{"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/slotgat-slot-based-message-passing-for","title":"SlotGAT: Slot-based Message Passing for Heterogeneous Graph Neural Network","arxiv_id":"2405.01927","date":"2024-05-03","proceeding":null,"authors":["Ziang Zhou","Jieming Shi","Renchi Yang","Yuanhang Zou","Qing Li"],"abstract":"Heterogeneous graphs are ubiquitous to model complex data. There are urgent needs on powerful heterogeneous graph neural networks to effectively support important applications. We identify a potential semantic mixing issue in existing message passing processes, where the representations of the neighbors of a node $v$ are forced to be transformed to the feature space of $v$ for aggregation, though the neighbors are in different types. That is, the semantics in different node types are entangled together into node $v$'s representation. To address the issue, we propose SlotGAT with separate message passing processes in slots, one for each node type, to maintain the representations in their own node-type feature spaces. Moreover, in a slot-based message passing layer, we design an attention mechanism for effective slot-wise message aggregation. Further, we develop a slot attention technique after the last layer of SlotGAT, to learn the importance of different slots in downstream tasks. Our analysis indicates that the slots in SlotGAT can preserve different semantics in various feature spaces. The superiority of SlotGAT is evaluated against 13 baselines on 6 datasets for node classification and link prediction. Our code is at https://github.com/scottjiao/SlotGAT_ICML23/.","url_abs":"https://arxiv.org/abs/2405.01927v1","url_pdf":"https://arxiv.org/pdf/2405.01927v1.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":"slotgat-slot-based-message-passing-for","repo_url":"https://github.com/scottjiao/slotgat_icml23","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"heterogeneous-node-classification","task_name":"Heterogeneous Node Classification"},{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":"node-classification","task_name":"Node Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/heterogeneous-node-classification-on-acm","task":"Heterogeneous Node Classification","dataset":"ACM (Heterogeneous Node Classification)","model":"SlotGAT","rank_in_archive_order":2,"of":11,"metrics":{" Macro-F1":"93.99","Micro-F1":"94.06"},"uses_additional_data":false},{"leaderboard":"/sota/heterogeneous-node-classification-on-dblp-2","task":"Heterogeneous Node Classification","dataset":"DBLP (Heterogeneous Node Classification)","model":"SlotGAT","rank_in_archive_order":2,"of":11,"metrics":{" Macro-F1":"94.95","Micro-F1":"95.31"},"uses_additional_data":false},{"leaderboard":"/sota/heterogeneous-node-classification-on-freebase","task":"Heterogeneous Node Classification","dataset":"Freebase (Heterogeneous Node Classification)","model":"SlotGAT","rank_in_archive_order":3,"of":9,"metrics":{" Macro-F1":"49.68","Micro-F1":"66.83"},"uses_additional_data":false},{"leaderboard":"/sota/heterogeneous-node-classification-on-imdb","task":"Heterogeneous Node Classification","dataset":"IMDB (Heterogeneous Node Classification)","model":"SlotGAT","rank_in_archive_order":2,"of":11,"metrics":{" Macro-F1":"64.05","Micro-F1":"68.54"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2405.01927","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.01927"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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