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
Latest papers
PGB: A PubMed Graph Benchmark for Heterogeneous Network Representation Learning
Although graph mining research via heterogeneous graph neural networks has taken center stage, it remains unclear whether these approaches capture the heterogeneity of the PubMed database, a vast digital repository containing over 33 million articles.
Transition Propagation Graph Neural Networks for Temporal Networks
The proposed TIP-GNN focuses on the bilevel graph structure in temporal networks: besides the explicit interaction graph, a node's sequential interactions can also be constructed as a transition graph.
GAT-COBO: Cost-Sensitive Graph Neural Network for Telecom Fraud Detection
xxhu94/gat-cobo
•
•
Extensive experiments on two real-world telecom fraud detection datasets demonstrate that our proposed method is effective for the graph imbalance problem, outperforming the state-of-the-art GNNs and GNN-based fraud detectors.
NetEffect: Discovery and Exploitation of Generalized Network Effects
Given a large graph with few node labels, how can we (a) identify whether there is generalized network-effects (GNE) or not, (b) estimate GNE to explain the interrelations among node classes, and (c) exploit GNE efficiently to improve the performance on downstream tasks?
Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphs
rmanluo/gsnop
•
•
Specifically, GSNOP combines the advantage of the neural process and neural ordinary differential equation that models the link prediction on dynamic graphs as a dynamic-changing stochastic process.
Code Recommendation for Open Source Software Developers
Ahren09/CODER
•
•
In this paper, we formulate the novel problem of code recommendation, whose purpose is to predict the future contribution behaviors of developers given their interaction history, the semantic features of source code, and the hierarchical file structures of projects.
Less is More: SlimG for Accurate, Robust, and Interpretable Graph Mining
Graph neural networks (GNNs) have succeeded in many graph mining tasks, but their generalizability to various graph scenarios is limited due to the difficulty of training, hyperparameter tuning, and the selection of a model itself.
FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs
fedego/fedego
•
•
As special information carriers containing both structure and feature information, graphs are widely used in graph mining, e. g., Graph Neural Networks (GNNs).
MentorGNN: Deriving Curriculum for Pre-Training GNNs
To comprehend heterogeneous graph signals at different granularities, we propose a curriculum learning paradigm that automatically re-weighs graph signals in order to ensure a good generalization in the target domain.
From Time Series to Networks in R with the ts2net Package
Ts2net also provides methods to transform a single time series into a network, such as recurrence networks, visibility graphs, and transition networks.
