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Hao Yan

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16 papers
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16

IJCAI Conference 2025 Conference Paper

A Relaxed Symmetric Non-negative Matrix Factorization Approach for Community Discovery (Extended Abstract)

  • Zhigang Liu
  • Hao Yan
  • Yurong Zhong
  • Weiling Li

Community discovery is a prominent issue in com-plex network analysis. Symmetric non-negative matrix factorization (SNMF) is frequently adopted to tackle this issue. The use of a single feature matrix can depict network symmetry, but it limits its ability to learn node representations. To break this limitation, we present a novel Relaxed Symmetric NMF (RSN) approach to boost an SNMF-based community detector. It works by 1) expanding the representational space and its degrees of freedom with multiple feature factors; 2) integrating the well-designed equality-constraints to make the model well-aware of the network’s intrinsic symmetry; 3) employing graph regularization to pre-serve the local geometric invariance of the network structure; and 4) separating constraints from decision variables for efficient optimization via the principle of alternating-direction-method of multi-pliers. RSN’s effectiveness is verified through empirical studies on six real social networks, show-casing superior precision in community discovery over existing models and baselines.

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NeurIPS Conference 2024 Conference Paper

Coherence-free Entrywise Estimation of Eigenvectors in Low-rank Signal-plus-noise Matrix Models

  • Hao Yan
  • Keith Levin

Spectral methods are widely used to estimate eigenvectors of a low-rank signal matrix subject to noise. These methods use the leading eigenspace of an observed matrix to estimate this low-rank signal. Typically, the entrywise estimation error of these methods depends on the coherence of the low-rank signal matrix with respect to the standard basis. In this work, we present a novel method for eigenvector estimation that avoids this dependence on coherence. Assuming a rank-one signal matrix, under mild technical conditions, the entrywise estimation error of our method provably has no dependence on the coherence under Gaussian noise (i. e. , in the spiked Wigner model), and achieves the optimal estimation rate up to logarithmic factors. Simulations demonstrate that our method performs well under non-Gaussian noise and that an extension of our method to the case of a rank-$r$ signal matrix has little to no dependence on the coherence. In addition, we derive new metric entropy bounds for rank-$r$ singular subspaces under $\ell_{2, \infty}$ distance, which may be of independent interest. We use these new bounds to improve the best known lower bound for rank-$r$ eigenspace estimation under $\ell_{2, \infty}$ distance.

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NeurIPS Conference 2023 Conference Paper

A Comprehensive Study on Text-attributed Graphs: Benchmarking and Rethinking

  • Hao Yan
  • Chaozhuo Li
  • Ruosong Long
  • Chao Yan
  • Jianan Zhao
  • Wenwen Zhuang
  • Jun Yin
  • Peiyan Zhang

Text-attributed graphs (TAGs) are prevalent in various real-world scenarios, where each node is associated with a text description. The cornerstone of representation learning on TAGs lies in the seamless integration of textual semantics within individual nodes and the topological connections across nodes. Recent advancements in pre-trained language models (PLMs) and graph neural networks (GNNs) have facilitated effective learning on TAGs, garnering increased research interest. However, the absence of meaningful benchmark datasets and standardized evaluation procedures for TAGs has impeded progress in this field. In this paper, we propose CS-TAG, a comprehensive and diverse collection of challenging benchmark datasets for TAGs. The CS-TAG datasets are notably large in scale and encompass a wide range of domains, spanning from citation networks to purchase graphs. In addition to building the datasets, we conduct extensive benchmark experiments over CS-TAG with various learning paradigms, including PLMs, GNNs, PLM-GNN co-training methods, and the proposed novel topological pre-training of language models. In a nutshell, we provide an overview of the CS-TAG datasets, standardized evaluation procedures, and present baseline experiments. The entire CS-TAG project is publicly accessible at \url{https: //github. com/sktsherlock/TAG-Benchmark}.

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IS Journal 2023 Journal Article

Convolutional Neural Network-Assisted Adaptive Sampling for Sparse Feature Detection in Image and Video Data

  • Geet Lahoti
  • Chitta Ranjan
  • Jialei Chen
  • Hao Yan
  • Chuck Zhang

In this article, we propose a feature detection approach that employs an adaptive sampling technique coupled with a convolutional neural network (CNN) model, to detect sparse features of interest in high-dimensional input data. Adaptive sampling criterion smartly explores the high-dimensional input and exploits the regions of interest. The CNN model determines the likelihood of the presence of the desired features, which guides the exploitation component of the sampling strategy. The effectiveness of the approach is illustrated using case studies, where emotions in a candidate’s interview video are detected for evaluation purpose and anomalies in a product’s image are extracted for quality control. The approach reduces evaluation time and minimizes amount of input data to be accessed and processed while effectively identifying desired sparse features.

