Extracting patterns from dynamic social interaction networks containing time-stamped social interactions such as eye contact between people is critical for inferring people’s social characters and relationship. Previous approaches on extracting those patterns primarily rely on sophisticated expert knowledge of psychology and social science, and the obtained features are often overly task-specific. More generic models based on representation learning of dynamic networks may be applied but the unique properties of social interactions cause severe model mismatch and degenerates the quality of the obtained representations. Here we fill this gap by proposing a novel framework, termed Temporal Network-diffusion Convolutional networks (TEDIC), for generic representation learning on dynamic social interaction networks. We make TEDIC a good fit by designing two components: (1) Adopt diffusion of node attributes over a combination of the original network and its complement to capture long-hop interactive patterns embedded in the behaviors of people making or avoiding contact; (2) Leverage temporal convolution networks with hierarchical set- pooling operation to flexibly extract patterns from different-length interactions scattered over a long time span. The design also endows TEDIC with certain self-explaining power. We evaluate TEDIC over five real datasets for four different social character prediction tasks including deception detection, dominance identification, nervousness detection and community detection. It not only consistently outperforms previous SOTA’s, but also provides two important pieces of social insight. In addition, it exhibits favorable societal characteristics by remaining unbiased to people from different regions.

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