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Competition Analysis on Enterprise Ecosystems via Multiview Relational hypergraph embedding

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2023年第6期10卷 3054-3065页

作者： Yin, Zikai Xu, Tong Zhao, Ziwei Zhiyuli, Aakas Li, Xueying Chen, Enhong Univ Sci & Technol China Sch Data Sci Anhui Prov Key Lab Big Data Anal & Applicat Hefei 230027 Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Anhui Prov Key Lab Big Data Anal & Applicat Hefei 230027 Peoples R China Alibaba Grp Hangzhou 310023 Peoples R China

Enterprise competition analysis has long been treated as a crucial task of management science, which can reveal pertinent information about market saturation and business opportunities to support the decision-making process of entrepreneurs and investors. Recently, with the development of graph representation techniques, enterprises could be now formulated in a novel graph-oriented perspective to model their cooperation and competition in a directional graph. However, these prior arts mainly treat the enterprises as individual nodes, while the ecosystems, i.e., enterprise groups formed based on their common interests, and consequent interaction effects have been largely ignored. To address this issue, in this article, by adapting the concept of hypergraph to describe enterprise ecosystems, we propose a novel multiview enterprise relation network (MERN) framework for enterprise competition analysis. Specifically, we first put forward a hypertranslating embedding (HTransE) algorithm inspired by translation distance models for the hyperrelation embedding of ecosystems. Meanwhile, a relational graph convolutional network (RGCN) is designed for ordinary relation embedding, e.g., supplier, client, or competitor. Afterward, considering the commercial diversity in ecosystems, which means that different enterprises may focus on different industry fields, we propose an industry trend embedding module to describe industry distribution and development trend factors for each enterprise. Finally, we apply the self-attention mechanism to integrate these three modules and adapt the bilinear model DistMult for competition link analysis. Extensive experiments on a real-world dataset validate that our solution can achieve better performance compared with several competitive baselines.

关键词： Ecosystems Industries Business Analytical models Market research Indexes Adaptation models Enterprise competition analysis hypergraph embedding translation distance model

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Dimensionality Reduction by Similarity Distance-Based hypergraph embedding

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ATMOSPHERE 2022年第9期13卷 1449-1449页

作者： Shen, Xingchen Fang, Shixu Qiang, Wenwen Southern Marine Sci & Engn Guangdong Lab Guangzho Guangzhou 511458 Peoples R China Chinese Acad Sci Inst Software Sci & Technol Integrated Informat Syst Lab Beijing 100190 Peoples R China China Acad Informat & Commun Technol Beijing 100191 Peoples R China

Dimensionality reduction (DR) is an essential pre-processing step for hyperspectral image processing and analysis. However, the complex relationship among several sample clusters, which reveals more intrinsic information about samples but cannot be reflected through a simple graph or Euclidean distance, is worth paying attention to. For this purpose, we propose a novel similarity distance-based hypergraph embedding method (SDHE) for hyperspectral images DR. Unlike conventional graph embedding-based methods that only consider the affinity between two samples, SDHE takes advantage of hypergraph embedding to describe the complex sample relationships in high order. Besides, we propose a novel similarity distance instead of Euclidean distance to measure the affinity between samples for the reason that the similarity distance not only discovers the complicated geometrical structure information but also makes use of the local distribution information. Finally, based on the similarity distance, SDHE aims to find the optimal projection that can preserve the local distribution information of sample sets in a low-dimensional subspace. The experimental results in three hyperspectral image data sets demonstrate that our SDHE acquires more efficient performance than other state-of-the-art DR methods, which improve by at least 2% on average.

关键词： dimensionality reduction hypergraph embedding unsupervised hyperspectral remote sensing

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Spammer Groups Detection Based on hypergraph embedding and Autoencoder Classifier Model 7

Spammer Groups Detection Based on Hypergraph Embedding and A...

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7th International Conference on Electronic Information Technology and Computer Engineering (EITCE)

作者： Ma, Ru Zhao, Jiachen Huo, Chenghang Zhan, Xiaodong Zhang, Fuzhi Yanshan Univ Sch Informat Sci & Engn Qinhuangdao Hebei Peoples R China Changan Commun Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9798400708305

Aiming at the limitations of existing spammer group detection approaches, we propose a novel approach for detecting spammer groups based on hypergraph embedding and autoencoder-classifier. First, we use time periodicity and rating behavior to model the review dataset as a heterogeneous hypergraph. Second, we combine the autoencoder and attention mechanism to design a hypergraph neural network to learn the high-quality low-dimensional vector representations of nodes from the heterogeneous hypergraph and use the MeanShift clustering algorithm to obtain candidate spamming groups by filtering items, ratings, and time nodes. Finally, we apply an abnormal detection model based on an autoencoder-classifier to detect the spammer groups. The experimental results on real-world review datasets show that the proposed method has better detection performance than the baselines.

