Arrow Research search

Author name cluster

Yanghui Rao

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

7 papers
2 author rows

Possible papers

7

ICML Conference 2025 Conference Paper

Improved Expressivity of Hypergraph Neural Networks through High-Dimensional Generalized Weisfeiler-Leman Algorithms

  • Detian Zhang
  • Chengqiang Zhang
  • Yanghui Rao
  • Li Qing
  • Chun Jiang Zhu

The isomorphism problem is a key challenge in both graph and hypergraph domains, crucial for applications like protein design, chemical pathways, and community detection. Hypergraph isomorphism, which models high-order relationships in real-world scenarios, remains underexplored compared to the graph isomorphism. Current algorithms for hypergraphs, like the 1-dimensional generalized Weisfeiler-Lehman test (1-GWL), lag behind advancements in graph isomorphism tests, limiting most hypergraph neural networks to 1-GWL’s expressive power. To address this, we propose the high-dimensional GWL (k-GWL), generalizing k-WL from graphs to hypergraphs. We prove that k-GWL reduces to k-WL for simple graphs, and thus develop a unified isomorphism method for both graphs and hypergraphs. We also successfully establish a clear and complete understanding of the GWL hierarchy of expressivity, showing that (k+1)-GWL is more expressive than k-GWL with illustrative examples. Based on k-GWL, we develop a hypergraph neural network model named k-HNN with improved expressive power of k-GWL, which achieves superior performance on real-world datasets, including a 6% accuracy improvement on the Steam-Player dataset over the runner-up. Our code is available at https: //github. com/talence-zcq/KGWL.

Details

IJCAI Conference 2022 Conference Paper

Graph-based Dynamic Word Embeddings

  • Yuyin Lu
  • Xin Cheng
  • Ziran Liang
  • Yanghui Rao

As time goes by, language evolves with word semantics changing. Unfortunately, traditional word embedding methods neglect the evolution of language and assume that word representations are static. Although contextualized word embedding models can capture the diverse representations of polysemous words, they ignore temporal information as well. To tackle the aforementioned challenges, we propose a graph-based dynamic word embedding (GDWE) model, which focuses on capturing the semantic drift of words continually. We introduce word-level knowledge graphs (WKGs) to store short-term and long-term knowledge. WKGs can provide rich structural information as supplement of lexical information, which help enhance the word embedding quality and capture semantic drift quickly. Theoretical analysis and extensive experiments validate the effectiveness of our GDWE on dynamic word embedding learning.

PDF Details DOI

IS Journal 2021 Journal Article

GSMNet: Global Semantic Memory Network for Aspect-Level Sentiment Classification

  • Zhiyue Liu
  • Jiahai Wang
  • Xin Du
  • Yanghui Rao
  • Xiaojun Quan

Aspect-level sentiment classification determines the sentiment polarity of a targeted aspect. To solve this task, attention-based neural networks are typically adopted to explore the interaction between the aspect and its context in a single sentence. However, such approaches ignore the rich semantic information that can be obtained from other sentences. This article shows that the contexts of aspects with similar meanings should be considered global semantic information that can be incorporated as domain knowledge. Then, a novel global semantic memory network (GSMNet) is proposed to share the global semantic information of various aspects and generate a domain-specific representation. With the help of domain knowledge, crucial words can be focused on more precisely. Moreover, instead of employing the concatenating operation for vectors before classification, GSMNet adopts a fine-grained information fusion layer to capture the importance of representations for efficiently extracting the valid parts of each dimension. The experimental results demonstrate the effectiveness of our model.

Details DOI

IS Journal 2019 Journal Article

Segment-level joint topic-sentiment model for online review analysis

  • Qinjuan Yang
  • Yanghui Rao
  • Haoran Xie
  • Jiahai Wang
  • Fu Lee Wang
  • Wai Hong Chan

With the rapid development of the Internet, an increasing number of users enjoy to shop online and express their reviews on the products and services. Analysis of these online reviews can not only help potential users make rational decisions when purchasing but also improves the quality of products and services. Hence, sentiment analysis for online reviews has become an important and meaningful research domain.

Details DOI

AAAI Conference 2017 Short Paper

Cross-Domain Sentiment Classification via Topic-Related TrAdaBoost

  • Xingchang Huang
  • Yanghui Rao
  • Haoran Xie
  • Tak-Lam Wong
  • Fu Lee Wang

Cross-domain sentiment classification aims to tag sentiments for a target domain by labeled data from a source domain. Due to the difference between domains, the accuracy of a trained classifier may be very low. In this paper, we propose a boosting-based learning framework named TR-TrAdaBoost for cross-domain sentiment classification. We firstly explore the topic distribution of documents, and then combine it with the unigram TrAdaBoost. The topic distribution captures the domain information of documents, which is valuable for cross-domain sentiment classification. Experimental results indicate that TR-TrAdaBoost represents documents well and boost the performance and robustness of TrAdaBoost.

PDF Details

IS Journal 2016 Journal Article

Contextual Sentiment Topic Model for Adaptive Social Emotion Classification

  • Yanghui Rao

Social emotion classification is important for numerous applications, such as public opinion measurement, corporate reputation estimation, and customer preference analysis. However, topics that evoke a certain emotion in the general public are often context-sensitive, making it difficult to train a universal classifier for all collections. A multilabeled sentiment topic model, namely, the contextual sentiment topic model (CSTM), can be used for adaptive social emotion classification. The CSTM distinguishes context-independent topics from both a background theme, which characterizes nondiscriminative information, and a contextual theme, which characterizes context-dependent information across different collections. Experimental results demonstrated the effectiveness of this model for the adaptive classification of social emotions.

Details DOI

AAAI Conference 2016 Conference Paper

Social Emotion Classification via Reader Perspective Weighted Model

  • Xin Li
  • Yanghui Rao
  • Yanjia Chen
  • Xuebo Liu
  • Huan Huang

With the development of Web 2. 0, many users express their opinions online. This paper is concerned with the classification of social emotions on varied-scale data sets. Different from traditional models which weight training documents equally, the concept of emotional entropy is proposed to estimate the weight and tackle the issue of noisy documents. The topic assignment is also used to distinguish different emotional senses of the same word. Experimental evaluations using different data sets validate the effectiveness of the proposed social emotion classification model.

PDF Details