A network representation learning method based on topology

Abstract In the network, nodes with similar neighborhood topology often have similar functions and play similar roles. Accurately identifying the role of nodes is the key to our understanding of the network and facilitates the completion of subsequent tasks. This paper proposes a network representation method based on the Neighborhood Topology Feature (NTF), which learns the latent representations of nodes based on their neighborhood topologies. NTF adopts the idea of energy dissipation and introduces the concept of energy level to measure the influence of neighboring nodes on the central node, and on this basis constructs the influence subgraph of the central node. NTF comprehensively uses the absolute and relative features of the neighboring nodes to describe the neighborhood topology of the central node hierarchically to achieve better learning effect. It can effectively reduce the interference of noise nodes by measuring the node pairs similarity on the influence subgraph. The experimental results show that NTF performs better than the existing methods on the public datasets, and it also achieves excellent results on the real data of the actual project.