关键词： Group spammer detection hypergraph hypergraph embedding Abnormal detection model

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Social Multi-role Discovering with hypergraph embedding for Location-Based Social Networks 14th

Social Multi-role Discovering with Hypergraph Embedding for ...

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14th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Minh Tam Pham Thanh Dat Hoang Minh Hieu Nguyen Viet Hung Vu Thanh Trung Huynh Quyet Thang Huynh Hanoi Univ Sci & Technol Hanoi Vietnam Griffith Univ Southport Qld Australia

ISBN: (纸本)9783031217425;9783031217432

Location-based social networks (LBSNs) have become more and more popular in the recent years. The typical LBSN platforms such as Foursquare, Facebook Local or Yelp allow the user to share their daily digital footprints in the form of check-ins with other people in different communities. The dynamic between users' social context and their mobility plays an important role in LBSN, e.g. users potentially participate with their friends in the same activity. The social interaction also demonstrate in the form of multi-role context, as each user may experience different activities with each particular community. Existing representation learning for LBSNs analysis often fails to fully capture such complex social pattern. In this paper, we propose a representation learning model in which the multi-role social interaction can be captured simultaneously with the mobility information. More specifically, the model first applies a "persona" decomposition process, where each user node is splitted into several pseudo nodes presenting for his social roles. The process then learns multiple presentations for each persona that reflect the corresponding role by maximizing the collocation of nodes sampling on input user-user edges (friendships) and user-time-POI-semantic hyperedges (check-ins). We conduct experiments on 5 real-world datasets with 7 state-of-the-art baselines to demonstrate the robustness of our model on downstream tasks such as friendship suggestion and location prediction.

关键词： Location-based social network hypergraph embedding Recommendation system

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Dimensionality Reduction of Hyperspectral Image Using Spatial-Spectral Regularized Sparse hypergraph embedding

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REMOTE SENSING 2019年第9期11卷 1039-1039页

作者： Huang, Hong Chen, Meili Duan, Yule Chongqing Univ Educ Minist China Key Lab Optoelect Technol & Syst Chongqing 400044 Peoples R China

Many graph embedding methods are developed for dimensionality reduction (DR) of hyperspectral image (HSI), which only use spectral features to reflect a point-to-point intrinsic relation and ignore complex spatial-spectral structure in HSI. A new DR method termed spatial-spectral regularized sparse hypergraph embedding (SSRHE) is proposed for the HSI classification. SSRHE explores sparse coefficients to adaptively select neighbors for constructing the dual sparse hypergraph. Based on the spatial coherence property of HSI, a local spatial neighborhood scatter is computed to preserve local structure, and a total scatter is computed to represent the global structure of HSI. Then, an optimal discriminant projection is obtained by possessing better intraclass compactness and interclass separability, which is beneficial for classification. Experiments on Indian Pines and PaviaU hyperspectral datasets illustrated that SSRHE effectively develops a better classification performance compared with the traditional spectral DR algorithms.

关键词： hyperspectral image dimensionality reduction spatial-spectral feature hypergraph embedding sparse representation

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SOCIAL-GUIDED REPRESENTATION LEARNING FOR IMAGES VIA DEEP HETEROGENEOUS hypergraph embedding

SOCIAL-GUIDED REPRESENTATION LEARNING FOR IMAGES VIA DEEP HE...

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IEEE International Conference on Multimedia and Expo (IEEE ICME)

作者： Chu, Yunfei Feng, Chunyan Guo, Caili Beijing Lab Adv Informat Networks Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Key Lab Network Syst Architecture & Conve Beijing 100876 Peoples R China

ISBN: (纸本)9781538617373

Representation learning for images is widely recognized as critical to the performance of end tasks such as image classification and cross-modal retrieval. However, most existing methods extract features only from visual content, far from adequate in interpreting semantics latent in images. For social images, there also exists rich social context information, e.g. owners, tags and groups, which provides cues for interpreting semantics. In this paper, we propose a representation learning framework via deep heterogeneous hypergraph embedding (DHHE), considering both visual content and social contexts. In particular, images and their contexts are first represented as a heterogeneous hypergraph, which is then embedded into a low-dimensional space. To incorporate visual information and generalize for unseen images, we learn the mapping from visual content to the semantic space. We conduct experiments with the tasks of classification, cross-modal retrieval and recommendation, which demonstrates the effectiveness of our approach and the merits of social guidance.

关键词： Representation learning deep learning hypergraph embedding social media

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SOCIAL-GUIDED REPRESENTATION LEARNING FOR IMAGES VIA DEEP HETEROGENEOUS hypergraph embedding

SOCIAL-GUIDED REPRESENTATION LEARNING FOR IMAGES VIA DEEP HE...

