Exploring Similarity-Based Graph Compression for Efficient Network Analysis and Embedding

Network analysis is an emerging field with a wide spectrum of applications across many disciplines such as social networks, computer networks, and healthcare. However, the ever-increasing size of real-world networks is a major challenge for network analysis due to their high computational and space costs. In this paper, we utilize a node similarity-based graph compression method, SGC, and investigate the effect of various node similarity measures on graph compression. SGC compresses the input graph to a smaller graph without losing any/much information about its global structure and the local proximity of its vertices. We apply our compression method to the network embedding problem to study its effectiveness and efficiency. Our experimental results on four real-world networks show that each similarity measure has a different effect on graph compression and embedding, where some yield an improvement up to 70% network embedding time without decreasing classification accuracy as evaluated on single and multi-label classification tasks.