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
Most implemented papers
Peregrine: A Pattern-Aware Graph Mining System
General purpose graph mining systems provide a generic runtime to explore subgraph structures of interest with the help of user-defined functions that guide the overall exploration process.
Learning Opinion Dynamics From Social Traces
In this work we propose an inference mechanism for fitting a generative, agent-like model of opinion dynamics to real-world social traces.
Persona2vec: A Flexible Multi-role Representations Learning Framework for Graphs
Graph embedding techniques, which learn low-dimensional representations of a graph, are achieving state-of-the-art performance in many graph mining tasks.
Unsupervised Differentiable Multi-aspect Network Embedding
pcy1302/asp2vec
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To capture the multiple aspects of each node, existing studies mainly rely on offline graph clustering performed prior to the actual embedding, which results in the cluster membership of each node (i. e., node aspect distribution) fixed throughout training of the embedding model.
Adversarial Directed Graph Embedding
To capture the directed edges between nodes, existing methods mostly learn two embedding vectors for each node, source vector and target vector.
Permutation-equivariant and Proximity-aware Graph Neural Networks with Stochastic Message Passing
niuchh/smp
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Graph neural networks (GNNs) are emerging machine learning models on graphs.
Autonomous Graph Mining Algorithm Search with Best Speed/Accuracy Trade-off
We first define a unified framework UNIFIEDGM that integrates various message-passing based graph algorithms, ranging from conventional algorithms like PageRank to graph neural networks.
Accelerating COVID-19 research with graph mining and transformer-based learning
In 2020, the White House released the, "Call to Action to the Tech Community on New Machine Readable COVID-19 Dataset," wherein artificial intelligence experts are asked to collect data and develop text mining techniques that can help the science community answer high-priority scientific questions related to COVID-19.
Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels
yimiachack/gskn
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However, the inability of GNNs to model substructures in graphs remains a significant drawback.
Identity Inference on Blockchain using Graph Neural Network
jjzhou012/I2BGNN
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In this paper, we present a novel approach to analyze user's behavior from the perspective of the transaction subgraph, which naturally transforms the identity inference task into a graph classification pattern and effectively avoids computation in large-scale graph.
