Mingqiang Zhou^*, Mengjiao Li, Zhiyuan Qian, Kunpeng Li

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2443-2460, 2023, DOI:10.32604/cmc.2023.038675 - 30 August 2023

Abstract Role-based network embedding aims to embed role-similar nodes into a similar embedding space, which is widely used in graph mining tasks such as role classification and detection. Roles are sets of nodes in graph networks with similar structural patterns and functions. However, the role-similar nodes may be far away or even disconnected from each other. Meanwhile, the neighborhood node features and noise also affect the result of the role-based network embedding, which are also challenges of current network embedding work. In this paper, we propose a Role-based network Embedding via Quantum walk with weighted Features… More >

Sufen Zhao^1,2, Rong Peng^1,*, Po Hu², Liansheng Tan²

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 83-130, 2023, DOI:10.32604/cmes.2023.024781 - 23 April 2023

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 More > Graphic Abstract

Hong Li^1,*, Jianjun Li¹, Guohui Li¹, Rong Gao², Lingyu Yan²

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1687-1709, 2023, DOI:10.32604/cmc.2023.035239 - 06 February 2023

Abstract Expert Recommendation (ER) aims to identify domain experts with high expertise and willingness to provide answers to questions in Community Question Answering (CQA) web services. How to model questions and users in the heterogeneous content network is critical to this task. Most traditional methods focus on modeling questions and users based on the textual content left in the community while ignoring the structural properties of heterogeneous CQA networks and always suffering from textual data sparsity issues. Recent approaches take advantage of structural proximities between nodes and attempt to fuse the textual content of nodes for… More >

S. Kalavathi^1,*, R. Nedunchelian²

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 39-56, 2023, DOI:10.32604/iasc.2023.031522 - 29 September 2022

Abstract Network analysis is a promising field in the area of network applications as different types of traffic grow enormously and exponentially. Reliable route prediction is a challenging task in the Large Scale Networks (LSN). Various non-self-learning and self-learning approaches have been adopted to predict reliable routing. Routing protocols decide how to send all the packets from source to the destination addresses across the network through their IP. In the current era, dynamic protocols are preferred as they network self-learning internally using an algorithm and may not entail being updated physically more than the static protocols.… More >

Ye Yang¹, Dongjie Zhu^2,*, 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 - 24 March 2022

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 More >

Dongjie Zhu¹, Yundong Sun¹, Xiaofang Li², Haiwen Du³, Rongning Qu², Pingping Yu^{4, *}, Xuefeng Piao¹, Russell Higgs⁵, Ning Cao⁶

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1343-1356, 2020, DOI:10.32604/cmc.2020.010008 - 30 April 2020

Abstract Interactivity is the most significant feature of network data, especially in social networks. Existing network embedding methods have achieved remarkable results in learning network structure and node attributes, but do not pay attention to the multiinteraction between nodes, which limits the extraction and mining of potential deep interactions between nodes. To tackle the problem, we propose a method called MultiInteraction heterogeneous information Network Embedding (MINE). Firstly, we introduced the multi-interactions heterogeneous information network and extracted complex heterogeneous relation sequences by the multi-interaction extraction algorithm. Secondly, we use a well-designed multi-relationship network fusion model based on More >

Chengzhang Zhu^{1, 2}, Wentao Zhao^{2, *}, Qian Li¹, Pan Li², Qiaobo Da³

CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 317-333, 2019, DOI:10.32604/cmc.2019.05677

Abstract Detecting collaborative fraudsters who manipulate opinions in social media is becoming extremely important in order to provide reliable information, in which, however, the diversity in different groups of collaborative fraudsters presents a significant challenge to existing collaborative fraudsters detection methods. These methods often detect collaborative fraudsters as the largest group of users who have the strongest relation with each other in the social media, consequently overlooking the other groups of fraudsters that are with strong user relation yet small group size. This paper introduces a novel network embedding-based framework NEST and its instance BEST to… More >