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Graph Mining

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/leo02016/mentorgnn

공식 코드

MentorGNN: Deriving Curriculum for Pre-Training GNNs

https://github.com/leoguti85/GraphEmbs

공식 코드

Unsupervised Network Embedding for Graph Visualization, Clustering and Classification

https://github.com/lnferreira/ts2net

공식 코드

From Time Series to Networks in R with the ts2net Package

https://github.com/manvic14/survey_fairness_graphs

공식 코드

A Survey on Fairness for Machine Learning on Graphs

https://github.com/mchiapino/mevt_anomaly

공식 코드

A Multivariate Extreme Value Theory Approach to Anomaly Clustering and Visualization

https://github.com/mengchillee/SlimG

공식 코드

Less is More: SlimG for Accurate, Robust, and Interpretable Graph Mining

https://github.com/mengchillee/neteffect

공식 코드

NetEffect: Discovery and Exploitation of Generalized Network Effects

https://github.com/minjiyoon/ICDM20-AutoGM

공식 코드

Autonomous Graph Mining Algorithm Search with Best Speed/Accuracy Trade-off

https://github.com/mizvol/WikiBrain

공식 코드

Wikipedia graph mining: dynamic structure of collective memory

https://github.com/mtang724/nwr-gae

공식 코드

Graph Auto-Encoder Via Neighborhood Wasserstein Reconstruction

https://github.com/nbalepur/mcqa-artifacts

공식 코드

Is Your Large Language Model Knowledgeable or a Choices-Only Cheater?

https://github.com/netalsgroup/gapa

공식 코드

Efficient Parallel Genetic Algorithm for Perturbed Substructure Optimization in Complex Network

https://github.com/neunms/reinforcement-learning-on-graph-a-survey

공식 코드

Reinforcement learning on graphs: A survey

https://github.com/neunms/reinforcement-learning-on-graphs-a-survey

공식 코드

Reinforcement learning on graphs: A survey

https://github.com/niuchh/smp

공식 코드

Permutation-equivariant and Proximity-aware Graph Neural Networks with Stochastic Message Passing

https://github.com/nveldt/GenMeanDSG

공식 코드

The Generalized Mean Densest Subgraph Problem

https://github.com/pcy1302/asp2vec

공식 코드

Unsupervised Differentiable Multi-aspect Network Embedding

https://github.com/peng-yiwen/WiKC

공식 코드

Refining Wikidata Taxonomy using Large Language Models

https://github.com/pku-dair/air

공식 코드

Model Degradation Hinders Deep Graph Neural Networks

https://github.com/ptarau/DeepRank

공식 코드

Interactive Text Graph Mining with a Prolog-based Dialog Engine

https://github.com/pyjhzwh/teacups_icdm

공식 코드

Accurate and Fast Estimation of Temporal Motifs using Path Sampling

https://github.com/pyrobits/dgcluster

공식 코드

DGCLUSTER: A Neural Framework for Attributed Graph Clustering via Modularity Maximization

https://github.com/rmanluo/gsnop

공식 코드

Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphs

https://github.com/safe-graph/DGFraud

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Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters

https://github.com/sawlani/a-doge

공식 코드

Fast Attributed Graph Embedding via Density of States

https://github.com/sbonner0/unsupervised-graph-embedding

공식 코드

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

https://github.com/sbonner0/unsupervised-graph-embeddings

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

https://github.com/shenxiaocam/Deep-network-embedding-for-graph-representation-learning-in-signed-networks

공식 코드

Deep Network Embedding for Graph Representation Learning in Signed Networks

https://github.com/songw-sw/cosmic

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Contrastive Meta-Learning for Few-shot Node Classification

https://github.com/songw-sw/tent

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Task-Adaptive Few-shot Node Classification

https://github.com/spacelearner/graph-gpt

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GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

https://github.com/tamsiuhin/llm-um-reading

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User Modeling in the Era of Large Language Models: Current Research and Future Directions

https://github.com/usail-hkust/causality-inspired-gnns

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When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook

https://github.com/wanglin0126/gcsntk

공식 코드

Fast Graph Condensation with Structure-based Neural Tangent Kernel

https://github.com/xxhu94/gat-cobo

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GAT-COBO: Cost-Sensitive Graph Neural Network for Telecom Fraud Detection

https://github.com/yihongt/dastnet

공식 코드

Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.