TEDIC: Neural Modeling of Behavioral Patterns in Dynamic Social Interaction Networks
TEDIC: Neural Modeling of Behavioral Patterns in Dynamic Social Interaction Networks
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DOI：
10.1145/3442381.3450096
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发表时间：
2021-04
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影响因子：
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通讯作者：
Yanbang Wang;Pan Li;Chongyang Bai;J. Leskovec
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作者：
Yanbang Wang;Pan Li;Chongyang Bai;J. Leskovec
动态社会互动网络是一个重要的抽象，用于模拟人们之间的眼神交流、说话和倾听等带有时间戳的社会互动。这些网络通常包含信息丰富而又微妙的模式，反映了人们的社会特征和关系，因此吸引了许多社会科学家和计算机科学家的注意。以前提取这些模式的方法主要依赖于复杂的心理学和社会科学专家知识，而获得的特征往往过于特定于任务。基于动态网络表征学习的更通用的模型可能会得到应用，但社会互动的独特性质会导致严重的模型不匹配，并降低所获得表征的质量。在这里，我们通过提出一个新的框架来填补这一空白，称为时间网络-扩散卷积网络（TEDIC），用于动态社会互动网络的通用表示学习。我们通过设计两个组件使TEDIC具有良好的拟合性：1)在原始网络及其补充网络的组合上采用节点属性的扩散，以捕获嵌入在人们建立或避免接触行为中的长跳交互模式；2)利用时序卷积网络的分层集池操作，灵活地从分散在长时间跨度上的不同长度的交互中提取模式。这种设计也赋予了TEDIC一定的自我解释能力。我们在五个真实数据集上对TEDIC进行了评估，用于四种不同的社会特征预测任务，包括欺骗检测、优势识别、紧张检测和社区检测。TEDIC不仅一贯优于以前的SOTA，而且还提供了两个重要的社会洞察力。此外，它对来自不同地区的人保持公正，表现出良好的社会特征。我们的项目网站是：http://snap.stanford.edu/tedic/。
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/.