Social network graphs possess apparent and latent knowledge about their respective actors and links which may be exploited, using effective and efficient techniques, for predicting events within the social graphs. Understanding the intrinsic relationship patterns among spatial social actors and their respective properties are crucial factors to be taken into consideration in event prediction within social networks. My research work proposes a unique approach for predicting events in social networks by learning the context of each actor/vertex using neighboring actors in a given social graph with the goal of generating vector-space embeddings for each vertex. Our methodology introduces a pre-convolution layer which is essentially a set of feature-extraction operations aimed at reducing the graph's dimensionality to aid knowledge extraction from its complex structure. Consequently, the low-dimensional node embeddings are introduced as input features to a one-dimensional ConvNet model for event prediction about the given social graph. Training and evaluation of this proposed approach have been done on datasets (compiled: November, 2017) extracted from real world social networks with respect to 3 European countries. Each dataset comprises an average of 280,000 links and 48,000 actors.