MultiNet: Scalable Multilayer Network Embeddings

Representation learning of networks via embeddings has garnered popularity and has witnessed significant progress recently. Such representations have been effectively used for classic network-based machine learning tasks like link prediction, community detection, and network alignment. However, most existing network embedding techniques largely focus on developing distributed representations for traditional flat networks and are unable to capture representations for multilayer networks. Large scale networks such as social networks and human brain tissue networks, for instance, can be effectively captured in multiple layers. In this work, we propose Multi-Net a fast and scalable embedding technique for multilayer networks. Our work adds a new wrinkle to the the recently introduced family of network embeddings like node2vec, LINE, DeepWalk, SIGNet, sub2vec, graph2vec, and OhmNet. We demonstrate the usability of Multi-Net by leveraging it to reconstruct the friends and followers network on Twitter using network layers mined from the body of tweets, like mentions network and the retweet network. This is the Work-in-progress paper and our preliminary contribution for multilayer network embeddings.