Event Prediction in Social Graphs Using 1-Dimensional Convolutional Neural Network

Abstract

Social network graphs and structures possess implicit knowledge embedded about their respective nodes and edges which may be exploited, using effective and efficient methods, for relative event prediction upon these network structures. Thus, understanding the intrinsic patterns of relationship among spatial social actors as well as their respective properties are very crucial factors to be taken into consideration with respect to event prediction in social network graphs. Generally, event prediction problems are considered to be NP-Complete. This research work proposes an original approach (Graph-ConvNet) for predicting events in social network structures using a one-dimensional convolutional neural network (1D-ConvNet) model. In this regard, two distinct methodologies have been proposed herein with each having its individual characteristics and advantages. The first methodology introduces a pre-convolution layer that involves reframing the input social network graph to a two-dimensional adjacency matrix. Thereafter, feature-extraction operations are applied to reduce the linear dimensionality (across the \( x-axis \)) of the input matrix before it is introduced to a repetitive series of non-linear convolution and pooling operations. The second methodology operates on a joint input comprising the edge list (E) of the social graph, and its associated feature space matrix. With regard to the observations and findings from experiments thus far: the first method is suitable for relatively smaller network graphs \( (nodes \le 30,000)\); while the second method is a good fit for much larger network graphs \( (nodes > 30,000)\). Training and evaluation of these proposed approaches have been done on datasets (compiled: November, 2017) extracted from real social network communities with respect to 3 European countries where each dataset comprises an average of 280,000 edges and 48,000 nodes.