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 with no code
Analysis of Insect-Plant Interactions Affected by Mining Operations, A Graph Mining Approach
The decline in ecological connections signifies the potential extinction of species, which can be attributed to disruptions and alterations.
Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications
Model pre-training on large text corpora has been demonstrated effective for various downstream applications in the NLP domain.
GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking
In this study, we conduct an extensive investigation to assess the proficiency of LLMs in comprehending graph data, employing a diverse range of structural and semantic-related tasks.
Graph Mining for Cybersecurity: A Survey
In recent years, with the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance.
Graph Neural Network Surrogates of Fair Graph Filtering
Graph filters that transform prior node values to posterior scores via edge propagation often support graph mining tasks affecting humans, such as recommendation and ranking.
SynGraphy: Succinct Summarisation of Large Networks via Small Synthetic Representative Graphs
In this paper we take the problem of visualising large graphs from a novel perspective: we leave the original graph's nodes and edges behind, and instead summarise its properties such as the clustering coefficient and bipartivity by generating a completely new graph whose structural properties match that of the original graph.
Adaptive Depth Graph Attention Networks
As one of the most popular GNN architectures, the graph attention networks (GAT) is considered the most advanced learning architecture for graph representation and has been widely used in various graph mining tasks with impressive results.
Signed Directed Graph Contrastive Learning with Laplacian Augmentation
To the best of our knowledge, it is the first to introduce magnetic Laplacian perturbation and signed spectral graph contrastive learning.
Trajectory Flow Map: Graph-based Approach to Analysing Temporal Evolution of Aggregated Traffic Flows in Large-scale Urban Networks
First, we partition the entire network into a set of cells based on the spatial distribution of data points in individual trajectories, where the cells represent spatial regions between which aggregated traffic flows can be measured.
Evaluating COVID-19 Sequence Data Using Nearest-Neighbors Based Network Model
Similarly, euclidean space is not considered the best choice when working with the classification and clustering tasks for biological sequences.
