{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/interpretable-subgraph-feature-extraction-for","title":"Interpretable Subgraph Feature Extraction for Hyperlink Prediction","arxiv_id":null,"date":"2023-12-01","proceeding":"ICDM 2023 12","authors":["Peiyan Li","Liming Pan","Kai Li","Claudia Plant","Christian Böhm"],"abstract":"Hyperlink prediction aims to predict interactions among multiple entries, constituting a practical yet challenging problem in the literature. While a handful of solutions have been proposed, they generally operate on the entire hypergraph. A practical subgraph-based solution not only enables better identification of localized characteristics of the central hyperedge but also alleviates scalability concerns. In this study, we present SSF, an innovative hyperlink prediction methodology based on Subgraph Structural Features. The rationale behind SSF is that hyperedges and non-hyperedges exhibit distinct local patterns, which can be unveiled through the assimilation of subgraph structural features. To this end, we utilize well-established structural heuristics such as walks and loops as the fundamental building blocks. We commence by extracting a subgraph encompassing each focal hyperedge, subsequently integrating an edge weakening scheme to facilitate feature extraction from the initial subgraph and its variations. The extracted feature vector is interpretable, and the designed edge weakening scheme empowers SSF with an adaptive capability to handle hypergraphs with varying densities. Lastly, a multilayer perceptron classifier is trained for prediction. Experiment results on ten real-world hypergraph networks demonstrate the effectiveness of the proposed approach.","url_abs":"https://ieeexplore.ieee.org/abstract/document/10415757","url_pdf":"https://www.researchgate.net/profile/Peiyan-Li-2/publication/378000024_Interpretable_Subgraph_Feature_Extraction_for_Hyperlink_Prediction/links/65c2441a1e1ec12eff78ca64/Interpretable-Subgraph-Feature-Extraction-for-Hyperlink-Prediction.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"interpretable-subgraph-feature-extraction-for","repo_url":"https://github.com/KXDY233/SSF","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"hyperedge-prediction","task_name":"Hyperedge Prediction"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"hyperedge-classification","task_name":"hyperedge classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}