{"type":"ScholarlyArticle","title":"Graph Convolutional Networks on User Mobility Heterogeneous Graphs for Social Relationship Inference","authors":[{"name":"Yongji Wu","slug":"yongji-wu","given":"Yongji","family":"Wu","affiliation":"School of Computer Science and Technology , University of Science and Technology of China"},{"name":"Defu Lian","slug":"defu-lian","given":"Defu","family":"Lian","affiliation":"School of Computer Science and Technology , University of Science and Technology of China"},{"name":"Shuowei Jin","slug":"shuowei-jin","given":"Shuowei","family":"Jin","affiliation":"School of Computer Science and Technology , University of Science and Technology of China"},{"name":"Enhong Chen","slug":"enhong-chen","given":"Enhong","family":"Chen","affiliation":"School of Computer Science and Technology , University of Science and Technology of China"}],"abstract":"Inferring social relations from user trajectory data is of great value in real-world applications such as friend recommendation and ride-sharing. Most existing methods predict relationship based on a pairwise approach using some hand-crafted features or rely on a simple skip-gram based model to learn embeddings on graphs. Using hand-crafted features often fails to capture the complex dynamics in human social relations, while the graph embedding based methods only use random walks to propagate information and cannot incorporate external semantic data provided. We propose a novel model that utilizes Graph Convolutional Networks (GCNs) to learn user embeddings on the User Mobility Heterogeneous Graph in an unsupervised manner. This model is capable of propagating relation layer-wisely as well as combining both the rich structural information in the heterogeneous graph and predictive node features provided. Our method can also be extended to a semi-supervised setting if a part of the social network is available. The evaluation on three real-world datasets demonstrates that our method outperforms the state-of-the-art approaches.","doi":"10.24963/ijcai.2019/541","url":"https://ijcai.org/proceedings/2019/541","pdf":"https://ijcai.org/proceedings/2019/0541.pdf","markdown":"https://ijcai.org/proceedings/2019/541.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/541","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":541,"pages":{"first":3898,"last":3904},"keywords":["Machine Learning Applications: Networks","Machine Learning: Deep Learning","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Backes et al., 2017] Michael Backes, Mathias Humbert, Jun Pang, and Yang Zhang. 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