Graph Mining
62 papers with code • 0 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in Graph Mining
Libraries
Use these libraries to find Graph Mining models and implementationsDatasets
Yelp-Fraud
Amazon-Fraud
Netzschleuder
NBA player performance prediction dataset
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.
Interactive Text Graph Mining with a Prolog-based Dialog Engine
Working on the Prolog facts and their inferred consequences, the dialog engine specializes the text graph with respect to a query and reveals interactively the document's most relevant content elements.
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.
High Quality, Scalable and Parallel Community Detectionfor Large Real Graphs
However, existing algorithms are, in general, based on complex and expensive computations, making them unsuitable for large graphs with millions of vertices and edges such as those usually found in the real world.
