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  • Open Access


    Heterogeneous Network Embedding: A Survey

    Sufen Zhao1,2, Rong Peng1,*, Po Hu2, Liansheng Tan2

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 83-130, 2023, DOI:10.32604/cmes.2023.024781

    Abstract Real-world complex networks are inherently heterogeneous; they have different types of nodes, attributes, and relationships. In recent years, various methods have been proposed to automatically learn how to encode the structural and semantic information contained in heterogeneous information networks (HINs) into low-dimensional embeddings; this task is called heterogeneous network embedding (HNE). Efficient HNE techniques can benefit various HIN-based machine learning tasks such as node classification, recommender systems, and information retrieval. Here, we provide a comprehensive survey of key advancements in the area of HNE. First, we define an encoder-decoder-based HNE model taxonomy. Then, we systematically overview, compare, and summarize various… More > Graphic Abstract

    Heterogeneous Network Embedding: A Survey

  • Open Access


    Community Discovery Algorithm Based on Multi-Relationship Embedding

    Dongming Chen, Mingshuo Nie, Jie Wang, Dongqi Wang*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2809-2820, 2023, DOI:10.32604/csse.2023.035494

    Abstract Complex systems in the real world often can be modeled as network structures, and community discovery algorithms for complex networks enable researchers to understand the internal structure and implicit information of networks. Existing community discovery algorithms are usually designed for single-layer networks or single-interaction relationships and do not consider the attribute information of nodes. However, many real-world networks consist of multiple types of nodes and edges, and there may be rich semantic information on nodes and edges. The methods for single-layer networks cannot effectively tackle multi-layer information, multi-relationship information, and attribute information. This paper proposes a community discovery algorithm based… More >

  • Open Access


    Critical Relation Path Aggregation-Based Industrial Control Component Exploitable Vulnerability Reasoning

    Zibo Wang1,3, Chaobin Huo2, Yaofang Zhang1,3, Shengtao Cheng1,3, Yilu Chen1,3, Xiaojie Wei5, Chao Li4, Bailing Wang1,3,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2957-2979, 2023, DOI:10.32604/cmc.2023.035694

    Abstract With the growing discovery of exposed vulnerabilities in the Industrial Control Components (ICCs), identification of the exploitable ones is urgent for Industrial Control System (ICS) administrators to proactively forecast potential threats. However, it is not a trivial task due to the complexity of the multi-source heterogeneous data and the lack of automatic analysis methods. To address these challenges, we propose an exploitability reasoning method based on the ICC-Vulnerability Knowledge Graph (KG) in which relation paths contain abundant potential evidence to support the reasoning. The reasoning task in this work refers to determining whether a specific relation is valid between an… More >

  • Open Access


    DCRL-KG: Distributed Multi-Modal Knowledge Graph Retrieval Platform Based on Collaborative Representation Learning

    Leilei Li1, Yansheng Fu2, Dongjie Zhu2,*, Xiaofang Li3, Yundong Sun2, Jianrui Ding2, Mingrui Wu2, Ning Cao4,*, Russell Higgs5

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3295-3307, 2023, DOI:10.32604/iasc.2023.035257

    Abstract The knowledge graph with relational abundant information has been widely used as the basic data support for the retrieval platforms. Image and text descriptions added to the knowledge graph enrich the node information, which accounts for the advantage of the multi-modal knowledge graph. In the field of cross-modal retrieval platforms, multi-modal knowledge graphs can help to improve retrieval accuracy and efficiency because of the abundant relational information provided by knowledge graphs. The representation learning method is significant to the application of multi-modal knowledge graphs. This paper proposes a distributed collaborative vector retrieval platform (DCRL-KG) using the multimodal knowledge graph VisualSem… More >

  • Open Access


    Knowledge Graph Representation Learning Based on Automatic Network Search for Link Prediction

    Zefeng Gu, Hua Chen*

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2497-2514, 2023, DOI:10.32604/cmes.2023.024332

    Abstract Link prediction, also known as Knowledge Graph Completion (KGC), is the common task in Knowledge Graphs (KGs) to predict missing connections between entities. Most existing methods focus on designing shallow, scalable models, which have less expressive than deep, multi-layer models. Furthermore, most operations like addition, matrix multiplications or factorization are handcrafted based on a few known relation patterns in several well-known datasets, such as FB15k, WN18, etc. However, due to the diversity and complex nature of real-world data distribution, it is inherently difficult to preset all latent patterns. To address this issue, we propose KGE-ANS, a novel knowledge graph embedding… More >

