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
Graph-Skeleton: ~1% Nodes are Sufficient to Represent Billion-Scale Graph
In this paper, we argue that properly fetching and condensing the background nodes from massive web graph data might be a more economical shortcut to tackle the obstacles fundamentally.
Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns
zjunet/G-Tuning
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In this paper, we identify the fundamental cause of structural divergence as the discrepancy of generative patterns between the pre-training and downstream graphs.
DGCLUSTER: A Neural Framework for Attributed Graph Clustering via Modularity Maximization
Graph clustering is a fundamental and challenging task in the field of graph mining where the objective is to group the nodes into clusters taking into consideration the topology of the graph.
Survey on Trustworthy Graph Neural Networks: From A Causal Perspective
Moreover, we introduce a taxonomy of Causality-Inspired GNNs (CIGNNs) based on the type of causal learning capability they are equipped with, i. e., causal reasoning and causal representation learning.
User Modeling in the Era of Large Language Models: Current Research and Future Directions
Two common types of user data are text and graph, as the data usually contain a large amount of user-generated content (UGC) and online interactions.
Fast Graph Condensation with Structure-based Neural Tangent Kernel
The rapid development of Internet technology has given rise to a vast amount of graph-structured data.
Independent Distribution Regularization for Private Graph Embedding
hkust-knowcomp/privategraphencoder
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Additionally, we introduce a novel regularization to enforce the independence of the encoders.
Contrastive Meta-Learning for Few-shot Node Classification
songw-sw/cosmic
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First, we propose to enhance the intra-class generalizability by involving a contrastive two-step optimization in each episode to explicitly align node embeddings in the same classes.
Fast Maximum $k$-Plex Algorithms Parameterized by Small Degeneracy Gaps
We define a novel parameter of the input instance, $g_k(G)$, the gap between the degeneracy bound and the size of the maximum $k$-plex in the given graph, and present an exact algorithm parameterized by this $g_k(G)$, which has a worst-case running time polynomial in the size of the input graph and exponential in $g_k(G)$.
Connecting the Dots: What Graph-Based Text Representations Work Best for Text Classification Using Graph Neural Networks?
Given the success of Graph Neural Networks (GNNs) for structure-aware machine learning, many studies have explored their use for text classification, but mostly in specific domains with limited data characteristics.
