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Found 11 papers, 8 papers with code
p-Norm Flow Diffusion for Local Graph Clustering
Local graph clustering and the closely related seed set expansion problem are primitives on graphs that are central to a wide range of analytic and learning tasks such as local clustering, community detection, nodes ranking and feature inference.
Sequential Recommendation with Latent Relations based on Large Language Model
Different from previous relation-aware models that rely on predefined rules, we propose to leverage the Large Language Model (LLM) to provide new types of relations and connections between items.
Common Sense Enhanced Knowledge-based Recommendation with Large Language Model
Knowledge-based recommendation models effectively alleviate the data sparsity issue leveraging the side information in the knowledge graph, and have achieved considerable performance.
Collaborative Word-based Pre-trained Item Representation for Transferable Recommendation
In this paper, we propose CoWPiRec, an approach of Collaborative Word-based Pre-trained item representation for Recommendation.
Local Graph Clustering with Noisy Labels
We provide sufficient conditions on the label noise under which, with high probability, using diffusion in the weighted graph yields a more accurate recovery of the target cluster.
Polynomial Width is Sufficient for Set Representation with High-dimensional Features
To investigate the minimal value of $L$ that achieves sufficient expressive power, we present two set-element embedding layers: (a) linear + power activation (LP) and (b) linear + exponential activations (LE).
On Classification Thresholds for Graph Attention with Edge Features
In CSBM the nodes and edge features are obtained from a mixture of Gaussians and the edges from a stochastic block model.
Equivariant Hypergraph Diffusion Neural Operators
Hypergraph neural networks (HNNs) using neural networks to encode hypergraphs provide a promising way to model higher-order relations in data and further solve relevant prediction tasks built upon such higher-order relations.
Graph Attention Retrospective
They were introduced to allow a node to aggregate information from features of neighbor nodes in a non-uniform way, in contrast to simple graph convolution which does not distinguish the neighbors of a node.
Local Hyper-Flow Diffusion
Recently, hypergraphs have attracted a lot of attention due to their ability to capture complex relations among entities.
$p$-Norm Flow Diffusion for Local Graph Clustering
Local graph clustering and the closely related seed set expansion problem are primitives on graphs that are central to a wide range of analytic and learning tasks such as local clustering, community detection, nodes ranking and feature inference.
