{"type":"ScholarlyArticle","title":"JANE: Jointly Adversarial Network Embedding","authors":[{"name":"Liang Yang","slug":"liang-yang","given":"Liang","family":"Yang","affiliation":"School of Artificial Intelligence , Hebei University of Technology , China; State Key Laboratory of Information Security , Institute of Information Engineering , CAS , China"},{"name":"Yuexue Wang","slug":"yuexue-wang","given":"Yuexue","family":"Wang","affiliation":"School of Artificial Intelligence , Hebei University of Technology , China"},{"name":"Junhua Gu","slug":"junhua-gu","given":"Junhua","family":"Gu","affiliation":"School of Artificial Intelligence , Hebei University of Technology , China"},{"name":"Chuan Wang","slug":"chuan-wang","given":"Chuan","family":"Wang","affiliation":"State Key Laboratory of Information Security , Institute of Information Engineering , CAS , China"},{"name":"Xiaochun Cao","slug":"xiaochun-cao","given":"Xiaochun","family":"Cao","affiliation":"State Key Laboratory of Information Security , Institute of Information Engineering , CAS , China"},{"name":"Yuanfang Guo","slug":"yuanfang-guo","given":"Yuanfang","family":"Guo","affiliation":"School of Computer Science and Engineering , Beihang University , China"}],"abstract":"Motivated by the capability of Generative Adversarial Network  on exploring the latent semantic space and capturing semantic variations in the data distribution, adversarial learning has been adopted in network embedding to improve the robustness. However, this important ability is lost in existing adversarially regularized network embedding methods, because  their embedding results are directly compared to the samples drawn from perturbation (Gaussian) distribution without any rectification from real data. To overcome this vital issue, a novel Joint Adversarial Network Embedding (JANE) framework is proposed to jointly distinguish the real and fake combinations of the embeddings, topology information and node features. JANE contains three pluggable components, Embedding module, Generator module and Discriminator module.\r\nThe overall objective function of JANE is defined in a min-max form, which can be  optimized via  alternating stochastic gradient. Extensive experiments demonstrate the remarkable superiority  of the proposed JANE on link prediction (3% gains in both AUC and AP) and node clustering (5% gain in F1 score).","doi":"10.24963/ijcai.2020/192","url":"https://ijcai.org/proceedings/2020/192","pdf":"https://ijcai.org/proceedings/2020/0192.pdf","markdown":"https://ijcai.org/proceedings/2020/192.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/192","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Data Mining","track":"Main track","number":192,"pages":{"first":1381,"last":1387},"keywords":["Data Mining: Mining Graphs, Semi Structured Data, Complex Data"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24671/file_24671.m3u8","length_sec":314,"provider":"hls"}],"references":[{"position":1,"raw":"[Bahdanau et al., 2015] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 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