Link prediction method for dynamic networks based on matching degree of nodes
The research of dynamic evolutionary trends and temporal characteristics in real networks which are important part of the real world is a hot research issue nowadays.As a typical research tool in the field of network science, link prediction technique can be used to predict the network evolution by...
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| Format: | Article |
| Language: | English |
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POSTS&TELECOM PRESS Co., LTD
2022-08-01
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| Series: | 网络与信息安全学报 |
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| Online Access: | http://www.infocomm-journal.com/cjnis/CN/10.11959/j.issn.2096-109x.2022053 |
| _version_ | 1818020037728927744 |
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| author | Cong LI, Xinsheng JI, Ushuxin LI, Jinsong LI, Haitao LI |
| author_facet | Cong LI, Xinsheng JI, Ushuxin LI, Jinsong LI, Haitao LI |
| author_sort | Cong LI, Xinsheng JI, Ushuxin LI, Jinsong LI, Haitao LI |
| collection | DOAJ |
| description | The research of dynamic evolutionary trends and temporal characteristics in real networks which are important part of the real world is a hot research issue nowadays.As a typical research tool in the field of network science, link prediction technique can be used to predict the network evolution by mining the historical edge information and then predict the future edge.The topological evolution of dynamic real networks was analyzed and it found that the interaction and matching between nodes in the network topology can capture the dynamic characteristics of the network more comprehensively.The proposed method analyzed the attribute characteristics of network nodes, and defined a node importance quantification method based on primary and secondary influences.Besides, a time decay factor was introduced to portray the influence of network topology on the formation of connected edges at different moments.Furthermore, the node importance and time decay factor were combined to define the Temporal Matching Degree of Nodes (TMDN), which was used to measure the possibility of future edge formation between node pairs.The experimental results in five real dynamic network datasets showed that the proposed method achieves better prediction performance under both AUC and Ranking Score, with a maximum improvement of 42%.It also proved the existence of interactive matching priority among nodes, and confirmed the effectiveness of both primary and secondary influence of nodes.As the future work, we will add diversified feature information to further deepen the analysis of dynamic real networks and then predict the evolution law more accurately. |
| first_indexed | 2024-04-14T08:00:35Z |
| format | Article |
| id | doaj.art-b1b0b2f5482e4038876a08eb4d1ce21d |
| institution | Directory Open Access Journal |
| issn | 2096-109X |
| language | English |
| last_indexed | 2024-04-14T08:00:35Z |
| publishDate | 2022-08-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 网络与信息安全学报 |
| spelling | doaj.art-b1b0b2f5482e4038876a08eb4d1ce21d2022-12-22T02:04:55ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2022-08-018413114310.11959/j.issn.2096-109x.2022053Link prediction method for dynamic networks based on matching degree of nodesCong LI, Xinsheng JI, Ushuxin LI, Jinsong LI, Haitao LI 0Information Engineering University, Zhengzhou 450001, ChinaThe research of dynamic evolutionary trends and temporal characteristics in real networks which are important part of the real world is a hot research issue nowadays.As a typical research tool in the field of network science, link prediction technique can be used to predict the network evolution by mining the historical edge information and then predict the future edge.The topological evolution of dynamic real networks was analyzed and it found that the interaction and matching between nodes in the network topology can capture the dynamic characteristics of the network more comprehensively.The proposed method analyzed the attribute characteristics of network nodes, and defined a node importance quantification method based on primary and secondary influences.Besides, a time decay factor was introduced to portray the influence of network topology on the formation of connected edges at different moments.Furthermore, the node importance and time decay factor were combined to define the Temporal Matching Degree of Nodes (TMDN), which was used to measure the possibility of future edge formation between node pairs.The experimental results in five real dynamic network datasets showed that the proposed method achieves better prediction performance under both AUC and Ranking Score, with a maximum improvement of 42%.It also proved the existence of interactive matching priority among nodes, and confirmed the effectiveness of both primary and secondary influence of nodes.As the future work, we will add diversified feature information to further deepen the analysis of dynamic real networks and then predict the evolution law more accurately.http://www.infocomm-journal.com/cjnis/CN/10.11959/j.issn.2096-109x.2022053dynamic networkslink predictionmatching degree of nodesnode importancetime decaying parameter |
| spellingShingle | Cong LI, Xinsheng JI, Ushuxin LI, Jinsong LI, Haitao LI Link prediction method for dynamic networks based on matching degree of nodes 网络与信息安全学报 dynamic networks link prediction matching degree of nodes node importance time decaying parameter |
| title | Link prediction method for dynamic networks based on matching degree of nodes |
| title_full | Link prediction method for dynamic networks based on matching degree of nodes |
| title_fullStr | Link prediction method for dynamic networks based on matching degree of nodes |
| title_full_unstemmed | Link prediction method for dynamic networks based on matching degree of nodes |
| title_short | Link prediction method for dynamic networks based on matching degree of nodes |
| title_sort | link prediction method for dynamic networks based on matching degree of nodes |
| topic | dynamic networks link prediction matching degree of nodes node importance time decaying parameter |
| url | http://www.infocomm-journal.com/cjnis/CN/10.11959/j.issn.2096-109x.2022053 |
| work_keys_str_mv | AT conglixinshengjiushuxinlijinsonglihaitaoli linkpredictionmethodfordynamicnetworksbasedonmatchingdegreeofnodes |