-the-art on the task of sign prediction.
Benchmarking Network Embedding Models for Link Prediction: Are We Making Progress?
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.
FONDUE: A Framework for Node Disambiguation Using Network Embeddings
Real-world data often presents itself in the form of a network.
Block-Approximated Exponential Random Graphs
An important challenge in the field of exponential random graphs (ERGs) is the fitting of non-trivial ERGs on large graphs.
ALPINE: Active Link Prediction using Network Embedding
Often, the link status of a node pair can be queried, which can be used as additional information by the link prediction algorithm.
Explainable Subgraphs with Surprising Densities: A Subgroup Discovery Approach
The connectivity structure of graphs is typically related to the attributes of the nodes.
Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information
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.
ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions
Networks are powerful data structures, but are challenging to work with for conventional machine learning methods.
EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction
In this paper we present EvalNE, a Python toolbox for evaluating network embedding methods on link prediction tasks.
Social and Information Networks
Conditional Network Embeddings
Network Embeddings (NEs) map the nodes of a given network into $d$-dimensional Euclidean space $\mathbb{R}^d$.
Interactive Visual Data Exploration with Subjective Feedback: An Information-Theoretic Approach
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.
Subjectively Interesting Subgroup Discovery on Real-valued Targets
The subgroup descriptions are in terms of a succinct set of arbitrarily-typed other attributes.
Informative Data Projections: A Framework and Two Examples
Methods for Projection Pursuit aim to facilitate the visual exploration of high-dimensional data by identifying interesting low-dimensional projections.