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NeurIPS Conference 2023 Conference Paper

Train Once and Explain Everywhere: Pre-training Interpretable Graph Neural Networks

  • Jun Yin
  • Chaozhuo Li
  • Hao Yan
  • Jianxun Lian
  • Senzhang Wang

Intrinsic interpretable graph neural networks aim to provide transparent predictions by identifying the influential fraction of the input graph that guides the model prediction, i. e. , the explanatory subgraph. However, current interpretable GNNs mostly are dataset-specific and hard to generalize to different graphs. A more generalizable GNN interpretation model which can effectively distill the universal structural patterns of different graphs is until-now unexplored. Motivated by the great success of recent pre-training techniques, we for the first time propose the Pre-training Interpretable Graph Neural Network ($\pi$-GNN) to distill the universal interpretability of GNNs by pre-training over synthetic graphs with ground-truth explanations. Specifically, we introduce a structural pattern learning module to extract diverse universal structure patterns and integrate them together to comprehensively represent the graphs of different types. Next, a hypergraph refining module is proposed to identify the explanatory subgraph by incorporating the universal structure patterns with local edge interactions. Finally, the task-specific predictor is cascaded with the pre-trained $\pi$-GNN model and fine-tuned over downstream tasks. Extensive experiments demonstrate that $\pi$-GNN significantly surpasses the leading interpretable GNN baselines with up to 9. 98\% interpretation improvement and 16. 06\% classification accuracy improvement. Meanwhile, $\pi$-GNN pre-trained on graph classification task also achieves the top-tier interpretation performance on node classification task, which further verifies its promising generalization performance among different downstream tasks. Our code and datasets are available at https: //anonymous. 4open. science/r/PI-GNN-F86C

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AAAI Conference 2023 Conference Paper

WSiP: Wave Superposition Inspired Pooling for Dynamic Interactions-Aware Trajectory Prediction

  • Renzhi Wang
  • Senzhang Wang
  • Hao Yan
  • Xiang Wang

Predicting motions of surrounding vehicles is critically important to help autonomous driving systems plan a safe path and avoid collisions. Although recent social pooling based LSTM models have achieved significant performance gains by considering the motion interactions between vehicles close to each other, vehicle trajectory prediction still remains as a challenging research issue due to the dynamic and high-order interactions in the real complex driving scenarios. To this end, we propose a wave superposition inspired social pooling (Wave-pooling for short) method for dynamically aggregating the high-order interactions from both local and global neighbor vehicles. Through modeling each vehicle as a wave with the amplitude and phase, Wave-pooling can more effectively represent the dynamic motion states of vehicles and capture their high-order dynamic interactions by wave superposition. By integrating Wave-pooling, an encoder-decoder based learning framework named WSiP is also proposed. Extensive experiments conducted on two public highway datasets NGSIM and highD verify the effectiveness of WSiP by comparison with current state-of-the-art baselines. More importantly, the result of WSiP is more interpretable as the interaction strength between vehicles can be intuitively reflected by their phase difference. The code of the work is publicly available at https://github.com/Chopin0123/WSiP.

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AAAI Conference 2020 Conference Paper

Tensor Completion for Weakly-Dependent Data on Graph for Metro Passenger Flow Prediction

  • Ziyue Li
  • Nurettin Dorukhan Sergin
  • Hao Yan
  • Chen Zhang
  • Fugee Tsung

Low-rank tensor decomposition and completion have attracted significant interest from academia given the ubiquity of tensor data. However, low-rank structure is a global property, which will not be fulfilled when the data presents complex and weak dependencies given specific graph structures. One particular application that motivates this study is the spatiotemporal data analysis. As shown in the preliminary study, weakly dependencies can worsen the low-rank tensor completion performance. In this paper, we propose a novel lowrank CANDECOMP / PARAFAC (CP) tensor decomposition and completion framework by introducing the L1-norm penalty and Graph Laplacian penalty to model the weakly dependency on graph. We further propose an efficient optimization algorithm based on the Block Coordinate Descent for efficient estimation. A case study based on the metro passenger flow data in Hong Kong is conducted to demonstrate an improved performance over the regular tensor completion methods.

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TIST Journal 2015 Journal Article

Peacock

  • Yi Wang
  • Xuemin Zhao
  • Zhenlong Sun
  • Hao Yan
  • Lifeng Wang
  • Zhihui Jin
  • Liubin Wang
  • Yang Gao

Latent Dirichlet allocation (LDA) is a popular topic modeling technique in academia but less so in industry, especially in large-scale applications involving search engine and online advertising systems. A main underlying reason is that the topic models used have been too small in scale to be useful; for example, some of the largest LDA models reported in literature have up to 10 3 topics, which difficultly cover the long-tail semantic word sets. In this article, we show that the number of topics is a key factor that can significantly boost the utility of topic-modeling systems. In particular, we show that a “big” LDA model with at least 10 5 topics inferred from 10 9 search queries can achieve a significant improvement on industrial search engine and online advertising systems, both of which serve hundreds of millions of users. We develop a novel distributed system called Peacock to learn big LDA models from big data. The main features of Peacock include hierarchical distributed architecture, real-time prediction, and topic de-duplication. We empirically demonstrate that the Peacock system is capable of providing significant benefits via highly scalable LDA topic models for several industrial applications.

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