DynamiSE: Dynamic Signed Network Embedding for Link Prediction

Haiting Sun, Peng Tian, Yun Xiong, Yao Zhang, Yali Xiang, Xing Jia, Haofen Wang

Abstract: In real-world scenarios, dynamic signed networks are ubiquitous where edges have positive and negative sign semantics and evolve over time. Encoding the dynamics and sign semantics of the network simultaneously is challenging. Moreover, over-smoothing is inevitably introduced by the learning of network dynamics. Targeting this gap, we propose Dynamic Signed Network Embedding (DynamiSE), which effectively integrates the balance theory and ordinary differential equation (ODE) into node representation learning to construct a deeper dynamic signed graph neural network and capture the complex sign semantics formed by the two types of edges.