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IEEE International Conference on Multimedia and Expo

作者： Yunfei Chu Chunyan Feng Caili Guo Beijing Laboratory of Advanced Information Networks Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications China

关键词： Representation learning Deep learning hypergraph embedding Social media

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Multi-source biological knowledge-guided hypergraph spatiotemporal subnetwork embedding for protein complex identification

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BRIEFINGS IN BIOINFORMATICS 2025年第1期26卷 bbae718页

作者： Wang, Shilong Cui, Hai Qu, Yanchen Zhang, Yijia Dalian Maritime Univ Informat Sci & Technol Coll 1 Linghai Rd Dalian 116026 Liaoning Peoples R China

Identifying biologically significant protein complexes from protein-protein interaction (PPI) networks and understanding their roles are essential for elucidating protein functions, life processes, and disease mechanisms. Current methods typically rely on static PPI networks and model PPI data as pairwise relationships, which presents several limitations. Firstly, static PPI networks do not adequately represent the scopes and temporal dynamics of protein interactions. Secondly, a large amount of available biological resources have not been fully integrated. Moreover, PPIs in biological systems are not merely one-to-one relationships but involve higher order non-pairwise interactions. To alleviate these issues, we propose HGST, a multi-source biological knowledge-guided hypergraph spatiotemporal subnetwork (subnet) embedding method for identifying biologically significant protein complexes from PPI networks. HGST initially constructs spatiotemporal PPI subnets using the scopes and temporal dynamics of proteins derived from multi-source biological knowledge, treating them as dynamic networks through fine-grained spatiotemporal partitioning. The spatiotemporal subnets are then transformed into hypergraphs, which model higher order non-pairwise relationships via hypergraph embedding. Simultaneously, fine-grained amino acid sequence features and coarse-grained gene ontology attributes are introduced for multi-dimensional feature fusion. Finally, protein complexes are identified from the reweighted subnets based on fused feature representations using the core-attachment strategy. Evaluations on four real PPI datasets demonstrate that HGST achieves competitive performance. Furthermore, a series of biological analyses confirm the high biological significance of the complexes identified by HGST. The source code is available at https://***/qifen37/HGST.

关键词： spatiotemporal subnetwork protein complex hypergraph embedding biological knowledge

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Higher-order link prediction via light hypergraph neural network and hybrid aggregator

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2025年第4期16卷 2671-2685页

作者： Rui, Xiaobin Zhuang, Jiaxin Sun, Chengcheng Wang, Zhixiao China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Minist Educ Mine Digitizat Engn Res Ctr Xuzhou 221116 Jiangsu Peoples R China

Link prediction, which aims to predict missing links or possible future links between two nodes, is one of the most important research in social network analysis. Higher-order link prediction, a natural extension of this problem in hypergraphs, focuses on predicting hyperlinks among multiple nodes. The extended problem has a wide range of applications, including predicting chemical reactions and forecasting social communications. hypergraph neural networks (HGNNs), designed to address the characteristics of higher-order networks (hypergraphs), are variants of graph neural networks (GNNs). Compared with traditional methods, these end-to-end approaches based on HGNNs are effective tools for higher-order link prediction. However, these approaches still have some shortcomings. On the one hand, HGNNs, deriving from traditional GNNs, inevitably inherit unnecessary complexity and redundant computation from deep learning lineage. On the other hand, the existing aggregators do not take into account the similarity among the nodes' features, resulting in information loss in the embeddings of hyperlinks. To solve the both shortcomings, firstly, we propose a Light hypergraph Neural Network (LHGNN) by removing some specific linear feature transformation layers and activation function layers to reduce complexity and increase reliability. Secondly, we propose a Hybrid Aggregator (HA) to obtain hyperlinks' embeddings more comprehensively by concatenating the mean and standard deviation of the nodes' embeddings. Experimental results on six datasets from four different domains show that our method outperforms state-of-the-art methods with a simpler structure.

关键词： Higher-order link prediction hypergraph embedding Light neural networks Hyperlink aggregator

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Scalable hypergraph Visualization

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2024年第1期30卷 595-605页

作者： Oliver, Peter Zhang, Eugene Zhang, Yue Oregon State Univ Sch Elect Engn & Comp Sci Corvalis OR 97331 USA

hypergraph visualization has many applications in network data analysis. Recently, a polygon-based representation for hypergraphs has been proposed with demonstrated benefits. However, the polygon-based layout often suffers from excessive self-intersections when the input dataset is relatively large. In this paper, we propose a framework in which the hypergraph is iteratively simplified through a set of atomic operations. Then, the layout of the simplest hypergraph is optimized and used as the foundation for a reverse process that brings the simplest hypergraph back to the original one, but with an improved layout. At the core of our approach is the set of atomic simplification operations and an operation priority measure to guide the simplification process. In addition, we introduce necessary definitions and conditions for hypergraph planarity within the polygon representation. We extend our approach to handle simultaneous simplification and layout optimization for both the hypergraph and its dual. We demonstrate the utility of our approach with datasets from a number of real-world applications.

关键词： hypergraph visualization scalable visualization polygon layout hypergraph embedding primal-dual visualization

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