Archaeologists have long recognized that spatial relationships are an important influence on and driver of all manner of social processes at scales from the local to the continental. Recent research in the realm of complex networks focused on community detection in human and animal networks suggests that there may be certain critical scales at which spatial interactions can be partitioned, allowing researchers to draw potential boundaries for interaction that provide insights into a variety of social phenomena. Thus far, this research has been focused on short time scales and has not explored the legacies of historic relationships on the evolution of network communities and boundaries over the long-term. In this study, we examine networks based on material cultural similarity drawing on a large settlement and material culture database from the U.S. Southwest/Mexican Northwest (ca. 1000–1450 CE) divided into a series of short temporal intervals. With these temporally sequenced networks we: 1) demonstrate the utility of network community detection for partitioning interactions in geographic space, 2) identify key transitions in the geographic scales of network communities, and 3) illustrate the role of previous network configurations in the evolution of network communities and their spatial boundaries through time.
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TEDIC: Neural Modeling of Behavioral Patterns in Dynamic Social Interaction Networks
Dynamic social interaction networks are an important abstraction to model time-stamped social interactions such as eye contact, speaking and listening between people. These networks typically contain informative while subtle patterns that reflect people’s social characters and relationship, and therefore attract the attentions of a lot of social scientists and computer scientists. 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 degenerate 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. TEDIC 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. Our project website is: http://snap.stanford.edu/tedic/.
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- NSF-PAR ID:
- 10300282
- Date Published:
- Journal Name:
- WWW '21: Proceedings of the Web Conference 2021
- Page Range / eLocation ID:
- 693 to 705
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3442381.3450096
