Node Classification in Complex Social Graphs via Knowledge-Graph Embeddings and Convolutional Neural Network Bonaventure C. Molokwu 1(B) , Shaon Bhatta Shuvo 1 , Narayan C. Kar 2 , and Ziad Kobti 1 1 School of Computer Science, University of Windsor, 401 Sunset Avenue, Windsor, ON N9B-3P4, Canada {molokwub,shuvos,kobti}@uwindsor.ca 2 Centre for Hybrid Automotive Research and Green Energy (CHARGE), University of Windsor, 401 Sunset Avenue, Windsor, ON N9B-3P4, Canada nkar@uwindsor.ca Abstract. The interactions between humans and their environment, comprising living and non-living entities, can be studied via Social Net- work Analysis (SNA). Node classification, as well as community detec- tion tasks, are still open research problems in SNA. Hence, SNA has become an interesting and appealing domain in Artificial Intelligence (AI) research. Immanent facts about social network structures can be effectively harnessed for training AI models in a bid to solve node classifi- cation and community detection problems in SNA. Hence, crucial aspects such as the individual attributes of spatial social actors, and the under- lying patterns of relationship binding these social actors must be taken into consideration in the course of analyzing the social network. These factors determine the nature and dynamics of a given social network. In this paper, we have proposed a unique framework, Representation Learning via Knowledge-Graph Embeddings and ConvNet (RLVECN), for studying and extracting meaningful facts from social network struc- tures to aid in node classification as well as community detection tasks. Our proposition utilizes an edge sampling approach for exploiting fea- tures of the social graph, via learning the context of each actor with respect to neighboring actors/nodes, with the goal of generating vector- space embedding per actor. Successively, these relatively low-dimensional vector embeddings are fed as input features to a downstream classifier for classification tasks about the social graph/network. Herein RLVECN has been trained, tested, and evaluated on real-world social networks. Keywords: Node classification · Feature learning · Feature extraction · Dimensionality reduction · Semi-supervised learning This research was supported by International Business Machines (IBM) and Compute Canada (SHARCNET). c  Springer Nature Switzerland AG 2020 V. V. Krzhizhanovskaya et al. (Eds.): ICCS 2020, LNCS 12142, pp. 183–198, 2020. https://doi.org/10.1007/978-3-030-50433-5_15