{"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/deepinf-social-influence-prediction-with-deep","title":"DeepInf: Social Influence Prediction with Deep Learning","arxiv_id":"1807.05560","date":"2018-07-15","proceeding":null,"authors":["Jiezhong Qiu","Jian Tang","Hao Ma","Yuxiao Dong","Kuansan Wang","Jie Tang"],"abstract":"Social and information networking activities such as on Facebook, Twitter,\nWeChat, and Weibo have become an indispensable part of our everyday life, where\nwe can easily access friends' behaviors and are in turn influenced by them.\nConsequently, an effective social influence prediction for each user is\ncritical for a variety of applications such as online recommendation and\nadvertising.\n  Conventional social influence prediction approaches typically design various\nhand-crafted rules to extract user- and network-specific features. However,\ntheir effectiveness heavily relies on the knowledge of domain experts. As a\nresult, it is usually difficult to generalize them into different domains.\nInspired by the recent success of deep neural networks in a wide range of\ncomputing applications, we design an end-to-end framework, DeepInf, to learn\nusers' latent feature representation for predicting social influence. In\ngeneral, DeepInf takes a user's local network as the input to a graph neural\nnetwork for learning her latent social representation. We design strategies to\nincorporate both network structures and user-specific features into\nconvolutional neural and attention networks. Extensive experiments on Open\nAcademic Graph, Twitter, Weibo, and Digg, representing different types of\nsocial and information networks, demonstrate that the proposed end-to-end\nmodel, DeepInf, significantly outperforms traditional feature engineering-based\napproaches, suggesting the effectiveness of representation learning for social\napplications.","url_abs":"http://arxiv.org/abs/1807.05560v1","url_pdf":"http://arxiv.org/pdf/1807.05560v1.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":"deepinf-social-influence-prediction-with-deep","repo_url":"https://github.com/xptree/DeepInf","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.05560","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.05560"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/xptree/DeepInf","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":1},"by_repo_kind":{"official":{"samples":1,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"426dbfd7d478620b","entry":"load_w2v_feature","repo":"xptree/DeepInf","repo_kind":"official","path":"src/utils.py","file_url":"https://github.com/xptree/DeepInf/blob/HEAD/src/utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"426dbfd7d478620b"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}