{"type":"ScholarlyArticle","title":"Social Network Analysis using RLVECN: Representation Learning via Knowledge-Graph Embeddings and Convolutional Neural-Network","authors":[{"name":"Bonaventure C. Molokwu","slug":"bonaventure-c-molokwu","given":"Bonaventure C.","family":"Molokwu","affiliation":"School of Computer Science , University of Windsor , Windsor - Ontario , Canada"},{"name":"Ziad Kobti","slug":"ziad-kobti","given":"Ziad","family":"Kobti","affiliation":"School of Computer Science , University of Windsor , Windsor - Ontario , Canada"}],"abstract":"Social Network Analysis (SNA) has become a very interesting research topic with regard to Artificial Intelligence (AI) because a wide range of activities, comprising animate and inanimate entities, can be examined by means of social graphs. Consequently, classification and prediction tasks in SNA remain open problems with respect to AI. Latent representations about social graphs can be effectively exploited for training AI models in a bid to detect clusters via classification of actors as well as predict ties with regard to a given social network. The inherent representations of a social graph are relevant to understanding the nature and dynamics of a given social network. Thus, our research work proposes a unique hybrid model: Representation Learning via Knowledge-Graph Embeddings and ConvNet (RLVECN). RLVECN is designed for studying and extracting meaningful representations from social graphs to aid in node classification, community detection, and link prediction problems. RLVECN utilizes an edge sampling approach for exploiting features of the social graph via learning the context of each actor with respect to its neighboring actors.","doi":"10.24963/ijcai.2020/739","url":"https://ijcai.org/proceedings/2020/739","pdf":"https://ijcai.org/proceedings/2020/0739.pdf","markdown":"https://ijcai.org/proceedings/2020/739.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/739","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":"Doctoral Consortium","track":"Doctoral Consortium","number":739,"pages":{"first":5198,"last":5199},"keywords":["Data Mining: Mining Graphs, Semi Structured Data, Complex Data","Data Mining: Classification, Semi-Supervised Learning","Machine Learning: Deep Learning","Data Mining: Feature Extraction, Selection and Dimensionality Reduction"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25468/file_25468.m3u8","length_sec":270,"provider":"hls"}],"references":[{"position":1,"raw":"[Goodfellow et al., 2017] Ian G. 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