Graph Mining
85 papers with code • 0 benchmarks • 6 datasets
Benchmarks
These leaderboards are used to track progress in Graph Mining
Libraries
Use these libraries to find Graph Mining models and implementationsDatasets
Yelp
Yelp-Fraud
Amazon-Fraud
Netzschleuder
IMCPT-SparseGM-100
Most implemented papers
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters
Finally, the selected neighbors across different relations are aggregated together.
False Information on Web and Social Media: A Survey
False information can be created and spread easily through the web and social media platforms, resulting in widespread real-world impact.
Exploring the Semantic Content of Unsupervised Graph Embeddings: An Empirical Study
sbonner0/unsupervised-graph-embeddings
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To explore this, we present extensive experimental evaluation from five state-of-the-art unsupervised graph embedding techniques, across a range of empirical graph datasets, measuring a selection of topological features.
Pitfalls of Graph Neural Network Evaluation
We perform a thorough empirical evaluation of four prominent GNN models and show that considering different splits of the data leads to dramatically different rankings of models.
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs
We present Karate Club a Python framework combining more than 30 state-of-the-art graph mining algorithms which can solve unsupervised machine learning tasks.
Twitch Gamers: a Dataset for Evaluating Proximity Preserving and Structural Role-based Node Embeddings
Proximity preserving and structural role-based node embeddings have become a prime workhorse of applied graph mining.
Reinforcement learning on graphs: A survey
neunms/reinforcement-learning-on-graph-a-survey
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In this survey, we provide a comprehensive overview of RL and graph mining methods and generalize these methods to Graph Reinforcement Learning (GRL) as a unified formulation.
Fairness in Graph Mining: A Survey
Recently, algorithmic fairness has been extensively studied in graph-based applications.
A Survey on Fairness for Machine Learning on Graphs
In that context, algorithmic contributions for graph mining are not spared by the problem of fairness and present some specific challenges related to the intrinsic nature of graphs: (1) graph data is non-IID, and this assumption may invalidate many existing studies in fair machine learning, (2) suited metric definitions to assess the different types of fairness with relational data and (3) algorithmic challenge on the difficulty of finding a good trade-off between model accuracy and fairness.
All the World's a (Hyper)Graph: A Data Drama
We introduce Hyperbard, a dataset of diverse relational data representations derived from Shakespeare's plays.
