Search Results for author: Menglin Yang

Found 18 papers, 12 papers with code

Hyperbolic Fine-tuning for Large Language Models

1 code implementation5 Oct 2024 Menglin Yang, Aosong Feng, Bo Xiong, Jihong Liu, Irwin King, Rex Ying

Through extensive experiments, we demonstrate that HypLoRA significantly enhances the performance of LLMs on reasoning tasks, particularly for complex reasoning problems.

Foundations and Frontiers of Graph Learning Theory

no code implementations3 Jul 2024 Yu Huang, Min Zhou, Menglin Yang, Zhen Wang, Muhan Zhang, Jie Wang, Hong Xie, Hao Wang, Defu Lian, Enhong Chen

Recent advancements in graph learning have revolutionized the way to understand and analyze data with complex structures.

Graph Learning Learning Theory

Hypformer: Exploring Efficient Hyperbolic Transformer Fully in Hyperbolic Space

2 code implementations1 Jul 2024 Menglin Yang, Harshit Verma, Delvin Ce Zhang, Jiahong Liu, Irwin King, Rex Ying

Our experimental results confirm the effectiveness and efficiency of Hypformer across various datasets, demonstrating its potential as an effective and scalable solution for large-scale data representation and large models.

DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphs

1 code implementation17 Jun 2024 Jiasheng Zhang, Jialin Chen, Menglin Yang, Aosong Feng, Shuang Liang, Jie Shao, Rex Ying

Moreover, we conduct extensive benchmark experiments on DTGB, evaluating 7 popular dynamic graph learning algorithms and their variants of adapting to text attributes with LLM embeddings, along with 6 powerful large language models (LLMs).

Dynamic graph embedding Edge Classification +4

Hyperbolic Representation Learning: Revisiting and Advancing

1 code implementation15 Jun 2023 Menglin Yang, Min Zhou, Rex Ying, Yankai Chen, Irwin King

To address this, we propose a simple yet effective method, hyperbolic informed embedding (HIE), by incorporating cost-free hierarchical information deduced from the hyperbolic distance of the node to origin (i. e., induced hyperbolic norm) to advance existing \hlms.

Representation Learning

kHGCN: Tree-likeness Modeling via Continuous and Discrete Curvature Learning

1 code implementation4 Dec 2022 Menglin Yang, Min Zhou, Lujia Pan, Irwin King

The prevalence of tree-like structures, encompassing hierarchical structures and power law distributions, exists extensively in real-world applications, including recommendation systems, ecosystems, financial networks, social networks, etc.

Link Prediction Node Classification +2

Hyperbolic Graph Representation Learning: A Tutorial

no code implementations8 Nov 2022 Min Zhou, Menglin Yang, Lujia Pan, Irwin King

We first give a brief introduction to graph representation learning as well as some preliminary Riemannian and hyperbolic geometry.

Graph Learning Graph Representation Learning +2

HICF: Hyperbolic Informative Collaborative Filtering

1 code implementation19 Jul 2022 Menglin Yang, Zhihao LI, Min Zhou, Jiahong Liu, Irwin King

The results reveal that (1) tail items get more emphasis in hyperbolic space than that in Euclidean space, but there is still ample room for improvement; (2) head items receive modest attention in hyperbolic space, which could be considerably improved; (3) and nonetheless, the hyperbolic models show more competitive performance than Euclidean models.

Collaborative Filtering Recommendation Systems

Discovering Representative Attribute-stars via Minimum Description Length

no code implementations27 Apr 2022 Jiahong Liu, Min Zhou, Philippe Fournier-Viger, Menglin Yang, Lujia Pan, Mourad Nouioua

However, there are generally two limitations that hinder their practical use: (1) they have multiple parameters that are hard to set but greatly influence results, (2) and they generally focus on identifying complex subgraphs while ignoring relationships between attributes of nodes. Graphs are a popular data type found in many domains.

Attribute Decision Making

BSAL: A Framework of Bi-component Structure and Attribute Learning for Link Prediction

1 code implementation18 Apr 2022 Bisheng Li, Min Zhou, Shengzhong Zhang, Menglin Yang, Defu Lian, Zengfeng Huang

Regarding missing link inference of diverse networks, we revisit the link prediction techniques and identify the importance of both the structural and attribute information.

Attribute Graph Classification +2

HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularization

1 code implementation18 Apr 2022 Menglin Yang, Min Zhou, Jiahong Liu, Defu Lian, Irwin King

Hyperbolic space offers a spacious room to learn embeddings with its negative curvature and metric properties, which can well fit data with tree-like structures.

Collaborative Filtering Recommendation Systems

TeleGraph: A Benchmark Dataset for Hierarchical Link Prediction

1 code implementation16 Apr 2022 Min Zhou, Bisheng Li, Menglin Yang, Lujia Pan

Link prediction is a key problem for network-structured data, attracting considerable research efforts owing to its diverse applications.

Link Prediction

Hyperbolic Graph Neural Networks: A Review of Methods and Applications

1 code implementation28 Feb 2022 Menglin Yang, Min Zhou, Zhihao LI, Jiahong Liu, Lujia Pan, Hui Xiong, Irwin King

Graph neural networks generalize conventional neural networks to graph-structured data and have received widespread attention due to their impressive representation ability.

Anatomy Graph Learning

Enhancing Hyperbolic Graph Embeddings via Contrastive Learning

no code implementations21 Jan 2022 Jiahong Liu, Menglin Yang, Min Zhou, Shanshan Feng, Philippe Fournier-Viger

Inspired by the recently active and emerging self-supervised learning, in this study, we attempt to enhance the representation power of hyperbolic graph models by drawing upon the advantages of contrastive learning.

Contrastive Learning Graph Representation Learning +2

Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation

no code implementations14 Aug 2021 Yankai Chen, Menglin Yang, Yingxue Zhang, Mengchen Zhao, Ziqiao Meng, Jianye Hao, Irwin King

Aiming to alleviate data sparsity and cold-start problems of traditional recommender systems, incorporating knowledge graphs (KGs) to supplement auxiliary information has recently gained considerable attention.

Knowledge-Aware Recommendation Knowledge Graphs

Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space

1 code implementation8 Jul 2021 Menglin Yang, Min Zhou, Marcus Kalander, Zengfeng Huang, Irwin King

To explore these properties of a complex temporal network, we propose a hyperbolic temporal graph network (HTGN) that fully takes advantage of the exponential capacity and hierarchical awareness of hyperbolic geometry.

Graph Embedding Graph Neural Network +2

FeatureNorm: L2 Feature Normalization for Dynamic Graph Embedding

1 code implementation27 Feb 2021 Menglin Yang, Ziqiao Meng, Irwin King

As a matter of fact, this smoothing technique can not only encourage must-link node pairs to get closer but also push cannot-link pairs to shrink together, which potentially cause serious feature shrink or oversmoothing problem, especially when stacking graph convolution in multiple layers or steps.

Dynamic graph embedding

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