Graph Mining

62 papers with code • 0 benchmarks • 5 datasets

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Use these libraries to find Graph Mining models and implementations

Most implemented papers

Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters

safe-graph/DGFraud 19 Aug 2020

Finally, the selected neighbors across different relations are aggregated together.

False Information on Web and Social Media: A Survey

bwoodhamilton/client_project_group_3 23 Apr 2018

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 19 Jun 2018

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

shchur/gnn-benchmark 14 Nov 2018

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

benedekrozemberczki/karateclub CIKM 2020

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

yuce/pyswip 31 Jul 2020

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

benedekrozemberczki/datasets 8 Jan 2021

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 13 Apr 2022

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

yushundong/pygdebias 21 Apr 2022

Recently, algorithmic fairness has been extensively studied in graph-based applications.

High Quality, Scalable and Parallel Community Detectionfor Large Real Graphs

benedekrozemberczki/karateclub WWW 2014

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.