  • Open Access


    Future Event Prediction Based on Temporal Knowledge Graph Embedding

    Zhipeng Li1,2, Shanshan Feng3,*, Jun Shi2, Yang Zhou2, Yong Liao1,2, Yangzhao Yang2, Yangyang Li4, Nenghai Yu1, Xun Shao5

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2411-2423, 2023, DOI:10.32604/csse.2023.026823

    Abstract Accurate prediction of future events brings great benefits and reduces losses for society in many domains, such as civil unrest, pandemics, and crimes. Knowledge graph is a general language for describing and modeling complex systems. Different types of events continually occur, which are often related to historical and concurrent events. In this paper, we formalize the future event prediction as a temporal knowledge graph reasoning problem. Most existing studies either conduct reasoning on static knowledge graphs or assume knowledges graphs of all timestamps are available during the training process. As a result, they cannot effectively reason over temporal knowledge graphs… More >

  • Open Access


    Improved Density Peaking Algorithm for Community Detection Based on Graph Representation Learning

    Jiaming Wang2, Xiaolan Xie1,2,*, Xiaochun Cheng3, Yuhan Wang2

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 997-1008, 2022, DOI:10.32604/csse.2022.027005


    There is a large amount of information in the network data that we can exploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection problem. Meanwhile, there is always an unpredictable distribution of class clusters output by graph representation learning. Therefore, we propose an improved density peak clustering algorithm (ILDPC) for the community detection problem, which improves the local density mechanism in the original algorithm and can better accommodate class clusters of different shapes. And we study the… More >

  • Open Access


    Research on Cross-domain Representation Learning Based on Multi-network Space Fusion

    Ye Yang1, Dongjie Zhu2,*, Xiaofang Li3, Haiwen Du4, Yundong Sun4, Zhixin Huo2, Mingrui Wu2, Ning Cao1, Russell Higgs5

    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1379-1391, 2022, DOI:10.32604/iasc.2022.025181

    Abstract In recent years, graph representation learning has played a huge role in the fields and research of node clustering, node classification, link prediction, etc., among which many excellent models and methods have emerged. These methods can achieve better results for model training and verification of data in a single space domain. However, in real scenarios, the solution of cross-domain problems of multiple information networks is very practical and important, and the existing methods cannot be applied to cross-domain scenarios, so we research on cross-domain representation is based on multi-network space integration. This paper conducts representation learning research for cross-domain scenarios.… More >

  • Open Access


    MSM: A Method of Multi-Neighborhood Sampling Matching for Entity Alignment

    Donglei Lu1, Yundong Sun2, Qinrui Dai2, Xiaofang Li3,*, Dongjie Zhu4, Haiwen Du2, Yansong Wang4, Rongning Qu3, Ning Cao1, Gregory M. P. O’Hare5

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1141-1151, 2022, DOI:10.32604/iasc.2022.020218

    Abstract The heterogeneity of knowledge graphs brings great challenges to entity alignment. In particular, the attributes of network entities in the real world are complex and changeable. The key to solving this problem is to expand the neighborhoods in different ranges and extract the neighborhood information efficiently. Based on this idea, we propose Multi-neighborhood Sampling Matching Network (MSM), a new KG alignment network, aiming at the structural heterogeneity challenge. MSM constructs a multi-neighborhood network representation learning method to learn the KG structure embedding. It then adopts a unique sampling and cosine cross-matching method to solve different sizes of neighborhoods and distinct… More >

  • Open Access


    Enhanced Deep Autoencoder Based Feature Representation Learning for Intelligent Intrusion Detection System

    Thavavel Vaiyapuri*, Adel Binbusayyis

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3271-3288, 2021, DOI:10.32604/cmc.2021.017665

    Abstract In the era of Big data, learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system (IDS). Owing to the lack of accurately labeled network traffic data, many unsupervised feature representation learning models have been proposed with state-of-the-art performance. Yet, these models fail to consider the classification error while learning the feature representation. Intuitively, the learnt feature representation may degrade the performance of the classification task. For the first time in the field of intrusion detection, this paper proposes an unsupervised IDS model leveraging the… More >

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