RUM: network Representation learning throUgh Multi-level structural information preservation
This work addresses the limitation of existing methods that neglect higher-level properties like global community structures in network representation learning, offering a more comprehensive approach.
The paper tackles the problem of network representation learning by proposing RUM, a framework that preserves multi-level structural information including local triads, neighborhood relationships, and global community affiliations, resulting in substantial performance advantages in real-life tasks.
We have witnessed the discovery of many techniques for network representation learning in recent years, ranging from encoding the context in random walks to embedding the lower order connections, to finding latent space representations with auto-encoders. However, existing techniques are looking mostly into the local structures in a network, while higher-level properties such as global community structures are often neglected. We propose a novel network representations learning model framework called RUM (network Representation learning throUgh Multi-level structural information preservation). In RUM, we incorporate three essential aspects of a node that capture a network's characteristics in multiple levels: a node's affiliated local triads, its neighborhood relationships, and its global community affiliations. Therefore the framework explicitly and comprehensively preserves the structural information of a network, extending the encoding process both to the local end of the structural information spectrum and to the global end. The framework is also flexible enough to take various community discovery algorithms as its preprocessor. Empirical results show that the representations learned by RUM have demonstrated substantial performance advantages in real-life tasks.