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<title cf:type="text"><![CDATA[Editorial department of the Journal of National University of Defense Technology -->Graph Intelligent Computing]]></title>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Multi-view heterogeneous graph embedding method with  hierarchical projection]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202503001]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[A self-supervised graph embedding approach based on hierarchical projection network called MeghenNet(multi-view heterogeneous graph projection network) was introduced to learn low-dimensional representations from multiple views. The concept of multiple-view heterogeneous graphs was defined to explicitly allow the model to simultaneously collect information from multiple data sources for modeling heterogeneous graphs. A hierarchical attention projection that involves a cross-relation projection to extract semantics information within each view was employed, followed by a cross-view projection to aggregate contextual information from other views. The mutual information loss function between each view embedding and the global embedding was computed to ensure the information consistency across views. Experimental results on several real-world datasets demonstrate that the proposed method outperforms state-of-the-art approaches when handling multi-view heterogeneous graphs.]]></description>
<pubDate>2025/6/3 0:00:00</pubDate>
<category><![CDATA[Graph Intelligent Computing]]></category>
<author><![CDATA[HAO Yunzhi, ZHENG Tongya, WANG Xingen, WANG Xinyu, SONG Mingli, CHEN Chun, ZHOU Chunyan]]></author>
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<atom:name>HAO Yunzhi, ZHENG Tongya, WANG Xingen, WANG Xinyu, SONG Mingli, CHEN Chun, ZHOU Chunyan</atom:name>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Multi-round social advertising sequence influence maximization]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202503002]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Existing research on sequential ad recommendations mainly focuses on user preferences for advertisement, insufficiently considering positive relationships between ads. Starting from the associations between ads, incorporates both ad networks and user networks into consideration, a multi-round social advertising influence maximization model based on triggering model was constructed. An ad edge based greedy strategy based on multi-round reverse influence sampling was proposed to enhance platform revenue, with theoretical proofs of its strict lower bound guarantee. Experiments show that compared to existing optimal methods, the proposed method increases the average ad propagation influence revenue by 35%, significantly enhancing ad recommendation effectiveness, providing a new solution for ad sequence recommendations.]]></description>
<pubDate>2025/6/3 0:00:00</pubDate>
<category><![CDATA[Graph Intelligent Computing]]></category>
<author><![CDATA[FU Bingyang, ZHANG Longjiao, SHI Qihao, WANG Zeyu, WANG Can, SONG Mingli]]></author>
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<atom:name>FU Bingyang, ZHANG Longjiao, SHI Qihao, WANG Zeyu, WANG Can, SONG Mingli</atom:name>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Adaptive strategy for boosting node costs minimization in  multi-round influence]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202503003]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[In order to reduce the marketing costs of merchants promoting products over multiple rounds on social networks,this study made a exploration on the selection of boosting nodes during the process of multi-round influence propagation. Based on the model of multi-round influence boosting propagation mode, an adaptive strategy for choosing boosting nodes was designed. Given known seed nodes, this strategy could find an efficient method to minimize the number of marketing rounds needed to reach a certain threshold of social influence, with nearly linear algorithmic complexity. Experimental results show that compared to existing heuristic algorithms and non-adaptive algorithms, the designed adaptive strategy can reduce the promotion rounds required to reach a specified threshold by 7.3%~18.3%, effectively reducing the promotion cost.]]></description>
<pubDate>2025/6/3 0:00:00</pubDate>
<category><![CDATA[Graph Intelligent Computing]]></category>
<author><![CDATA[ZHANG Longjiao, FU Bingyang, SHI Qihao, SONG Mingli, WANG Can, ZHANG Yue]]></author>
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<atom:name>ZHANG Longjiao, FU Bingyang, SHI Qihao, SONG Mingli, WANG Can, ZHANG Yue</atom:name>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Anomaly detection algorithm based on graph neural network formissing multivariate time series]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202503004]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Addressing the issue of anomaly detection on missing multivariate time series data in real IoT（Internet of things） environments, a novel method on multivariate time series anomaly detection algorithm intergrated with graph embedding of missing information was proposed. Using a joint learning framework of pre-interpolation and anomaly detection task fusion, a GNN（graph neural network） pre-interpolation module based on time series Gaussian kernel function was designed to realize the joint optimization of pre-interpolation and anomaly detection task. A graph structure learning method for embedding missing information in time series data was proposed, using graph attention mechanism to fuse missing information masking matrix and spatiotemporal feature vectors, effectively modeling the potential connections of missing data distribution in multivariate time series. The performance of the algorithm was verified on real IoT sensor datasets. Experimental results prove that the proposed method significantly outperform the mainstream two-stage methods on the task of missing multivariate time series anomaly detection. The comparative experiment of the pre-interpolation module fully prove the effectiveness of the GNN pre-interpolation layer based on the Gaussian kernel function.]]></description>
<pubDate>2025/6/3 0:00:00</pubDate>
<category><![CDATA[Graph Intelligent Computing]]></category>
<author><![CDATA[GAO Yang, WANG Xinyu, HE Da, SONG Mingli, ZHOU Chunyan]]></author>
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<atom:name>GAO Yang, WANG Xinyu, HE Da, SONG Mingli, ZHOU Chunyan</atom:name>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[S-Cypher: temporal query language on the temporal propertygraph model]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202503005]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Traditional graph data models lack explicit temporal dimension representation, which may lead to complex temporal queries and potential loss of temporal information integrity. To address this limitation, a temporal property graph data model and a corresponding temporal graph query language called S-Cypher were proposed. The temporal graph data model represents utilized object nodes to represent entities, and introduced property nodes and value nodes to represent entity properties. Valid time was recorded on nodes and edges between object nodes to express temporal information, and the recorded valid time adhered a set of temporal constraints. S-Cypher served as a temporal extension to Cypher, ensured compatibility while providing a concise and comprehensive temporal graph query syntax, including temporal data types, temporal graph pattern matching, time window constraints, and temporal paths. An implementation scheme for executing S-Cypher temporal graph queries on Neo4j was also provided. Experimental results demonstrate that the query time of S-Cypher is on average 1.29 times that of Cypher, indicating that S-Cypher can effectively manage temporal graph data in Neo4j with satisfactory performance.]]></description>
<pubDate>2025/6/3 0:00:00</pubDate>
<category><![CDATA[Graph Intelligent Computing]]></category>
<author><![CDATA[JIANG Tiantian, CHEN Guanlin, SONG Mingli, HANG Haitian, WANG Haoye]]></author>
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<atom:name>JIANG Tiantian, CHEN Guanlin, SONG Mingli, HANG Haitian, WANG Haoye</atom:name>
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