Search Results for author: Shenghao Yang

Found 11 papers, 8 papers with code

p-Norm Flow Diffusion for Local Graph Clustering

1 code implementation ICML 2020 Kimon Fountoulakis, Di Wang, Shenghao Yang

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.

Clustering Community Detection +1

Sequential Recommendation with Latent Relations based on Large Language Model

1 code implementation27 Mar 2024 Shenghao Yang, Weizhi Ma, Peijie Sun, Qingyao Ai, Yiqun Liu, Mingchen Cai, Min Zhang

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

1 code implementation27 Mar 2024 Shenghao Yang, Weizhi Ma, Peijie Sun, Min Zhang, Qingyao Ai, Yiqun Liu, Mingchen Cai

Knowledge-based recommendation models effectively alleviate the data sparsity issue leveraging the side information in the knowledge graph, and have achieved considerable performance.

Local Graph Clustering with Noisy Labels

no code implementations12 Oct 2023 Artur Back de Luca, Kimon Fountoulakis, Shenghao Yang

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.

Clustering Graph Clustering

Polynomial Width is Sufficient for Set Representation with High-dimensional Features

no code implementations8 Jul 2023 Peihao Wang, Shenghao Yang, Shu Li, Zhangyang Wang, Pan Li

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).

Inductive Bias

On Classification Thresholds for Graph Attention with Edge Features

no code implementations18 Oct 2022 Kimon Fountoulakis, Dake He, Silvio Lattanzi, Bryan Perozzi, Anton Tsitsulin, Shenghao Yang

In CSBM the nodes and edge features are obtained from a mixture of Gaussians and the edges from a stochastic block model.

Classification Graph Attention +2

Equivariant Hypergraph Diffusion Neural Operators

1 code implementation14 Jul 2022 Peihao Wang, Shenghao Yang, Yunyu Liu, Zhangyang Wang, Pan Li

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.

Computational Efficiency Node Classification

Graph Attention Retrospective

1 code implementation26 Feb 2022 Kimon Fountoulakis, Amit Levi, Shenghao Yang, Aseem Baranwal, Aukosh Jagannath

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.

Graph Attention Node Classification +1

Local Hyper-Flow Diffusion

1 code implementation NeurIPS 2021 Kimon Fountoulakis, Pan Li, Shenghao Yang

Recently, hypergraphs have attracted a lot of attention due to their ability to capture complex relations among entities.

Clustering Community Detection +1

$p$-Norm Flow Diffusion for Local Graph Clustering

2 code implementations20 May 2020 Kimon Fountoulakis, Di Wang, Shenghao Yang

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.

Clustering Community Detection +1

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