Reverse Graph Learning for Graph Neural Network

Peng, Liang; Hu, Rongyao; Kong, Fei; Gan, Jiangzhang; Mo, Yujie; Shi, Xiaoshuang; Zhu, Xiaofeng

doi:10.1109/tnnls.2022.3161030

Cited by 47 publications

(3 citation statements)

References 39 publications

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“…Based on the study of references [16][17][18][19][20], graph-based intrusion detection involves processing each stream into a graph by utilizing the interactive information between packets. The graph is then classified using various methods to learn its vector representation.…”

Section: Related Workmentioning

confidence: 99%

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Research on Virus Propagation Network Intrusion Detection Based on Graph Neural Network

Ying,

Pan,

Chen

et al. 2024

Mathematics

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The field of network security is highly concerned with intrusion detection, which safeguards the security of computer networks. The invention and application of intrusion detection technology play indispensable roles in network security, and it is crucial to investigate and comprehend this topic. Recently, with the continuous occurrence of intrusion incidents in virus propagation networks, traditional network detection algorithms for virus propagation have encountered limitations and have struggled to detect these incidents effectively and accurately. Therefore, updating the intrusion detection algorithm of the virus-spreading network is imperative. This paper introduces a novel system for virus propagation, whose core is a graph-based neural network. By organically combining two modules—a standardization module and a computation module—this system forms a powerful GNN model. The standardization module uses two methods, while the calculation module uses three methods. Through permutation and combination, we obtain six GNN models with different characteristics. To verify their performance, we conducted experiments on the selected datasets. The experimental results show that the proposed algorithm has excellent capabilities, high accuracy, reasonable complexity, and excellent stability in the intrusion detection of virus-spreading networks, making the network more secure and reliable.

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Section: Related Workmentioning

confidence: 99%

“…[11][12][13][14][15]) and graph-based detection (ref. [16][17][18][19][20]). The traffic-based detection method uses statistical analysis to detect link-type data, though this method does not apply to topology.…”

Section: Introductionmentioning

confidence: 99%

Research on Virus Propagation Network Intrusion Detection Based on Graph Neural Network

Ying,

Pan,

Chen

et al. 2024

Mathematics

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show abstract

“…[40] propose a fast flexible bipartite graph model for the coclustering method that directly uses the original matrix to construct the bipartite graph. Since existing GNN methods usually have trouble in predicting unseen data points, [41] propose a reverse GNN model to learn the graph from the intrinsic space of the original data points as well as to investigate a new out-of-sample extension method. Some graph convolutional networks are not robust to different quality of both the feature matrix and the initial graph, [42] propose a multigraph fusion method to produce a highquality graph and a low-dimensional space of original highdimensional data for the GCN model.…”

Section: Graph Modelingmentioning

confidence: 99%

A Trend of AI Conference Convergence in Similarity: An Empirical Study Through Trans-Temporal Heterogeneous Graph

Gao

Zhang

Chen

et al. 2023

IEEE Trans. Knowl. Data Eng.

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Publishing the research works on academic publications is an important part of the scientific process. Since the development of computer science research is very fast, researchers tend to publish the research works in a fast way, such as conferences whose review processes are faster than the journals. In the past decades, one conference usually focuses on a specific research field and the topic or method overlap between conferences is low. We have noticed that, in recent years, some topics or methods which were once studied in a small number of specific research fields have become popular in many other fields. Naturally, we come up with two research questions: (1) Do the conferences indeed become similar? and (2) How do conferences become similar? In this paper, we first use a trans-temporal heterogeneous graph network to model academic conferences in recent 20 years. Due to the large number of conferences, we categorize these conferences into 6 research fields for brevity. Then, we first quantitatively and qualitatively assess "Do the research fields become similar?" and then focus on exploring "How do research fields become similar?". From the result, we find the reason for the research fields in computer science become similar is that AI becomes pervasive and researchers tend to apply the machine learning methods to different application fields. Since the methods become universal between different research fields, researchers should pay more attention to advanced information in other fields to motivate more interdisciplinary works. To assist the researchers to explore related interdisciplinary advanced information, it is crucial to measure the cross-field impact of papers using the citation information and recommend the paper which has a high cross-field impact on the related researchers. As for the newly published papers which do not have any citations, we also propose a cross-field impact prediction model to recommend the cutting-edge research works to related researchers accurately. Experiments conducted on real-world datasets verify the effectiveness of the proposed method.

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Drug discovery through Covid-19 genome sequencing with siamese graph convolutional neural network

Pati

Gupta

Banerjee³

et al. 2023

Multimed Tools Appl

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After several waves of COVID-19 led to a massive loss of human life worldwide due to the changes in its variants and the vast explosion. Several researchers proposed neural network-based drug discovery techniques to fight against the pandemic; utilizing neural networks has limitations (Exponential time complexity, Non-Convergence, Mode Collapse, and Diminished Gradient). To overcome those difficulties, this paper proposed a hybrid architecture that will help to repurpose the most appropriate medicines for the treatment of COVID-19. A brief investigation of the sequences has been made to discover the gene density and noncoding proportion through the next gene sequencing. The paper tracks the exceptional locales in the virus DNA sequence as a Drug Target Region (DTR). Then the variable DNA neighborhood search is applied to this DTR to obtain the DNA interaction network to show how the genes are correlated. A drug database has been obtained based on the ontological property of the genomes with advanced D3Similarity so that all the chemical components of the drug database have been identified. Other methods obtained hydroxychloroquine as an effective drug which was rejected by WHO. However, The experimental results show that Remdesivir and Dexamethasone are the most effective drugs, with 97.41 and 97.93%, respectively.

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Reverse Graph Learning for Graph Neural Network

Cited by 47 publications

References 39 publications

Research on Virus Propagation Network Intrusion Detection Based on Graph Neural Network

Research on Virus Propagation Network Intrusion Detection Based on Graph Neural Network

A Trend of AI Conference Convergence in Similarity: An Empirical Study Through Trans-Temporal Heterogeneous Graph

Drug discovery through Covid-19 genome sequencing with siamese graph convolutional neural network

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