Abstract:
Network embedding solves network analytics. Methods focus on single-layered homogeneous or heterogeneous networks. Multilayer networks—heterogeneous networks with multiple edge/relation types—can naturally represent many real-world complex systems.
Multilayer network embedding struggles to capture and use rich interaction information of multi-type relations. HMNE, a fast and scalable multilayer network embedding model, efficiently preserves and learns multi-type relations into a unified embedding space.
We develop a heuristic 3D interactive walk technique for multilayer networks to leverage rich interactions between layers and capture important information in the layered structure. Node classification and link prediction are evaluated using our proposed model HMNE.
The proposed model outperforms competitive baselines in time and memory on seven social and biological multilayer network datasets.
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