Graph Mining
70 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
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.
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.
Wikipedia graph mining: dynamic structure of collective memory
The model exploits collective effect of the dynamics to discover events.
Attention Models in Graphs: A Survey
However, in the real-world, graphs can be both large - with many complex patterns - and noisy which can pose a problem for effective graph mining.
Mathematics Content Understanding for Cyberlearning via Formula Evolution Map
Although the scientific digital library is growing at a rapid pace, scholars/students often find reading Science, Technology, Engineering, and Mathematics (STEM) literature daunting, especially for the math-content/formula.
Deep Network Embedding for Graph Representation Learning in Signed Networks
As an effective approach to solve graph mining problems, network embedding aims to learn a low-dimensional feature vector representation for each node of a given network.
Unsupervised Network Embedding for Graph Visualization, Clustering and Classification
In this work we provide an unsupervised approach to learn embedding representation for a collection of graphs so that it can be used in numerous graph mining tasks.
A Multivariate Extreme Value Theory Approach to Anomaly Clustering and Visualization
In a wide variety of situations, anomalies in the behaviour of a complex system, whose health is monitored through the observation of a random vector X = (X1,.
SliceNDice: Mining Suspicious Multi-attribute Entity Groups with Multi-view Graphs
Given the reach of web platforms, bad actors have considerable incentives to manipulate and defraud users at the expense of platform integrity.
AGATHA: Automatic Graph-mining And Transformer based Hypothesis generation Approach
Hypothesis generation systems address this challenge by mining the wealth of publicly available scientific information to predict plausible research directions.
