Adaptive multi-source data fusion vessel trajectory prediction model for intelligent maritime traffic

Incorporating logic rules with textual representations for interpretable knowledge graph reasoning

One key challenge within the domain of network science is accurately finding important nodes within a network. In recent years, researchers have proposed various node centrality indicators from different perspectives. However, many existing methods have their limitations. For instance, certain approaches lack a balance between time efficiency and accuracy, while the majority of research neglects the significance of local clustering coefficients, a crucial node property. Thus, this paper introduces a centrality metric called DNC (degree and neighborhood information centrality) that considers both node degree and local clustering coefficients. The combination of these two aspects provides DNC with the ability to create a more comprehensive measure of nodes’ local centrality. In addition, in order to obtain better performance in different networks, this paper sets a tunable parameter α to control the effect of neighbor information on the importance of nodes. Subsequently, the paper proceeds with a sequence of experiments, including connectivity tests, to validate the efficacy of DNC. The results of the experiments demonstrate that DNC captures more information and outperforms the other eight centrality metrics.

A Novel Method to Identify Key Nodes in Complex Networks Based on Degree and Neighborhood Information

Educational Data Mining (EDM) has emerged as a vital field of research, which harnesses the power of computational techniques to analyze educational data. With the increasing complexity and diversity of educational data, Deep Learning techniques have shown significant advantages in addressing the challenges associated with analyzing and modeling this data. This survey aims to systematically review the state-of-the-art in EDM with Deep Learning. We begin by providing a brief introduction to EDM and Deep Learning, highlighting their relevance in the context of modern education. Next, we present a detailed review of Deep Learning techniques applied in four typical educational scenarios, including knowledge tracing, undesirable student detecting, performance prediction, and personalized recommendation. Furthermore, a comprehensive overview of public datasets and processing tools for EDM is provided. Finally, we point out emerging trends and future directions in this research area.

pdf/a-comprehensive-survey-on-deep-learning-techniques-in-4jukff3rcj.pdf

A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining

A well-performed doctor recommendation system is significant for both patients and Online Medical Consultation Platforms (OMCPs). Though previous studies have proposed many doctor recommendation methods, some are overly personalized for implementation in large-scale OMCPs, while some other machine learning-based approaches perform poorly due to the simplistic information available about patients and doctors on OMCPs. This research proposes an online doctor recommendation framework based on knowledge graph (KG) and joint learning to address these problems. The framework first constructs a comprehensive medical KG, including details about doctors on the platform and a wealth of medical knowledge, to better extract features of doctors and patients. It obtains feature representations of doctors from the medical KG and extracts features from patients' consultation texts at both sentence and word levels using word embedding and KG embedding. Finally, these features are fed into a deep neural network to calculate the recommendation probability. All processes are learned simultaneously within an overall framework. Extensive experiments conducted on four real datasets illustrate the superior performance of our model and the effectiveness of incorporating KG into doctor recommendation in providing interpretations for the recommendation results. 

Knowledge-enhanced online doctor recommendation framework based on knowledge graph and joint learning

More and more researches have focused on the use of knowledge graphs (KG) to solve the sparsity problem of traditional collaborative filtering recommendation systems. However, most KG based recommendation algorithms focus on independent paths connecting users and items, or iteratively propagate user preferences in KG. Therefore, in this study, we propose a knowledge graph recommendation system algorithm for the multiple paths RNN encoder (AGRE), which fully considers the association between paths. Specifically, the paths between the user and the item are coded by a specified RNN (MRNN) to accurately learn the user’s preferences. Traditional RNNs can encode multiple paths without considering the association between paths, but our RNN can encode multiple paths with considering the association between paths. Our RNNs are encoded with full consideration of the association between paths. We have compared AGRE with other state-of-the-art algorithms on three real-world datasets, and achieved good results in terms of AUC and Precision@K. This indicates that AGRE could solve the problem of sparse interaction between users and items, and could make full use of the knowledge graph for recommendation. • We consider the association between paths when encoding multiple paths. • We design an improved RNN. By this RNN, we can encode multiple paths. 

AGRE: A knowledge graph recommendation algorithm based on multiple paths embeddings RNN encoder


 The identification of important nodes in complex networks has always been a prominent topic in the field of network science. Nowadays, the emergence of large-scale networks has sparked our research interest in complex network centrality methods that balance accuracy and efficiency. Therefore, this paper proposes a novel centrality method called Spon (Sum of the Proportion Of Neighbors) Centrality, which combines algorithmic efficiency and accuracy. Spon only requires information within the three-hop neighborhood of a node to assess its centrality, thereby exhibiting lower time complexity and suitability for large-scale networks. To evaluate the performance of Spon, we conducted connectivity tests on 16 empirical unweighted networks and compared the monotonicity and algorithmic efficiency of Spon with other methods. Experimental results demonstrate that Spon achieves both accuracy and algorithmic efficiency, outperforming eight other methods, including CycleRatio, Collective Influence, and Social Capital. Additionally, we present a method called W-Spon to extend Spon to weighted networks. Comparative experimental results on 10 empirical weighted networks illustrate that W-Spon also possesses advantages compared to methods such as I-Core and M-Core.

Identifying Critical Nodes in Complex Networks Based on Neighborhood Information

As modern network communication technology rapidly develops in recent years, complex networks have become a hot multidisciplinary research field. In this field, relatively important nodes mining is an emerging research topic with theoretical significance and application value. However, most researchers in the field of complex networks focus on sorting the global information in the network. Existing relatively important nodes mining algorithms commonly focus on the structural characteristics of the network and do not take into account the influence of community information on relatively important nodes mining. This paper addresses these problems by proposing a relatively important nodes mining algorithm based on community detection and biased random walk with restart (CDBRWR). This approach integrates the community information of the network into the mining of relatively important nodes for the first time and recommends a new biased random walk strategy with restart to realize the accurate and efficient mining of relatively important nodes in various networks. The performance of the proposed algorithm is examined through experimental verification and analysis of real network datasets. Results show that the CDBRWR algorithm outperforms other comparative algorithms in precision, recall, and AUC (area under the curve). • CDBRWR makes the best of the network nodes’ community information to obtain the network’s topology characteristics. • CDBRWR adopts the biased random walk strategy with restart to acquire the global information of the entire network. • CDBRWR is a superior candidate for quantifying the relative importance of nodes in the network. • A simple yet clear research framework about how to identify relatively important nodes. 

Relatively important nodes mining algorithm based on community detection and biased random walk with restart

Identifying nodes that hold relative importance in complex networks is a burgeoning research area. Previous studies have demonstrated the effectiveness of the gravity model in capturing interactions among nodes in high-dimensional network space. Traditional algorithms predominantly concentrate on structural characteristics and node random walks in high-dimensional network space, overlooking substantial computational overhead and intricacies within the high-dimensional space. Simultaneously, the rapid evolution of the Internet has given rise to a series of emerging technologies, such as embedding models that can map high-dimensional network nodes to low-dimensional vector space. To address this issue, we introduce a novel algorithm for quantifying node relative importance, denoted as the Network Embedding and Gravity Model (NEGM). First, the network embedding method transforms nodes into low-dimensional, real-valued, dense vectors in Euclidean space. Then, drawing inspiration from Newton's law of universal gravitation, it proposes a novel gravity model. Finally, utilizing the novel gravity model, it calculates the aggregate attractive force of all nodes within the target node set. Experimental results show that NEGM excels in measuring the relative importance of nodes in various types of networks, demonstrating its significant potential for mining deep-seated information within authentic networks like protein networks and criminal networks. 

Estimating the relative importance of nodes in complex networks based on network embedding and gravity model

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