graphs-5

Graph Mining

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in graph-mining-5

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Dataset

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Libraries

i

Use these libraries to find graph-mining-5 models and implementations

benedekrozemberczki/karateclub

2 papers 2,084

Datasets

NBA player performance prediction dataset

IMCPT-SparseGM-100

Subtasks

No subtasks available.

Most implemented papers

Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters

Philip S. Yu, Yingtong Dou, Zhiwei Liu, Li Sun, Yutong Deng, Hao Peng•Tue Aug 18 2020

This paper introduces two types of camouflages based on recent empirical studies, i.e., the feature camouflage and the relation camouflage and proposes a new model named CAmouflage-REsistant GNN (CARE-GNN), to enhance the GNN aggregation process with three unique modules against camouflages.

611

Content

0

Paper Graph

False Information on Web and Social Media: A Survey

Srijan Kumar, Neil Shah•Sun Apr 22 2018

A comprehensive survey spanning diverse aspects of false information is presented, namely the actors involved in spreading false information, rationale behind successfully deceiving readers, quantifying the impact offalse information, and algorithms developed to detect false information.

387 0

Paper Graph

Exploring the Semantic Content of Unsupervised Graph Embeddings: An Empirical Study

Stephen Bonner, Ibad Kureshi, John Brennan, G. Theodoropoulos, A. Mcgough, B. Obara•Mon Jun 18 2018

This paper investigates if graph embeddings are approximating something analogous to traditional vertex-level graph features, and demonstrates that several topological features are indeed being approximated in the embedding space, allowing key insight into how graph embeds create good representations.

35 0

Paper Graph

Pitfalls of Graph Neural Network Evaluation

Stephan Günnemann, Aleksandar Bojchevski, Oleksandr Shchur, Maximilian Mumme•Tue Nov 13 2018

This paper performs a thorough empirical evaluation of four prominent GNN models and suggests that simpler GNN architectures are able to outperform the more sophisticated ones if the hyperparameters and the training procedure are tuned fairly for all models.

1642 0

Paper Graph

Karate Club: An API Oriented Open-Source Python Framework for Unsupervised Learning on Graphs

Benedek Rozemberczki, Oliver Kiss, Rik Sarkar•Mon Mar 09 2020

Karate Club - a Python framework combining more than 30 state-of-the-art graph mining algorithms that make it easy to identify and represent common graph features, and its efficiency in learning performance on a wide range of real world clustering problems and classification tasks is shown.

204 0

Paper Graph

Interactive Text Graph Mining with a Prolog-Based Dialog Engine

Paul Tarau, Eduardo Blanco•Sun Jan 19 2020

A Prolog-based dialog engine that explores interactively a ranked fact database extracted from a text document and reorganizes dependency graphs to focus on the most relevant content elements of a sentence and integrate sentence identifiers as graph nodes is designed.

4 0

Paper Graph

Twitch Gamers: a Dataset for Evaluating Proximity Preserving and Structural Role-based Node Embeddings

Benedek Rozemberczki, Rik Sarkar•Thu Jan 07 2021

Descriptive analysis of the social network and node classification experiments illustrate that Twitch Gamers is suitable for assessing the predictive performance of novel proximity-preserving and structural role-based node embedding algorithms.

88 0

Paper Graph

Reinforcement Learning on Graphs: A Survey

Mingshuo Nie, Dongming Chen, Dongqi Wang•Tue Apr 12 2022

This survey provides a comprehensive overview of these fusion works to Graph Reinforcement Learning (GRL) as a unified formulation and creates a collection of papers for both interested scholars who want to enter this rapidly developing domain and experts who would like to compare GRL methods.

74 0

Paper Graph

Fairness in Graph Mining: A Survey

Song Wang, Yushun Dong, Jing Ma, Chen Chen, Jundong Li•Wed Apr 20 2022

A novel taxonomy of fairness notions on graphs is proposed, which sheds light on their connections and differences, and an organized summary of existing techniques that promote fairness in graph mining is presented.

162 0

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A Survey on Fairness for Machine Learning on Graphs

Manvi Choudhary, Charlotte Laclau, C. Largeron•Tue May 10 2022

This survey is the first one dedicated to fairness for relational data and provides a comprehensive overview of recent contributions in the domain of fair machine learning for graphs, that is classify into pre-processing, in-processing and post-processing models.

33 0

Paper Graph

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