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This technique has demonstrated remarkable efficacy in various network analysis and inference tasks. However, existing approaches study network evolution mainly by analyzing snapshots of temporal networks, while neglecting the intrinsic formation mechanisms of temporal heterogeneous networks. Few dynamic models delve into the intrinsic factors propelling network evolution. To fill this research gap, we introduce a novel learning framework for temporal heterogeneous network representation learning with dynamic influence modeling, denoted as THNRD. THNRD pioneers the application of the Hawkes process to temporal heterogeneous networks, utilizing the linking process of dynamic events to emulate the network\u2019s formation mechanism, capturing the intrinsic dynamic progression of temporal heterogeneous networks. Subsequently, THNRD introduces a multilayer spatiotemporal aggregation model under a unified spatiotemporal framework, which is designed to harmoniously integrate the semantic and dynamic attributes of the networks. We also take node influence into consideration to further describe the temporal emergent phenomena. We verify the effectiveness of our proposed method via extensive experimental evaluations on real\u2010world datasets. The results consistently demonstrate that THNRD outperforms current state\u2010of\u2010the\u2010art methods.<\/jats:p>","DOI":"10.1155\/int\/6673499","type":"journal-article","created":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:39:27Z","timestamp":1775914767000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Temporal Heterogeneous Network Representation Learning With Dynamic Influence Modeling"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-6013-836X","authenticated-orcid":false,"given":"Haodan","family":"Ran","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3565-2482","authenticated-orcid":false,"given":"Yang","family":"Fang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6339-0219","authenticated-orcid":false,"given":"Xiang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1786-9831","authenticated-orcid":false,"given":"Jiuyang","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1440-4757","authenticated-orcid":false,"given":"Weiming","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,4,11]]},"reference":[{"key":"e_1_2_12_1_2","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevResearch.2.033325"},{"key":"e_1_2_12_2_2","unstructured":"ZhangM.andChenY. 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