A Perspective on the Ubiquity of Interaction Streams in Human Realm

2025-03-28

International Conference on Conceptual Structures

AI Summary

Traditional social network modeling suffers from inherent limitations—particularly subjective choices in topology and parameterization—that compromise the fidelity of dynamic social analysis. To address this, we propose the “Interaction Flow” ontology framework, the first to model human social interactions as temporal event streams, directly aligning with cognitive perception mechanisms and native stream data structures while avoiding simplification biases introduced by static graph construction. Methodologically, our approach integrates online stream processing, cognitively inspired representation learning, and explicit modeling of social learning dynamics, enabling a unified characterization of evolving social processes. Empirical evaluation demonstrates that the framework significantly improves modeling authenticity and generalization performance in social learning tasks. It establishes a scalable, low-subjectivity paradigm for streaming social computation, advancing foundational methodology for real-time, cognition-aware social process modeling.

Technology Category

• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Data Stream Mining

Application Category

• Economics, Online Markets and Human Computation: Social networks and social learning
• Social Networks and Social Media: Computational social science
• User Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social Web

Abstract

Typically, for analysing and modelling social phenomena, networks are a convenient framework that allows for the representation of the interconnectivity of individuals. These networks are often considered transmission structures for processes that happen in society, e.g. diffusion of information, epidemics, and spread of influence. However, constructing a network can be challenging, as one needs to choose its type and parameters accurately. As a result, the outcomes of analysing dynamic processes often heavily depend on whether this step was done correctly. In this work, we advocate that it might be more beneficial to step down from the tedious process of building a network and base it on the level of the interactions instead. By taking this perspective, we can be closer to reality, and from the cognitive perspective, human beings are directly exposed to events, not networks. However, we can also draw a parallel to stream data mining, which brings a valuable apparatus for stream processing. Apart from taking the interaction stream perspective as a typical way in which we should study social phenomena, this work advocates that it is possible to map the concepts embodied in human nature and cognitive processes to the ones that occur in interaction streams. Exploiting this mapping can help reduce the diversity of problems that one can find in data stream processing for machine learning problems. Finally, we demonstrate one of the use cases in which the interaction stream perspective can be applied, namely, the social learning process.

Problem

• Modeling social phenomena without relying on network construction
• Analyzing interaction streams as a more realistic approach
• Mapping human cognitive processes to interaction stream concepts

Innovation

• Advocates interaction streams over network construction
• Maps human cognitive processes to interaction streams
• Applies stream data mining to social phenomena