Modern sociology has profoundly uncovered many convincing social criteria for behavioural analysis. Unfortunately, many of them are too subjective to be measured and presented in online social networks. On the other hand, data mining techniques can better find data patterns but many of them leave behind unnatural understanding. In this paper, we propose a fundamental methodology to support the further fusion of data mining techniques and sociological behavioral criteria. Our highlights are three-fold: First, we propose an effective hypergraph awareness and a fast line graph construction framework. The hypergraph can more profoundly indicate the interactions between individuals and their environments because each edge in the hypergraph (a.k.a hyperedge) contains more than two nodes, which is perfect to describe social environments. A line graph treats each social environment as a super node with the underlying influence between different environments. In this way, we go beyond traditional pair-wise relations and explore richer patterns under various sociological criteria; Second, we propose a novel hypergraph-based neural network to learn social influence flowing from users to users, users to environments, environment to users, and environments to environments. The neural network can be learned via a task-free method, making our model very flexible to support various data mining tasks and sociological analysis; Third, we propose both qualitative and quantitive solutions to effectively evaluate the most common sociological criteria like social conformity, social equivalence, environmental evolving and social polarization. Our extensive experiments show that our framework can better support both data mining tasks for online user behaviours and sociological analysis.
翻译:现代社会学深刻地揭示了许多具有说服力的社会行为分析标准。 不幸的是,许多数据开采技术过于主观,无法在网上社交网络中进行衡量和展示。另一方面,数据开采技术可以更好地找到数据模式,但许多却留下非自然理解。在本文中,我们提出了一个支持数据开采技术和社会行为标准进一步融合的基本方法。我们的亮点有三重:第一,我们建议一个有效的高压意识和一个快速线图构建框架。高压可以更深刻地显示个人及其环境之间的相互作用,因为高压(a.k. a supergedge)中每个边缘都包含两个节点以上,可以完美地描述社会环境。直线图将每个社会环境视为一个超级节点,具有不同环境之间的内在影响。在本文中,我们提出了一个基本方法,我们超越传统的双向关系,根据不同的社会行为标准探索更丰富的模式;第二,我们提出了一个新的基于超直线线网络,以学习用户向用户、用户向环境、环境和环境流动的社会影响。 神经网络网络可以通过一个更好的无任务来学习,用来描述社会环境环境环境环境环境环境环境环境环境环境环境环境。我们可以通过一个完美的方法来学习,通过一种无差别方法,通过一种无差别的方法将每个环境环境环境环境环境环境环境环境环境环境环境环境环境环境环境环境环境环境分析作为超标度分析。 通过一个超标度分析,使我们的模型的模型进行模型分析,我们的社会环境的模型的模型和高度的模型来提出一个超常态的模型, 分析, 和最灵活的模型来提出一个模型来提出一个模型来显示社会-