Graph Mining

39 papers with code • 0 benchmarks • 3 datasets

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Use these libraries to find Graph Mining models and implementations

Most implemented papers

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.

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.

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.

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.

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.

Wikipedia graph mining: dynamic structure of collective memory

mizvol/WikiBrain 1 Oct 2017

The model exploits collective effect of the dynamics to discover events.

Attention Models in Graphs: A Survey

zhliping/Deep-Learning 20 Jul 2018

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.

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.