SimNet: Similarity-based network embeddings with mean commute time.

SimNet: Similarity-based network embeddings with mean commute time.

Blog Article

In this paper, we propose a new approach for learning node embeddings for weighted undirected networks.We perform a random walk on the network to extract the latent structural information and perform node embedding learning under a similarity-based framework.Unlike previous works, we apply a different criterion to capture the proximity information between nodes in read more a network, and use it for improved modeling of similarities between nodes.We show that the mean commute time (MCT) between two nodes, defined as the average time a random walker takes to reach a target node and return to the source, plays a crucial role in quantifying the actual degree of proximity between two nodes of the network.We then introduce a novel definition of a similarity matrix that is based on the pair-wise mean commute time captured, which enables us to adequately represent the connection of similar nodes.

We utilize ribavirin coupon pseudoinverse of the Laplacian matrix of the graph for calculating such a proximity measure, capturing rich structural information out of the graph for learning more adequate node representations of a network.The results of different experiments on three real-world networks demonstrate that our proposed method outperforms existing related efforts in classification, clustering, visualization as well as link prediction tasks.