dynnode2vec: Scalable Dynamic Network Embedding
This work addresses the need for scalable dynamic network embedding in real-world applications, though it is incremental as it builds upon the existing node2vec method.
The paper tackles the problem of embedding dynamic networks, which evolve over time, by proposing dynnode2vec, a method that extends node2vec to handle dynamic settings efficiently, achieving scalable performance on large datasets.
Network representation learning in low dimensional vector space has attracted considerable attention in both academic and industrial domains. Most real-world networks are dynamic with addition/deletion of nodes and edges. The existing graph embedding methods are designed for static networks and they cannot capture evolving patterns in a large dynamic network. In this paper, we propose a dynamic embedding method, dynnode2vec, based on the well-known graph embedding method node2vec. Node2vec is a random walk based embedding method for static networks. Applying static network embedding in dynamic settings has two crucial problems: 1) Generating random walks for every time step is time consuming 2) Embedding vector spaces in each timestamp are different. In order to tackle these challenges, dynnode2vec uses evolving random walks and initializes the current graph embedding with previous embedding vectors. We demonstrate the advantages of the proposed dynamic network embedding by conducting empirical evaluations on several large dynamic network datasets.